Citation
What drives parents?

Material Information

Title:
What drives parents? a case sensitive inquiry into parents' mode preferences for the journey to school
Creator:
Zuniga, Kelly Draper
Place of Publication:
Denver, Colo.
Publisher:
University of Colorado Denver
Publication Date:
Language:
English
Physical Description:
xvi, 371 leaves : ; 28 cm.

Thesis/Dissertation Information

Degree:
Doctorate ( Doctor of Philosophy)
Degree Grantor:
University of Colorado Denver
Degree Divisions:
College of Architecture and Planning, CU Denver
Degree Disciplines:
Design and Planning
Committee Chair:
van Vliet, Willem
Committee Members:
Sancar, Fahriye H.
Wridt, Pamela J.
Clarke, Susan E.
Kirshner, Ben

Subjects

Subjects / Keywords:
Walking -- Environmental aspects -- United States ( lcsh )
Fitness walking -- United States ( lcsh )
Exercise for children -- United States ( lcsh )
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis (Ph. D.)--University of Colorado Denver, 2010. Design and planning
Bibliography:
Includes bibliographical references (leaves 358-371).
General Note:
College of Architecture and Planning
Statement of Responsibility:
by Kelly Draper Zuniga.

Record Information

Source Institution:
|University of Colorado Denver
Holding Location:
|Auraria Library
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
655755354 ( OCLC )
ocn655755354

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Full Text
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What Drives Parents? A Case Sensitive Inquiry into Parents
Mode Preferences for the Journey to School
by
Kelly Draper Zuniga
B.S., The Pennsylvania State University, 1993
M.S., University of California, Davis, 2002
A thesis submitted to the
University of Colorado Denver
in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Design and Planning
2010


2010 by Kelly Draper Zuniga
All rights reserved.


This thesis for the Doctor of Philosophy
degree by
Kelly Draper Zuniga
has been approved
by
Willem van Vliet
Fahriye H. Sancar


Zuniga, Kelly Draper (Ph.D., Design and Planning)
What Drives Parents? A Case Sensitive Inquiry into Parents Mode Preferences for the
Journey to School
Thesis directed by Professor Willem van Vliet-
ABSTRACT
This dissertation uses a case-sensitive approach to examine an active travel interventions
diverse target population. It builds on a series of travel choice models, and draws key
conceptual themes from Chapins (1974) human activity model, which highlights
opportunity-related and propensity-related factors associated with behaviors. The research
addresses two key issues. First, planning research about active travel emphasizes
environmental influences on travel mode and focuses on obstacles to active travel, assuming
that parents would walk if they could. Second, previous research diminishes variance in travel
behavior as it identifies most important factors associated with travel mode.
I used case-sensitive mixed methods to investigate parents perceptions of school travel, and
to consider their propensity in addition to their opportunity to walk. I conducted my research
in two phases with 12 schools that participated in 2007-2008 SR2S non-infrastructure
programs. In phase one, I analyzed 6$ short, semi-structured interviews with parents from 12
Denver elementary schools to identify local school commuting issues. In phase two, I used
cluster analysis of a Q-sort activity completed by 650 parents from seven of the schools to
identify and characterize attitude-based subgroups of parents.
My qualitative study revealed disparity in parents perceptions of travel related issues, which
acknowledged variance in travel behavior, and also indicated variations in parents
opportunity and propensity to walk children to school. More importantly, that portion of the
research provided data to further examine attitudinal diversity about active travel in Denvers
public elementary schools.
My quantitative study found that opportunity and propensity were positively, albeit weakly
correlated. However, it also found that travel characteristics were not a function of
opportunity and propensity alone, as Chapins (1974) human activity model suggested.
Rather, parents that belonged to subgroups with positive attitudes about active travel
described shades of travel behavior between typically walk and typically drive. These
findings suggest that intervention tailored to address specific issues and motivations could
encourage parents to walk more often even if they already walk part of the time.
This abstract accurately represents the content of the candidates thesis. I recommend its
publication.
Signed
Willem van Vliet
u


ACKNOWLEDGMENT
I feel profoundly grateful to my advisor, Willem van Vliet-, for his wisdom,
patience, and kind leadership in my research. Although his specific guidance and
support considerably improved the quality of the dissertation, I appreciate two things
he did in particular: First, he let me decide for myself what was good for me and my
family, but consistently presented professional opportunities for me to accept or
decline; and Second, he did not tell me how to do a dissertation up front, but let me
grow into my academic self at a natural pace. I am a better person because of it.
I wish to thank all the members of my committee for their valuable participation and
insight into this project. I selected each member of this eclectic group for a unique
attribute that I appreciated independently Fahriye for her notoriously high standards
that scared me just a little and for reminding me that the doctorate is not just about
getting a job; Pamela for her friendship, creativity, and constructive insight; Ben for
reminding me to see the positive side of things I critique; and Susan for her case-
sensitive methodological expertise, but even more because she was easy to talk to. It
was a pleasant surprise to find that the group also worked well together.
I would not have been able to complete this task if not for the ongoing support of
colleagues in the department and the dissertation support group. I appreciate them for
reviewing articles, listening to rehearsals, holding me accountable in my endless
dissertation to do list and for sharing their research with me.
I am grateful to my family. By their open mindedness, my parents raised me to love
learning new things. By his example, my brother taught me to embrace adventure
and challenge. Through decades of faithful correspondence, my grandpa Jack
prepared me to love writing. Those lessons provided a firm foundation for this work.
And finally, I express my love and appreciation to my sweet husband Miguel and to
my beautiful daughter Haley. Thanks, Miguel, for taking late night baby duty so that
I could think clearly during the day and for your generous support over the years.
Thanks to Haley, who came into my life mid-way through the dissertation. I learn
more from watching her figure things out than I have in my decades of schooling.
These two will make the ends of the earth feel like home.


TABLE OF CONTENTS
Figures....................................................................xii
Tables.....................................................................xiv
Images.....................................................................xvi
CHAPTER
1: A Case-Sensitive Approach to the Question What Drives Parents?..........1
Introduction..............................................................1
Advocates for Active School Travel........................................3
Childrens Health.......................................................3
Pedestrian Safety.......................................................4
Environmental Sustainability............................................5
Universal Access A Shared Political Agenda............................8
The Policy Problem: Encouraging Active Travel............................10
Two Gaps Between Planning Research and Praxis............................12
A Case Sensitive Approach................................................14
Preview of Remaining Chapters............................................15
2: A Theoretical Framework for Learning Why Parents Really Drive Their Children
to School...................................................................18
Introduction.............................................................18
Limitation of Correlative Studies........................................20
Choice Models............................................................21
vi


Rational Choice Models
23
Alternative Behavioral Theories...........................................25
Environmental Determinism.................................................31
Summary......................................................................33
3: Why Do Parents Drive Children to School? An Interpretive Review of
Literature.....................................................................35
Introduction.................................................................35
Why do parents drive children to school?....................................37
Qualitative Studies.......................................................39
Quantitative Studies......................................................40
Distilling Findings to Emphasize Environmental Barriers...................43
Specific Variables..........................................................46
Opportunity-Related Factors...............................................47
Propensity-Related Factors................................................60
Environmental Determinism.................................................71
Summary......................................................................72
4: Qualitative Methods.........................................................75
Introduction.................................................................75
Research Design.............................................................77
Research Setting............................................................79
Research Sites and SR2S Program Affiliation...............................80
vii


Program 1: Denver Public Schools and Denver Osteopathic Foundation.......83
Program 2: Denver Environmental Health and Transportation Solutions......93
Program 3: Denver Health and DPS Rides..................................106
Program 4 (Alternate): Children Youth and Environments
Center for Research and Design (CYE)....................................Ill
Data Collection...........................................................114
Gaining Entry Participation in Programs...............................114
Recruitment Procedures..................................................115
Analysis and Interpretation...............................................122
Summary...................................................................125
5: Qualitative Findings......................................................129
Introduction..............................................................129
Major Themes..............................................................130
Theme 1: Life Pace......................................................131
Theme 2: Nurture........................................................141
Theme 3: Context........................................................153
Mapping Attitudes Using Opportunity-Propensity Measures...................164
Summary...................................................................167
Chapter 6: Quantitative Methods..............................................169
Introduction..............................................................169
Second Phase Research Design..............................................171
vm


Research Settings..........................................................173
Pilot Sorting Exercise Locations.........................................173
Main Sorting Exercise Locations..........................................175
Data Collection............................................................176
Research Packets.........................................................176
Revised Research Packets- for Main Sorting Exercise......................179
Recruitment Procedures...................................................193
Data Analysis and Interpretation...........................................208
Similarity Between Cases the Agglomeration Schedule....................209
Core Perspectives........................................................210
Characterizing Core Perspectives Ideal Types...........................215
Identifying Representative Cases Best Specimens........................218
Summary....................................................................221
7: Quantitative Findings......................................................224
Introduction...............................................................224
Characteristics of Cluster Analysis Cases..................................226
Range of Perspectives......................................................227
Opportunity and Propensity (Top Right Quadrant)..........................230
Propensity Only (Top Left Quadrant)......................................235
Opportunity Only (Bottom Right Quadrant).................................239
Neither Opportunity Nor Propensity (Bottom Left Quadrant)................240
ix


Similarities Between Core Perspectives.....................................242
Low Correlation..........................................................244
Moderate Correlation.....................................................245
High Correlation.........................................................247
Overall Similarities.......................................................248
Preference for Physical Activity.........................................248
Motivations for Travel Mode Choices......................................249
Social Conditions and Companionship......................................251
Contextual Conditions....................................................252
General Disagreements Time.............................................253
Summary....................................................................254
8: Answering the Question What Drives Parents?..............................257
Introduction...............................................................257
Main Contributions.........................................................257
Implications for Practice................................................259
Implications for Research................................................263
Strengthening the Relationship Between Research and Practice.............268
Directions for Further Research............................................271
x


APPENDIX
A. INTERVIEW SUBJECTS BY SCHOOL.....................275
B. LIST OF MEETINGS.................................278
C. INTERVIEW SCHEDULE...............................280
D. MAIN STUDY AGGLOMERATION SCHEDULES...............282
E. MAIN STUDY DENDROGRAMS...........................289
F. PILOT STUDY IDEAL TYPES..........................296
G. MAIN STUDY IDEAL TYPES...........................302
H. PILOT STUDY BEST SPECIMENS.......................348
I. MAIN STUDY BEST SPECIMENS........................350
BIBLIOGRAPHY........................................358
xi


LIST OF FIGURES
Figure
2-1: CONCEPTUAL FRAMEWORK OF AN ELEMENTARY-AGED
CHILDS TRAVEL BEHAVIOR................................22
2-2: TWO UTILITY-BASED CONCEPTUAL FRAMEWORKS...........25
2-3: CONCEPTUAL MODEL OF INDIVIDUAL SPATIAL CHOICE.....27
2-4: GENERAL MODEL FOR EXPLAINING ACTIVITY PATTERNS....30
4- 1: DATA COLLECTION TIMELINE........................ 79
5- 1: ATTITUDE TYPES BY PROPENSITY AND OPPORTUNITY....166
6- 1: PILOT SORTING EXERCISE PACKET ENGLISH (NOT TO SCALE) ...179
6-2: MAIN SORTING EXERCISE PACKET ENGLISH (NOT TO SCALE)... 181
6-3: POSTER ENGLISH (NOT TO SCALE)...................183
6-4: SAMPLE Q-SORT DISTRIBUTION DIAGRAM (NOT TO SCALE).185
6-5: PILOT Q SET......................................188
6-6: MAIN Q SET.......................................190
6-7: REVISED QUESTIONNAIRE........................... 192
6-8: PILOT STUDY AGGLOMERATION SCHEDULE.............. 210
6-9: PILOT STUDY DENDROGRAM...........................214
6- 10: IDEAL TYPE FOR PILOT CLUSTER A..................217
7- 1: ATTITUDE TYPOLOGY FOR SCHOOL TRAVEL..............229
7-2: CLUSTER ED-A IDEAL TYPES..........................233
xii


7-3: CLUSTER BR-A IDEAL TYPES
234
7-4: CLUSTER SA-B IDEAL TYPES.......................235
7-5: CLUSTER SA-A IDEAL TYPES.......................237
7-6: CLUSTER BR-D IDEAL TYPES.......................238
7-7: CLUSTER CO-B IDEAL TYPES.......................240
7-8: CLUSTER PH-F IDEAL TYPES.......................242
7- 9: MAIN STUDY CLUSTER CORRELATIONS USING PEARSONS
COEFFICIENT.........................................243
8- 1: REVISED CONCEPTUAL MODEL OF MODE CHOICE FOR
ELEMENTARY SCHOOL
TRAVEL..............................................264
xiii


LIST OF TABLES
Table
3- 1: Range of Factors Associated with Travel Mode.........................38
4- 1: 2007 Denver SR2S programs and participating schools.................. 82
4-2: Demographic profile of Edison neighborhoods versus City of Denver ...84
4-3: Demographic profile of Force neighborhoods versus City of Denver.....87
4-4: Demographic profile of Sabin neighborhoods versus City of Denver.....89
4-5: Demographic profile of Slavens neighborhoods versus City of Denver...91
4-6: Demographic profile of Corys neighborhoods versus City of Denver ...95
4-7: Demographic profile of Bromwells neighborhoods versus City of Denver ... 97
4-8: Demographic profile of Stecks neighborhoods versus City of Denver..100
4-9: Demographic profile of Philips neighborhoods versus City of Denver.102
4-10: Demographic profile of Hallett and Smiths neighborhoods versus
City of Denver...........................................................104
4-11: Demographic profile of Lowrys neighborhoods versus City of Denver...107
4-12: Demographic profile of Halletts neighborhoods versus City of Denver.110
4-13: Demographic profile of Munroes neighborhoods versus City of Denver ....112
4-14: Proportions of interview subjects at each school by gender and race..117
4-15: Hierarchy of coding themes.........................................126
6-1: 2008 school enrollment by race/ethnicity and free/reduced lunch program ... 174
6-2: Pilot study response rates..........................................194
xiv


6-3: Main study response rates.....................................................195
6-4: Response rates by race/ethnicity..............................................198
6-5: Pilot Q sample demographic characteristics....................................201
6-6: Main study Q sample filters...................................................202
6-7: Main study Q sample characteristics...........................................206
6-8: Core perspectives by site and combination distance............................214
6- 9: Pilot study best specimens.................................................. 220
7- 1: Travel characteristics of clusters in top right quadrant.....................231
7-2: Travel characteristics of clusters in top left quadrant......................236
7-3: Travel characteristics of single cluster in bottom right quadrant............239
7-4: Travel characteristics of clusters in bottom left quadrant....................241
xv


LIST OF IMAGES
Image
4-1: Edison elementary school in neighborhood context.................86
4-2: Force elementary school in neighborhood context..................88
4-3: Sabin elementary school in neighborhood context..................90
4-4: Slavens elementary school in neighborhood context................93
4-5: Cory elementary school in neighborhood context...................96
4-6: Bromwell elementary school in neighborhood context...............99
4-7: Steck elementary school in neighborhood context..................101
4-8: Philips elementary school in neighborhood context................103
4-9: Hallett and Smith elementary schools in neighborhood context.....106
4-10: Lowry elementary school in neighborhood context.................109
4-11: Valdez elementary school in neighborhood context................Ill
4-12: Munroe elementary school in neighborhood context................ 114
xvi


CHAPTER 1:
A CASE-SENSITIVE APPROACH TO THE QUESTION
WHAT DRIVES PARENTS?
Introduction
Over the course of the last century, and particularly after World War II,
western societies have become increasingly automobile-centered (Newman &
Kenworthy, 1999). More recently, the trend towards private automobile travel has
extended to include childrens trips to and from school. For example, national
travel surveys in the United States and Britain revealed a marked decrease in the
proportion of students age 5-11 walking or biking to and from elementary school
over the past four decades (McDonald, 2007; Pooley, 2004; Centers for Disease
Control and Prevention [CDCP], 2005; U.S. Department of Transportation
[USDOT], 1969-2001). Data indicate that in the United States the proportion
decreased from approximately 49% in 1969 to approximately 15% in 2001
(McDonald, 2007; CDCP, 2005; USDOT, 1969-2001).
I begin this chapter by describing four reasons that policy-makers should
be concerned about mode choice for school travel: to protect childrens health, to
increase pedestrian safety, to achieve environmental sustainability and to ensure
1


universal access (see section 1.2). Each of those reasons supports the health, safety,
and welfare mission espoused by the planning professions codes (American Institute
of Certified Planners [AICP], 2005). The legitimacy of planning intervening in school
travel behavior is worthy of careful examination.
However, in my dissertation, I accept the premise that intervention is justified,
and focus my attention on the policy objective: to increase the proportion of active
school trips. Later in this chapter, I introduce two federally funded programs that
encourage active school travel Healthy People 2010 and Safe Routes to School -
and note two reasons that traditional planning research regarding school travel is ill
equipped to direct this type of intervention, reinforcing the gap between planning
research and praxis.
The purpose of my dissertation is to bridge a gap between traditional planning
research regarding travel mode choices and intervention that aims to influence travel
behavior. Rather than identifying and generalizing factors that uniformly explain the
decrease in active travel, my research explains how parents experiences of those
factors vary. I examine parents attitudes about school travel using a combination of
qualitative and quantitative methods in a case-sensitive research design. In contrast
with traditional research methods, case-sensitive research magnifies differences in
research subjects orientations towards an issue in order to improve communication
of an idea or policy objective. In this case, it allows planners to direct active travel
intervention to more effectively influence parents mode choices for school trips.
2


Advocates for Active School Travel
An informal coalition of active travel advocates focuses active travel policy
discourse on childrens health, public safety, environmental sustainability, and
universal accessibility. Popular media presents the same issues to the public and
makes them a part of the views that influence parents travel decisions. Although
policy-makers may agree with the motivations for an active travel campaign, they are
likely to continue to approve funding only for programs that promise and produce a
substantial increase in rates of active travel.
Childrens Health
Advocacy for childrens health has been at the forefront of the policy agenda
for active travel to school. Rates of overweight and obese children have increased by
nearly 300% in the past decade (McDonald, 2007; Ogden, Carool, & Curtin, 2006),
and health problems that have traditionally occurred in adults, including cardio-
pulmonary disease, diabetes, asthma, depression and anxiety are becoming more
common in children as well (Frank, 2001; Kegerreis, 1993; CDCP, 2005; U.S.
Department of Health and Human Services [USDHHS], 1996). Since cars and busses
gather along streets to wait for children during pick up time, the concentration of
fumes from idling engines also presents an health risk in the immediate vicinity of the
schools (Environmental Protection Agency [EPA], 2002). In addition to its impact on
the quality of life for children and their families, childhood disease is an additional
3


burden on government healthcare programs that are already experiencing financial
pressures.
The recent decrease in active travel among children is symptomatic of a
general decline in physical activity in the United States (USDHHS, 1996, 2000).
Some researchers claim that active commuting is a critical, but overlooked source of
childrens daily physical activity (Tudor-Locke, Ainsworth, & Popkin, 2001).
According to various studies, omission of the active trip to school significantly
decreases childrens achievement of health-related activity guidelines, limits
academic achievement, contributes to anxiety and depression, and establishes lifelong
patterns of inactivity (Dwyer, Sallis, Blizzard, Lazarus, & Dean, 2001; James, 1995;
Kegerreis, 1993; Shephard, 1997; Tudor-Locke, Ainsworth, & Popkin, 2001).
Pedestrian Safety
The decrease in active travel is also responsible for a higher proportion of
traffic injuries for pedestrians and bicyclists (Killingsworth & Lamming, 2001;
McMillan, 2005). Studies find that as fewer children walk and bike to school,
neighborhood streets become more dangerous for the remaining pedestrians who are
more difficult for drivers to see (Jacobsen, 2003). In addition, transportation funding
is typically apportioned based on counts of vehicles versus pedestrians passing a
designated point in the road. As the ratio of vehicular to pedestrian activity increases,
new funding reinforces the dominance of automobile traffic (Gotthelf, 2007). As a
4


result, active travel advocates rely heavily on public perceptions and grassroots
activism to counter an auto-centric establishment.
Active travel advocates emphasize childrens health in order to encourage
action at the policy level and to influence views that shape decisions about the
commute to school. Although much of the rhetoric is based on empirical research,
discussion of health issues bear influence even when they lack empirical support. For
example, a disputed medical report from 2002 suggested that if the obesity epidemic
were not controlled, life expectancy for the upcoming generation would be lower than
the one preceding it (Center for Consumer Freedom [CCF], 2005; Cable News
Network [CNN], 2007). The Center for Consumer Freedom describes the rapid
spread of the exaggerated statement as it has entered the lexicon of obesity
scaremongers, making its way into countless articles, editorials, and even
Congressional testimony all without so much as a shred of credible research to back
it up (CCF, 2005). Other sources suggest that life expectancy in the United States is
still climbing, but that it lags behind at least 30 other countries (CNN, 2007). As
indicators of overall national wellbeing, health-related issues carry significant
political power.
Environmental Sustainability
A second key concern, environmental sustainability, similarly motivates the
policy agenda for active travel on the commute to school. Automobile ownership has
5


increased exponentially in the past half century and has been a subject of great
concern due to its damaging impact to the environment (Black, Collins, & Snell,
2001). Researchers estimate that vehicle ownership has increased by an average of
approximately 3.69 million passenger vehicles per year since 1960 (Federal Highway
Administration [FHA], 2006). Grassroots activists and transportation planners share
concern for the impact of private automobile travel on the environment.
Increased use of private automobiles is associated with faster depletion of
petroleum and other non-renewable resources and increase of air, water and noise
pollution. Research is abundant regarding ways to address these problems and
includes efforts to lessen environmental impacts by improving transportation
technology, as well as efforts to lessen environmental impacts by reducing rates of
private automobile travel.
The trip to elementary school poses particular concern for environmentalists
because per distance traveled, very short trips have a disproportionately high negative
environmental impact (Black, Collins, & Snell, 2001; Goodwin, 1995). Goodwin
notes that very short trips, especially those within residential neighborhoods, do not
provide sufficient time for cars to warm up effectively and include more frequent
stops, which result in decreased fuel efficiency (Goodwin, 1995). Most importantly,
short car trips are far more likely to be replaced with active modes of travel than are
long trips, so it is reasonable to focus policy efforts in this area.
6


As private automobile transportation becomes more dominant, the layouts of
cities and towns evolve to accommodate it. In the book, Crabgrass Frontier,
Kenneth Jackson chronicles the rise of suburbanization and the corresponding
deterioration of the pedestrian domain in the United States. He points out that
development patterns are a function of the interrelationship of technology, cultural
norms, population pressure, land values, and social relationships (Jackson, 1985,
p.3). He suggests that over the past century, those systems have combined to make
private automobile-use increasingly attractive, convenient, accessible, and perhaps
inevitable. His description also portrays development patterns as somewhat
reactionary as opposed to being the catalyst of change. That attitude differentiates his
argument from advocates of new urbanism who generally expect certain changes in
urban design to affect an increase of pedestrian activity (Newman & Kenworthy,
1999).
Automobile-centered development also includes more impervious paved
surfaces and less vegetated open-space, which impacts both environmental and social
systems. At the city level, accommodation of automobiles contributes to the urban
heat island effect and increased storm runoff (Oke, 1982). Automobile-centered
development can also be problematic for the economic sustainability of the city and
region. Since the heyday of urban renewal, planners have increasingly argued that
automobile centered urban design diminishes the vitality of the streetscape (Duany,
2000; Jacobs, 1961; Kunstler, 1993). Neighborhoods and commercial districts that
7


lack a pedestrian presence are prone to criminal activity and economic decline.
Environmental and social systems are intimately and inextricably connected, and both
potentially threatened by the dominance of private automobile use.
Universal Access-A Shared Political Agenda
Quality of life issues for children, including independent mobility and
universal accessibility, may not carry the same political weight as childrens health or
environmental sustainability in the policy discussion of active travel. In part, the
lower priority of these issues reflects the ambivalence of the adult public, including
policy makers, regarding childrens human rights (Bessant, 2003, 2004). Since the
mid-20th century in the United States, childhood has generally been viewed as a
period of preparation, during which the child is under the supervision and protection
of parents and other guardians, and is generally excluded from the public realm where
productive and commercial activities take place (Cahill, 1990; Mintz, 2004; Simpson,
1997). However, in many ways, childrens quality of life parallels the quality of life
experienced by adults, particularly caregivers and seniors, and will become more
prominent as seniors become the demographic majority and gain political strength in
the coming decades.
Seniors and People with Disabilities
Researchers identify independent mobility and access to transport as one of
three key indicators of quality of life for seniors and for people with physical
8


disabilities (Banister & Bowling, 2004; Meyers, Anderson, Miller, Shipp, & Hoenig,
2002; Stumbo, Martin, & Hedrick, 2009). This parallels discussions of independent
mobility regarding children and youth, which highlight access to and quality of
childrens physical environments (L Chawla, 1998; Louise Chawla, 2001; Holloway
& Valentine, 2000; Karsten & vanVliet, 2006; Wridt, 2004). In theory, pedestrian
orientation in urban design accommodates children, youth, seniors and people with
disabilities even if a child-friendly, senior-friendly, or handicap-accessible focus is
not the intention. Automobile travel has often been described as a key to
independence (Hillman, Adams, & Whitelegg, 1990). However, since operation of
motorized vehicles is restricted, pedestrian-orientation or multi-modal planning
enables access to resources regardless of age or ability. For example, Home Zones
in the U.K. as well as Woonerven in the Netherlands use physical design elements
to significantly limit traffic speeds in residential neighborhoods, making the streets
safe and accessible for pedestrians at any life stage (Gill, 1997, 2006).
Caregivers
Just as the layouts of cities and towns change to favor automobile travel over
pedestrian travel, the lifestyles of drivers change to reinforce the dependence of non-
drivers. In this sense, the independent mobility of children and seniors is linked to the
quality of life experienced by caregivers. Over the past several decades, rates of
parental escorting have increased for childrens trips to school (McDonald, 2008d).
9


Solomon argues that the increase in escorting reflects changes in popular theories of
parenting, which emphasize attention over independence (Solomon, 1993). However,
the more attentive style of parenting comes at a cost to the primary caregiver, since
multiple trips per child per day can mean hours spent driving. As an example of that
cost, Gershuny (1993) argues that the increase in escorting children is linked to
womens second-rate position in the job market. Although employers are not legally
permitted to discriminate on a basis of gender or parenthood, they are within their
rights to deny advancement to employees whose personal lives conflict with work
schedules.
Given the problems with childrens health, pedestrian safety, environmental
sustainability and universal access associated with increasing automobile use, it is
appropriate that planners seek opportunities to advance pedestrian-orientation and
multi-modal design. However, given the reactionary characteristic of development, as
Jackson (1985) described, an increase in pedestrian activity may be prerequisite to
achieving a more pedestrian friendly, designed physical environment. Therefore, this
project focuses on efforts to increase rates of active travel within the existing physical
environment.
The Policy Problem: Encouraging Active Travel
Policy-makers largely view the decrease in active travel to school as
problematic and have directed substantial public resources to reverse it. For example,
10


in 2000, the U.S. Department of Health and Human Services set an objective to
increase rates of active travel for trips between home and school for children ages 5-
15 to 50% by the year 2010 (USDHHS, 2000). To that end, the USDHHS has
provided funding for public-private partnerships to implement active travel
interventions within schools.
Another example of federal legislation aiming to increase active travel to
school is Safe Routes to School (SR2S) a Federal-Aid program of the United States
Department of Transportation's Federal Highway Administration, created by Section
1404 of the Safe, Accountable, Flexible, Efficient Transportation Equity Act: A
Legacy for Users (SAFETEA-LU). That legislation allocated $612 million in Federal
funds over five fiscal years, 2005-2009 to be administered by State Departments of
Transportation. The purposes of the program are:
To enable and encourage children, including those with
disabilities, to walk and bicycle to school;
To make bicycling and walking to school a safer and more
appealing transportation alternative, thereby encouraging a
healthy and active lifestyle from an early age; and
To facilitate the planning, development, and implementation of
projects and activities that will improve safety and reduce traffic,
fuel consumption, and air pollution in the vicinity (~2 miles) of
primary and middle schools (Grades K-8).
To increase rates of safe active travel, the Safe Routes to School National
Partnership recommends a combination of interventions that include education,
encouragement, engineering, enforcement and evaluation (Safe Routes to School
11


National Partnership [SR2S], 2007) That catch-all approach to intervention
intuitively promises to increase active travel by addressing a wide variety of reasons
that parents currently choose to drive their children to school. However, research
about travel behavior for school trips cannot guarantee that the programs will achieve
an increase in pedestrian travel, in part because they cannot adequately explain why
parents drive their children to school.
Two Gaps Between Planning Research and Praxis
Traditional planning research is ill equipped to guide the design of active
travel intervention for two reasons. First, several authors argue that correlative
research findings do not indicate solutions (Crane, 2000; Handy, 1996; Richards &
Ben-Akiva, 1975). Although planning research identifies many factors associated
with driving to school, including safety issues, it has not and cannot establish direct,
causal relationships. Researchers often describe factors associated with driving as
obstacles to active travel and policy-makers treat them accordingly. For example,
Safe Routes to School assumes that safety issues prevent parents from having the
opportunity to walk their children to school, but does not consider whether parents
would be inclined to walk if they could. Further research is needed to determine why
parents choose to drive rather than walk.
Second, active travel programs are not likely to affect their entire target
populations evenly, but few studies differentiate meaningful subgroups for tailored
12


behavioral intervention. Traditional planning research often controls for personal
characteristics rather than examining them directly in order to measure the
significance of environmental features. For example, McDonald (2008a) isolates low-
income and minority students to identify the environmental factors that most
influence travel behavior for that segment of the population. In order to evaluate the
impact of land use density, diversity and design, Cervero and Kockelman (1997)
control for a variety of socio-demographic and household characteristics of the trip-
maker. The controls are necessary because researchers expect personal characteristics
to influence travel behavior. Illustrating that expectation, Boarnet and Sarmiento
(1998) model travel demand (N) as a simple function of time cost (p), individual
income (y), and a vector of socio-demographic variables (S) in the equation
N=/(p,y,S).
However, some research does examine personal characteristics associated
with mode choice (Bemetti, Longo, Tomasella, & Violin, 2008). These generally
focus on standard socio-demographic indicators such as race and ethnicity, education
and/or income level, but ultimately produce new correlations that cannot explain why
different behaviors occur. For example, Bemetti et al. (2008) find that in Trieste,
various socio-demographic groups, such as elderly individuals and women are
associated with public transit use and recommend that transportation planners take
those differences into account. A few studies examine lifestyles and attitudes that
13


underlie travel choices, but they tend to generalize findings rather than characterizing
subgroups (Black, Collins, & Snell, 2001; Hillman, 1995; Joshi & MacLean, 1995).
A Case Sensitive Approach
My dissertation applies an alternative, case-sensitive research approach that
bridges the two aforementioned gaps between planning research and praxis. First, in
contrast with traditional research methods that focus on factors external to the
research subjects, case-sensitive research focuses attention on the research subjects
themselves to examine the attitudes that underlie their travel behaviors. That approach
assumes that by addressing issues that resonate strongly with the parents, intervention
can influence them to increase the proportion of school trips they make out-of-car.
Second, case-sensitive research magnifies differences in research subjects
attitudes towards an issue in order to improve communication of an idea or policy
objective. It does not differentiate subgroups of the population based on standard
socio-demographic indicators such as race or ethnicity, gender, age or educational
attainment. Although those factors are useful as predictors of behavior, they do not
indicate solutions to the policy problem. The case-sensitive approach assumes that
subgroups of parents share value-orientations about the school commute, and that
planners can tailor intervention to maximize its influence on those subgroups. It also
assumes that some groups of parents will be more amenable to behavioral change
than others, and that it is reasonable to focus intervention within those margins.
14


Preview of Remaining Chapters
In Chapter Two, I examine a theoretical model that explains parents travel
mode choices for elementary school trips (McMillan, 2005). Although the model
captures key elements of the planning fields prominent activity-choice models, I
argue that it emphasizes the opportunity to walk children to school, neglecting
parents propensity to do so if given the opportunity. The model magnifies that
shortcoming by failing to explain the significance of personal characteristics and their
relationship to other factors.
For active travel programs like SR2S to increase the numbers of active school
trips, facilitators need to know why parents choose to walk or drive. A growing body
of active travel research identifies factors associated with the decline in active travel
and with parents choice to drive children to school. In Chapter Three, I review
research in this area, noting the accomplishments and limitations of traditional
research approaches.
In Chapter Four, I describe my research design and the qualitative methods
that I used to examine Denver parents experiences of the school commute. I
conducted short, semi-structured interviews with 64 parents from 12 elementary
schools that participated in CDOT SR2S non-infrastructure programs and used
content analysis to identify themes in the transcripts. Then I compared prominent
discursive themes from that study with those from extant literature.
15


I present my findings from the qualitative study in Chapter Five. The three
main themes that resulted from my analysis life pace, nurture, and context are
consistent with findings from extant qualitative research. In contrast, the variety of
statements that composed each thematic category suggests that parents experiences
of the school commute differ even when influenced by similar external conditions.
These findings indicate that personal characteristics, including attitudes and
perceptions, are closely linked to travel mode choices. They also point to the
significance of propensity as a determinant of mode choice. I used data from the
qualitative study to develop the tool that I used for a sorting exercise detailed below.
In Chapter Six, I describe the quantitative methods that I used to examine
parents attitude types about school travel, and to investigate the relationship between
opportunity-related and propensity-related factors. 650 parents from seven elementary
schools completed a sorting exercise and questionnaire for this quantitative research
phase. I used cluster analysis to identify and compare attitude-based clusters of
parents at each school. For each cluster, I then measured levels of opportunity and
propensity to use active travel and evaluated those measures as an attitude-based
typology.
I present my findings from the quantitative study in Chapter Seven. My
analysis resulted in 31 discrete clusters of parents at the seven schools. I graph the
clusters based on opportunity and propensity measures, and find that even within
quadrants of that graph attitude types substantively vary. This finding suggests that
16


attitude-based evaluation would benefit active travel intervention, even at schools
whose populations appear to be culturally homogeneous.
In Chapter Eight, I consider the implications of my research findings in terms
of the methodological approach for studying and influencing travel behavior, the
professional ideology and practice of planning, and a revised conceptual model for
predicting travel mode choices. I conclude by offering recommendations for further
research.
17


CHAPTER 2:
A THEORETICAL FRAMEWORK FOR LEARNING
WHY PARENTS REALLY DRIVE
THEIR CHILDREN TO SCHOOL
Introduction
Children walk to school less frequently than they once did (McDonald, 2007;
CDCP, 2005; USDOT, 1969-2001). As Chapter One describes, researchers blame
driving for environmental degradation, childrens health problems, and limited
independent mobility for children, seniors, people with disabilities and caregivers.
Policy-makers address those issues by allocating substantial public resources to
programs like Safe Routes to School that encourage children to walk (Hubsmith,
2006). Supporting the policy objective to increase proportions of active trips to
school, planning researchers study why parents drive their children to school
(McMillan, 2005). Despite progress, some scholars claim that extant work cannot
sufficiently guide intervention and that researchers should situate it within a
theoretical framework that explains relationships in causal terms (Crane, 2000;
Handy, 1996).
18


In this chapter, I examine McMillans (2005) model of childrens school
travel and contrast it with other choice models in planning. Expanding on rational
choice theory, alternative conceptual frameworks include environmental, socio-
demographic and psychological characteristics associated with activity choices.
McMillans (2005) model covers those key dimensions of travel choice, roughly
describing the environmental characteristics as mediating factors and the social and
personal characteristics as moderating factors.
However, McMillans (2005) model only vaguely references interactions
between the different types of factors. For example, it is not clear why and to what
extent socio-demographic and psychological factors modify the influence of
environmental factors on parents travel mode choices. Because the model focuses on
the influence of urban form as an intervention, it emphasizes environmental factors
over socio-demographic and psychological factors, suggesting that the influences of
the latter two are secondary.
The emphasis of McMillans (2005) model on environmental factors reflects a
contemporary bias of planning research on the subject. Whereas early works in urban
sociology emphasized social and personal systems and only superficially
acknowledged the role of the physical setting (Michelson, 1976), contemporary
planning research reflects architectural determinism in its assumption that
modifications to the built environment can cause and/or direct behavioral change.
19


My dissertation research builds on McMillans (2005) and Chapins (1974)
models of travel choice by examining parents attitudes about the various facets of
travel choice (environmental, socio-demographic and psychological factors) and how
they relate to the opportunity and propensity to walk to elementary school.
Limitation of Correlative Studies
Two oft-cited reviews of planning literature about travel choice conclude that
extant research cannot sufficiently guide intervention to change behavior (Crane,
2000; Handy, 1996). Both authors explain that travel-demand modeling and
multinomial logit modeling can describe conditions and correlations, but not causal
relationships. As a result, the findings of those studies cannot explain why individuals
choose to drive rather than walk. That statement echoes Richards and Ben-Akivas
(1975) critique of travel-demand models from decades earlier.
Traditional models can be regarded as simply simulation models in
the sense that, while reproducing a known situation, they have few, if
any, explanatory powers. These are usually correlative models rather
than causal" (1975, p. 7).
Correlative studies, including aggregate-level and disaggregate-level analyses,
leave a gap in understanding because the relationships between factors may be
indirect or may be entirely coincidental. For example, McDonald (2008a) describes a
correlation between ethnicity and rates of walking to school, but then qualifies the
finding, suggesting that the correlation misleads readers.
20


However, models controlling for several individual and
neighborhood covariates found no differences among racial groups.
This suggests that differences in observed rates of active
transportation result from differences in the underlying distribution of
explanatory factors rather than varied behavior patterns across racial
groups (2008a, p. 343).
In order for researchers to learn why parents drive their children to school and to
guide active travel intervention, they must examine relationships among
environmental, socio-demographic and psychological factors.
Choice Models
Handy (1996) explains that even strong relationships between physical
environmental factors such as block length or sidewalk width and driving do not
guarantee that pedestrian-oriented urban form interventions will influence people to
walk more often. She recommends framing travel behavior research theoretically in
order to understand the nature of independent variables on mode choice. Choice
models reflect theories that account for the complex range of factors influencing
peoples choices, as opposed to focusing on one type of influence (Handy, 1996).
That breadth allows the research to guide intervention and influence peoples travel
decisions more efficiently.
McMillan (2005) proposes a conceptual model to explain childrens travel for
the journey to school (see figure 2-1). She bases the model on a review of planning
literature focused on efforts to influence travel mode choices for short school trips.
21


Through this model, McMillan (2005) considers how urban form changes such as
widened sidewalks might indirectly affect mode-choices. She assumes that parents
determine their childrens travel behavior and illustrates parents travel mode choices
as a complex function involving mediating and moderating factors.
FIGURE 2-1: CONCEPTUAL FRAMEWORK OF AN ELEMENTARY-AGED
CHILDS TRAVEL BEHAVIOR, Source: Adapted from McMillan (2005)
The model roughly outlines mediating factors as physical and social
environmental characteristics such as neighborhood safety and traffic safety. It also
includes household transportation options as a mediating factor, although that
characteristic might be interpreted as socio-demographic rather than environmental if
it describes access to private automobiles as opposed to public transportation. The
model outlines moderating factors as social and personal characteristics including
social/cultural norms, socio-demographics, and parental attitudes.
McMillan (2005) uses a multiplication sign (x) to indicate interaction between
mediating and moderating factors, and posits that personal characteristics magnify
22


parents responses to physical and social environmental characteristics. However, the
model focuses on the influence of urban form (physical environmental factors) as
determinants of the mediating factors, and ultimately the guiding influence in parents
travel decisions.
Rational Choice Models
Travel behavior research relies heavily on rational choice theory, the
dominant paradigm of microeconomics, which suggests that people choose actions
that promise to maximize benefits and minimize personal costs. Although the
research nominally emphasizes personal choice in decision-making, some researchers
argue that rational choice theory is inherently deterministic because individuals
responses to external stimuli are predetermined (Scott, 2000).
Planning research often describes travel behavior in terms of rational choice
theory without explicitly framing the discussion within a theoretical travel choice
model. For example, Boamet and Sarmiento (1998) describe travel demand (N) as a
function of time cost (p), individual income (y), and socio-demographic variables (S),
and further defines time cost as a conglomerate of urban form characteristics (L) such
as density, street grid orientation, and land use mix. They express the model as an
equation.
N=/(p,y; S), where p= / (L)
23


Cervero and Kockelman (1997) describe a rational-choice based conceptual
framework. The theories they cite assume that trip demand is a function of the
alternative destinations attributes such as quality of service or relative prices at
commercial establishments. They argue, however, that the framework should also
include site characteristics, particularly what they call the 3 Ds of the built
environment: density, diversity and design. This expanded model strongly resembles
Boamet and Sarmientos (1998) equation.
I present Cervero and Kockelmans (1997) conceptual framework with factors
from Boamet and Sarmientos (1998) equation in parentheses to illustrate their
similarities and to show that the relationships between factors also roughly parallel
McMillans (2005) model (see figure 2-2).
The frameworks include socio-demographic variables primarily for control
purposes1, but acknowledge that those factors are likely to moderate the influence of
time cost factors derived from characteristics of the built environment. All three
models focus on urban form as a predictor of travel demand. Although McMillan
(2005) qualifies that urban form changes only indirectly influence parents decisions,
all three models treat relationships between physical environmental characteristics
and mode choice as causal.
1 (1998) This model also includes transportation options and distance between home and
school as control variables.
24


BUILT ENVIRONMENT SOCIO-DEMOGRAPHICS (control variables)
(p time cost) -Gender, Race
-Densities (L density) (S sociodemographic variables)
-Diversity (L land use mix) X -Education, Household income (y individual income, S household
-Design (L street grid orientation) characteristics) (Transportation supply and services, Distance1)
TRAVEL DEMAND (and mode)
FIGURE 2-2: TWO UTILITY-BASED CONCEPTUAL FRAMEWORKS
Alternative Behavioral Theories
Planners and behavioral scientists have developed a number of conceptual
choice models that address deficiencies in rational choice theory. Critiques of rational
choice theory challenge the expectation that people make choices based on complete
knowledge of alternatives. They also dispute the singular focus on self-interest as a
motivation for choice. Although these critiques have led to refinements in rational
choice models, they still use the deterministic cost/benefit analysis as a predictor of
choice.
Bounded Rationality
Some scholars critique rational-choice theories because they assume that
people make perfectly rational decisions based on complete knowledge of alternatives
25


(Lindblom, 1959; Simon, 1957). Simon (1957) argues that in reality people (including
policy-makers and the policies target populations) make decisions based on bounded
sets of alternatives. However, the theory of bounded rationality concurs that within
the bounds of limited understanding, people generally work to maximize benefits and
minimize costs. For example, Lindblom (1959) argues that planners select from
among a few familiar policies rather than seeking the best option because resources
prevent thorough examination of alternatives. Similarly, people who are unfamiliar
with a bus system may not be inclined to take the bus, even if it promises to reduce
the time and monetary costs of travel. Thus, the theory of bounded rationality
suggests that policy can influence mode choice by expanding public knowledge of
alternatives.
Schulers (1979) choice model reflects Simons (1957) bounded rationality
theory by emphasizing cognition within an otherwise rational-choice model (see
figure 2-4). It describes a linear process by which individuals select retail outlets,
starting by perceiving attributes of alternative venues, then assigning values to them
and then ranking them by preference. Presumably the ranking follows a rational
analysis of costs and benefits. However, Schuler (1979) extends the rational-choice
model by acknowledging that cognition impacts the ways that people weigh costs and
benefits. For example, people can only compare the characteristics of alternative
venues with which they are somewhat familiar, as per Simons (1957) bounded
rationality theory.
26


FIGURE 2-3: CONCEPTUAL MODEL OF INDIVIDUAL SPATIAL CHOICE.
Source: Adapted from Schuler (1979)
The emphasis on cognition also suggests that people perceive known
alternatives differently, which means that environmental factors cannot solely predict
mode choices. Michelson (1976, 1977) argues this point, and suggests that antecedent
cultural and social systems influence perceptions of physical environmental
conditions, thus moderating activity choices. Consistent with that argument, Schulers
(1979) model describes a feedback loop in which previous shopping behavior
influences cognition of behavioral alternatives. However, it does not specify what
types of factors influence people to perceive alternatives differently.
McMillans (2005) model addresses Michelsons (1977) and Schulers (1979)
cognitive emphasis by indicating interaction between mediating and moderating
factors. That connection implies that certain personal characteristics may precondition
27


or predispose individuals to respond to environmental conditions differently.
McMillan (2005) only vaguely describes the nature of that interaction. She states:
There may also be factors that have no apparent relationship to
urban form and are not seen as intervening causal variables, yet affect
parental decision making about the trip to school (e.g., household
income, number and age of children in family, cultural norms). Such
variables may be moderators, meaning that the strength of the
relationship between an intermediate variable and parental decision
making may vary for different levels of a variable (such as age or
gender) (McMillan, 2005, p. 449).
According to her statement, personal characteristics may explain the cognitive limits
that Simon (1957) describes in his theory of bounded rationality. Still, she describes
the influence of environmental factors as causal, and the influence of personal
characteristics as having a secondary, moderating role in determining mode choice.
Complex Motivations
Some scholars critique rational choice theorys emphasis on self-interest and
argue that individuals may be motivated by other psychological influences. For
example, Weber (1920) and Parsons (1937) consider rational, emotional and value-
oriented influences on behavior. Their work influenced urban sociologists to examine
relationships among cultural, social and personality systems (Michelson, 1976).
While these types of factors begin to convey the complexity of psychological
influences on peoples choices, they can still be operationalized as part of the
deterministic cost/benefit analysis of rational-choice theory.
28


Chapins (1974) general model of human activity differentiates and elaborates
opportunity-related and propensity-related influences, but emphasizes
environmental factors in its discussion of intervention (see figure 2-3). In this model,
propensity-related influences include a range of motivations beyond utility
maximization, including enjoyment, thoughtways, roles and personal characteristics
predisposing and preconditioning action. Those elements are weakly comparable to
McMillans (2005) moderating factors, although Chapin (1974) differentiates
psychological and socio-demographic factors as two facets of propensity.
Opportunity-related factors in Chapins (1974) model include congeniality of
surroundings and availability and quality of facilities or services. Those elements are
comparable to McMillans (2005) mediating factors, and include physical and social
environmental characteristics. Like the mediating factors of McMillans (2005)
model, congeniality includes characteristics like neighborhood safety and traffic
safety that are influenced by urban form. Chapins (1974) model does not describe a
relationship between opportunity and propensity, except inasmuch as they both
determine behavior.
29


External Sources of
Change (e.g. Economic,
Population, Cultural)
Satisfaction-Dissatisfaction
Investments & Practices
of Private Sector
FIGURE 2-4: GENERAL MODEL FOR EXPLAINING ACTIVITY PATTERNS
Source: Adapted from Chapin (1974).
30


The model depicts two feedback loops. In the first, the activity pattern
influences policy-makers to intervene with investments and regulation. External
conditions such as economic, population or cultural changes also influence those
policy efforts, which focus on modifying the physical built environment and thereby
modifying the opportunity to engage in the activity. The model does not explain why
the activity pattern would elicit a policy response, but it may be assumed that policy-
makers perceive it to impact the health, safety and/or welfare of the public. In the
second feedback loop, the activity produces a level of satisfaction that influences
peoples motivation to participate in it again. The model does not describe policy
efforts to address satisfaction levels or to otherwise modify the propensity to engage
in the activity.
Chapins (1974) model emphasizes opportunity-related factors by suggesting
that public and private sector responses address them alone. McMillans (2005)
model similarly emphasizes opportunity-related, mediating factors by hypothesizing
that urban form indirectly determines mode choice. That emphasis on environmental
factors reflects a bias in planning literature on the subject, which often examines
elements of urban form as influences on travel behavior.
Environmental Determinism
Planning researchers debate the role of the built environment in shaping social
behavior (Lipman, 1969; Michelson, 1976). Michelson (1976) argues that early
31


planning research examined urban phenomena using a human-ecological approach
that treated the physical built environment as a staging area in which social behaviors
and pathologies could or could not occur. For example, early urban scholars described
the city in terms of concentric zones, influenced by the growth of the central business
district. Zones located farther from the CBD were negatively correlated with rates of
gambling and other vices because their lower land values did not invite speculation
(Michelson, 1976). In that respect, the physical environment was not seen as a direct
influence on behavior except inasmuch as it housed economic activities. Michelson
(1976) suggested that planners should examine the combined influences of
environmental, social and personal systems in order to understand urban processes,
and emphasized that researchers should not overlook the influence of urban form on
social behavior.
In contrast, Lipman (1969) argues that the architecture profession embraces a
belief system that strategically exaggerates the influence of the built environment on
social behavior. The architectural belief system that Lipman (1969) recounts begins
with functionalism the understanding that form follows function (Blake, 1963;
Gropius, 1959). It then proceeds to determinism the understanding that form causes
function (Brody, 1966). Finally, it concludes with social engineering the
understanding that architects should use the built environment to shape social
behavior. Lipman (1969) suggests that architecture has solidified its professional role
by emphasizing its ability to direct social behavior.
32


Active travel research examines travel mode choices using conceptual models
that reflect a deterministic rational choice theory. McMillans (2005) model
emphasizes the influence of environmental factors, but includes social and personal
factors as secondary explanations of social behavior. In this respect, planning theory
reflects the environmental determinism that is characteristic of the architectural belief
system.
Summary
Supporting intervention to increase rates of active travel, researchers identify
various types of factors and their correlations with mode choices. Correlation studies
describe conditions surrounding travel behaviors, but do not explain why parents
choose to drive rather than walk their children to school (Handy, 1996; Richards &
Ben-Akiva, 1975). As a result, some scholars argue that planning research cannot
effectively guide intervention (Crane, 2000; Handy, 1996).
McMillan (2005) proposes a theoretical model to explain childrens travel
behavior for the trip to elementary school, and to guide active travel intervention. The
model includes key elements of choice models from other behavioral sciences, but
does not detail relationships between factors. Like other choice models in planning,
McMillans (2005), model emphasizes environmental factors that provide the
opportunity to walk over personal factors that may incline parents to engage in active
travel for the trip to school. That emphasis reflects a bias in the planning literature,
33


which largely aims to determine how changes in urban form can affect travel demand
and mode choice (Boamet & Sarmiento, 1998; Cervero & Kockelman, 1997;
McMillan, 2005). That bias reflects a professional belief system, which posits that
built form can be used as an intervention to influence social behavior (Lipman, 1969).
My dissertation research builds on McMillans (2005) and Chapins (1974)
theoretical models to study parents mode choices for trips to school. I examine
parents attitudes about the school commute, and consider the interaction between
environmental, social and personal factors as they relate to the opportunity and
propensity to use active travel modes. In the next chapter, I review planning research
in more detail, and consider why the emphasis on opportunity-related, environmental
factors can limit the capacity of planners to guide intervention.
34


CHAPTER 3:
WHY DO PARENTS DRIVE CHILDREN TO SCHOOL?
AN INTERPRETIVE REVIEW OF LITERATURE
Introduction
Percentages of children walking to school have declined between 1969 and
2001 (McDonald, 2007; USDOT, 1969-2001). In Chapter One, I discussed three
concerns associated with that trend and policy that aims to reverse it. Policy-making
organizations at the national level, including the U.S. Department of Transportation,
aim to encourage more children to walk to school (Hubsmith, 2006). However, some
scholars argue that transportation planning research has not explained the problem -
why people drive sufficiently to guide policy changes (Crane, 2000; Handy, 1996).
To encourage children and their parents to walk to school, researchers need to
first conceptualize parents mode choices as part of a comprehensive explanatory
model (Handy, 1996). In Chapter Two, I examined McMillans (2005) model of
childrens school travel and noted that the model captures key elements of both
Chapins (1974) and Schulers (1979) activity-choice models. However, McMillans
(2005) model emphasizes the opportunity to walk children to school, neglecting
parents propensity to do so if they have the chance. Magnifying that emphasis,
35


the model also does not explain relationships between key elements such as socio-
demographic and environmental characteristics. I borrow concepts from those models
to frame my empirical study.
In this chapter, I review research that explains why parents drive children to
school instead of walking. Similar to McMillans (2005) model, I argue that
researchers interpretations of findings tend to emphasize opportunity-related factors
and to diminish parents attitudinal diversity. Planning research about school travel
addresses the fundamental question why do parents drive children to school? -
using qualitative and quantitative methods. Both approaches richly describe the
school commute, but neither guides policy to strategically target parents who drive.
The chapter is organized to first briefly review qualitative and quantitative
research methods used to study school travel. Then it discusses opportunity-related
and propensity-related factors associated with mode choice for school trips.
Qualitative studies associate mode choice for the school trip with a variety of social
and physical environmental factors that either enhance or restrict parents opportunity
to walk children to school. They also associate mode choice with socio-demographic
and psychological factors that affect parents propensity to walk. However, most
studies use quantitative methods to distill those findings, concluding that policy
should target key environmental2 obstacles that prevent children from walking.
2 Note that throughout my dissertation, the term environmental refers to both social
and physical characteristics unless otherwise specified.
36


Travel mode research tends to focus on conventional environmental and
socio-demographic variables, neglecting the psychological facets of mode-choice. It
also emphasizes the opportunity to walk even in studies that prioritize examining
socio-demographic variables. As a result, only limited research addresses parents
inclination to drive, or the diversity of their attitudes about the commute.
Why do parents drive children to school?
Exploratory and descriptive studies use a variety of methods to identify
opportunity-related (environmental) and propensity-related (socio-demographic and
psychological) factors associated with driving children to school (Black, Collins et
al., 2001; Bradshaw, 1995; Collins & Keams, 2001; DiGuiseppi, 1998; Hillman,
Adams et al., 1990; Joshi & MacLean, 1995; McDonald, 2007; McMillan, 2006b;
Pooley, 2004; CDCP, 2005; Schlossberg, Greene et al., 2006; Timperio, Ball et al.,
2006). For example, data collection methods include open-ended oral histories and
group discussions as well as structured interviews, questionnaires and large-scale
surveys. The more open their data collection, the more the studies findings reflect
psychological factors in addition to environmental and socio-demographic factors
(see Table 3-1). In the next section, I review studies that use qualitative methods
followed by studies that use quantitative methods.
37


Table 3-1: Range of Factors Associated with Travel Mode
o CO Opportunity-related Propensity-related
u CO a. £ Social/Physical Socio-demographic Psychological
Environmental
CO Distance Car access Enjoyment
2 S- "C Proximity Life pace
O +-> co Social contact
jC Risk perception
Bullies Age Car-centredness
Crime Car access Child preference
Crossings Gender Contact (social/teacher)
CO a. Distance Maturity (child) Convenience/time
I Dogs Number of children Encouragement/praise
Cd) to Driveways Youngest child Enjoyment
3 CJ Parking problems Environmental awareness
U- Stranger Danger Habit
CO £ Surveillance Health benefits
.2 Traffic danger/congestion Importance
U Visibility Individual responsibility
C Weather School (materials)
Vi c a> Social norms
O JZ o Trip-linking
w o Bus routes Age Activities (caregiver/student)
s CO Block length/design Car access Attitudes about active travel
o V l Crime Education Convenience
& 3 &0 Crossings Employment Cost
Vi 0) c/T Cul de sacs Gender Family circumstances
L* 3 Directness Household size Habits
cd Distance Income School (materials, programs)
£ Housing type Language Social norms
.> Incomplete sidewalks Licenced Peer pressure
O 0> Infrastructure Marital status Trip-linking
[E5 o Intersections Physical condition Values
c/f Neighborhood children Race
.Si Percent grid Socio-economic status
*0 Population density Tenure
Retail density Weight
3 u Service density
H Stranger Danger
s Topography
3 C Traffic danger
c o Urban/suburban
CO 3 a
38


Qualitative Studies
As an example of qualitative data collection methods, Pooley et al. (2004)
conducted 156 oral history interviews of children, parents and grandparents and used
qualitative analysis techniques to find out what was happening during their
elementary school years, how they got to school and why they picked the travel mode
they reported.
This example of open methods identified environmental, socio-
demographic, and psychological, reasons for changes in travel behavior. Specifically,
Pooley et al. (2004) found four broad trends associated with increased car use:
increasing distances from home to school, increasing access to private vehicles, faster
pace of life, and increasing perceptions of risk. The study also describes continuities
of walking associated with opportunities to socialize, personal enjoyment and some
families proximity between home and school. The open interview oral history
approach allowed results to include two seemingly contradictory factors: distance and
proximity. This finding suggests that factors significant to some parents are not
significant to all parents.
Similarly, Joshi and McLean (1995) facilitated group discussions with open-
ended questions and use qualitative analysis techniques to find out why parents
choose a particular travel mode. Their study found fifteen reasons that parents drive
to school. Collins and Kearns (2001) surveyed to find out mode frequencies, but
include open-ended questions on the self-completion form. By asking parents the
39


open-ended question, Why do you drive? both studies elicited responses that
included environmental, socio-demographic and psychological dimensions of choice.
In contrast, Black et al. (2001) conducted interviews for a pilot study, but
directed parents to explain in detail and with free responses their understanding of,
and attitudes to, traffic and transport problems (Black, Collins et al., 2001, p. 1129).
Although the interview allowed parents to respond openly, it limited the input to
specific facets of the policy issue rather than openly soliciting their reasons for
driving. That approach ruled out specific intervention tactics, but did not find new
ones.
Qualitative data collection methods are useful for exploring why parents drive
their children to school. They allow respondents to define and/or expand the list of
factors that explain their trip decisions, and they allow respondents to respond freely,
rather than forcing them to select from among limited alternatives. However, research
objectives may not always warrant using qualitative data collection methods. Open-
ended interviews and systematic observations are resource intensive, typically involve
smaller numbers of respondents and cannot generalize findings beyond the study area.
In the next section, I review studies that use quantitative research methods.
Quantitative Studies
Studies of school travel using structured data collection methods such as
questionnaires and large-scale surveys with single or multiple-answer, multiple-
40


choice questions find combinations of previously defined environmental, socio-
demographic and psychological factors rather than learning new ones. That approach
can be useful for determining population parameters. For example, it can find out
what proportion of the study population expresses concern over stranger danger.
However, because the choices are limited, it forces respondents to select something as
their explanation for travel mode choice, even if their true explanation is not listed.
Also, the multiple-choice format may plant ideas that otherwise were not a concern.
For example, by reading that stranger danger can be a problem, parents may adopt it
as a personal concern even if they previously had not considered it one.
Bradshaw (1995) and DiGuiseppi et al. (1998) conducted self-completion
multiple-choice surveys using conventional econometric variables such as distance,
traffic safety, stranger-danger, and conventional socio-demographic variables such as
age, gender, and household car-ownership. However, Bradshaw (1995) also included
convenience, trip-linking and peer pressure, which reflect psychological explanations.
Similar to Bradshaw (1995), McMillan (2006b) measured psychological facets of
choice by including likert-scaled attitudinal measures of neighborhood safety,
household transportation options, caregiver attitudes, and social/cultural norms. These
studies hypothesized relationships between variables rather than broadly identifying
reasons that parents drive. As Handy (1996) points out, those studies can describe
correlations, but cannot explain travel decisions without a strong theoretical model.
41


CDCP (2005) and Schlossberg et al. (2006) conducted surveys that included
conventional socio-demographic variables but also asked respondents to mark which
of several predefined obstacles prevent them from walking. CDCP (2005) defined
obstacles primarily as environmental variables distance to school, traffic danger,
crime and weather conditions. Schlossberg et al. (2006) borrowed their list of
obstacles from Smart Ways to School, a program in Lane County, Oregon, funded by
the U.S. Environmental Protection Agency, and emphasized personal/psychological
variables convenience, trip linking, heavy backpack, after school programs, and
projects or instruments to transport. They also included environmental obstacles -
stranger danger and weather- as well as objective measures of neighborhood urban
form identified by geo-coding the surveys. Timperio et al. (2006) similarly paralleled
perceived and objective measures of environmental variables, but also included
perceived and objective measures of personal fitness. By including pre-defmed
variables, these studies measured what portion of the target population each obstacle
influences. Doing so focuses attention on the obstacles rather than the affected
population.
McDonald (2007) analyzed data from several years of National Personal
Travel Surveys. Using the existing data set had the potential to limit her analysis to
trip, child, and household characteristics. However, because the study was
longitudinal, she introduced and associated additional historical trends such as
changing school enrollment policies and childhood obesity levels with changing rates
42


of active travel to school. As a result, her structured study has an exploratory quality
that allowed it to identify reasons parents drive to school.
It may not be necessary for every study to identify new factors that parents
associate with their travel choices for school trips. However, by limiting the selection
of variables in surveys, research may mislead policymakers by overemphasizing
certain factors, or by influencing respondents perceptions. In the next section, I
explain how interpretation of survey data overemphasizes environmental barriers to
active travel.
Distilling Findings to Emphasize Environmental Barriers
Most studies that use qualitative data collection methods and content analysis
also use a second, quantitative method of data collection and analysis that reduces
findings to the most significant factor(s) (Black, Collins et al., 2001; Collins &
Kearns, 2001; Hillman, Adams et ah, 1990; Joshi & MacLean, 1995). Studies that
only use quantitative data collection methods similarly reduce findings (Bradshaw,
1995; DiGuiseppi, 1998; McDonald, 2007; CDCP, 2005). It is significant to note that
the reduction of findings tends to emphasize students opportunity to walk to school
in terms of environmental barriers.
For example, in addition to the open-ended question and qualitative analysis
mentioned above, Collins and Kearns (2001) used a mapping exercise to ask
respondents, Where are dangerous places? The second interview question focused
43


responses on geographically specific environmental safety concerns. Despite the
broader range of responses from their first question, the authors concluded with
recommendations that only address traffic safety. Schlossberg et al. (2006) similarly
focused their findings through a mapping exercise that diminished non-environmental
explanations for car travel. That approach led respondents to claim environmental
explanations for their choices, even if those explanations were secondary.
In addition to their open-ended interviews, Hillman et al. (1990) and Joshi and
McLean (1995) used self-completion questionnaires that asked parents to pick from
among several reasons that they drive to school. These two studies differ because
Joshi and McLean (1995) allowed parents to mark more than one answer, although
they also ask them to point out their main reason. Both studies reduced findings to the
single most significant factor. Hillman et al. (1990) identified traffic danger whereas
Joshi and McLean (1995) identified stranger danger. By distilling findings, these
studies appear to contradict each other by suggesting different most important
factors. While they both looked for a single factor, what they found was a
combination of factors relevant to parents travel choices.
Bradshaw (1995), DiGuiseppi (1998), CDCP (2005) and McDonald (2007)
used statistical methods to reduce survey results to the most significant factors.
Excepting Bradshaw (1995), whose findings included trip-linking, all of the studies
emphasized environmental obstacles to active travel. All four studies concluded that
distance between home and school was the best predictor of mode choice, although
44


CDCP (2005) also mentioned traffic danger. Their agreement reinforces the perceived
significance of distance from school, which suggests that school districts should
change their catchment areas in order to encourage more students to walk. That
recommendation is valid if travel mode is given highest priority. However, open
enrollment policy also provides families with educational and practical alternatives
for their childrens education that may outrank travel mode.
Black et al. (2001) departed from the environmental focus of the literature by
emphasizing attitudinal factors. They conducted an attitudinal, Likert-scaled analysis
to find out which of thirty-one statements resonate strongly with respondents. They
distilled the findings from that questionnaire to the three strongest attitudinal factors:
environmental awareness, personal responsibility and car-centredness. Factor analysis
further distilled the results and concluded that environmental awareness is not as
strong a predictor as the other two. This study breaks new ground by measuring
attitudinal factors for school travel in a probability model. However, similar to other
quantitative studies, emphasizes a single factor.
In contrast with studies identifying a single prominent factor, McMillan
(2006b) and Timperio et al. (2006) used statistical methods to analyze results of
questionnaires and surveys, and concluded that a range of environmental, socio-
demographic and psychological factors influence travel mode to greater and lesser
degrees. Rather than isolating the most significant factor overall, Timperio et al.
(2006) described the relative influence of findings for specific socio-demographic
45


subgroups. The study by Pooley et al. (2004) also stands out because it presented
findings from open-ended interviews without reducing them beyond the initial
content analysis. As a result, their findings suggested a range of options for active
travel intervention, each of which may target some portion of students and their
parents.
By emphasizing opportunity-related environmental barriers, extant research
tends to assume that parents would allow their children to walk if they could. This is
important because that assumption diminishes parents attitudinal diversity and
prevents the research from directing intervention to address strategic subgroups of
parents.
Specific Variables
As described above, travel mode research identifies opportunity-related
(environmental), and propensity-related (socio-demographic and psychological)
variables associated with parents mode choice for school trips. Quantitative research
tends to include conventional environmental and socio-demographic variables,
neglecting the psychological facet of mode-choice. It also emphasizes the opportunity
to walk even when it purports to address socio-demographic variables. As a result,
only limited research addresses parents propensity to drive, or their attitudes about
the commute to school. Extant research generally assumes that parents are
homogeneous in their attitudes about school travel, which suggests that intervention
46


should be narrowly defined and broadly administered to increase pedestrian activity.
In the next section, I review opportunity-related and propensity-related explanations
for travel mode choice.
Opportunity-Related Factors
Much planning research assumes that opportunity-related, environmental
obstacles prevent active travel behavior. Although the research identifies physical and
social environmental characteristics, it rarely differentiates the two and often uses the
titles interchangeably. For example, Oneil et al. (2001) list objective physical
characteristics of neighborhoods that influence childrens home range and include
traffic patterns, lighting, levels of crime and vandalism, muggings, poverty and lack
of social control. Although each of those environmental factors may influence
independent mobility, all but the first two describe social environmental
neighborhood characteristics.
This distinction is important because, as McMillan (2005) illustrates in her
model of childrens travel behavior, characteristics of urban form only indirectly
impact parents travel mode choices by influencing primarily social environmental
conditions. Paradoxically, planning research and policy that aim to encourage active
travel focus almost exclusively on physical environmental variables. Although
qualitative studies identity several social environmental factors associated with mode
47


choice such as stranger danger and bullying, there are few studies that specifically
examine them in that context.
Physical Environmental
Research focused on physical environmental variables examines how
characteristics of land use, urban form and distance to school influence mode choice
and how they might be manipulated to encourage active travel (Boamet &
Greenwald, 2001; Boamet & Sarmiento, 1998; Cervero & Kockelman, 1997; Cervero
& Radisch, 1996; Crane, 1996, 1998, 2000; Greenwald & Boamet, 2001; Handy,
1996; McMillan, 2005).
Studies of land use and urban form fit into a broader discourse of pedestrian
oriented design. In my review, I include research regarding the influence of New
Urbanism on travel behavior. Evaluations of Safe Routes to School infrastructure
improvements similarly test whether urban form influences travel mode (Boamet,
Anderson et al., 2005; Boamet, Day et al., 2005; McMillan, 2006b). In that respect,
school travel research fits neatly into broader discussions of pedestrian-orientation.
Challenging New Urbanism
Advocates of new urbanism conceptualize opportunity in terms of time costs,
and expect land use characteristics to influence travel mode (Appleyard, Gerson et al.,
1981; Katz, 1993). However, empirical evidence to support their claims remains
inconclusive (Boamet & Sarmiento, 1998; Crane, 2000; Handy, 1996). Several
48


studies challenge claims of new urbanism, testing the hypothesized relationship
between urban form and travel mode. In general, the studies have been moderately
successful in establishing broad correlations between characteristics of urban form
and travel mode. However, they do not explain changes of behavior.
For example, Cervero and Radisch (1996) and Cervero and Kockelman (1997)
compared two San Francisco neighborhoods and found that clusters of measures
representing density, diversity and design correlate with mode choice. They qualified
the findings, saying that urban design characteristics are more strongly associated
with mode choice for non-work trips than for work-related trips. Those findings
might apply to school trips and would suggest that urban form influences mode
choice for students and their caregivers. However, in a comparative study of walkable
and non-walkable neighborhoods, Frank et al. (2006) found that active travel is
associated positively with active lifestyles regardless of environmental context. That
is, if people are inclined to walk, they walk, even if there are no sidewalks.
Researchers have had difficulty establishing causal relationships between
urban form and travel mode, especially where behavioral change is concerned. Crane
(1996) describes an econometric model of travel demand, based on the time costs of
various neighborhood characteristics and finds that models cannot accurately predict
changes of behavior. Crane (1998) later used travel diaries and GIS data but still
found only weak correlations that were not suited to guide intervention. One problem
with correlative urban form research is that people may self-select to live in urban
49


neighborhoods because they enjoy more active lifestyles (Handy, 1996). To guide
active travel intervention, researchers need to examine the impact that urban form
changes have on travel behavior. In the next section, I review research that examines
density, diversity and distance, and design.
Density
Research is well developed regarding the impact of density on travel demand
(Cervero & Radisch, 1996; Levinson & Wynn, 1963). However, demand can been
accommodated with a variety of travel modes. More significant to advocates of active
travel, researchers have begun to investigate the impacts of density on travel mode,
particularly the choice between motorized and non-motorized modes for work and
non-work trips (Boamet & Greenwald, 2000; Cervero & Radisch, 1996; Greenwald
& Boamet, 2001).
Researchers hypothesize that housing density and subsequent population
density influence travel mode in at least two ways. First, the increase of demand
facilitates alternative modes of transportation, such as public bussing or ride sharing
in lieu of single occupant, private automobiles. However, demand for alternative
transportation does not necessarily influence active forms of travel, except for the
portion of each trip from origin and destination to transit stops, which is often
disregarded in research.
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Second, higher population density can facilitate informal surveillance, what
Jacobs (1961) calls eyes upon the street, making streets safer for pedestrians. She
explains thus:
The public peace the sidewalk and street peace of cities is... kept
primarily by an intricate, almost unconscious, network of voluntary
controls and standards among the people themselves, and enforced by
the people themselves (Jacobs, 1961, p. 40).
As an example of that phenomenon, Ross (2007) found that childrens active
travel to school reinforces informal surveillance because of the weak ties they form
through repeated short interactions with people along the way. Consistent with this
theory, Appleyard (2003) found that high neighborhood vehicular traffic correlates to
fewer social connections. That suggests that people who do not take as many walking
trips make subsequent walks more hazardous.
Researchers similarly hypothesize that commercial density including service
and retail influences travel demand and mode. That relationship largely depends on
access to employment and on distances between origin and destination for work and
non-work trips. Studies show correlations between non-motorized trips and land use
diversity (Boamet & Greenwald, 2000; Cervero & Kockelman, 1997). However the
correlations are stronger for non-work trips (Boamet & Greenwald, 2000; Cervero &
Kockelman, 1997; Greenwald & Boamet, 2001). That finding suggests that people
continue to drive to work, even if they use active travel for shopping trips.
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Land Use Diversity and Distance
Researchers hypothesize that land use diversity may encourage active travel
(Boamet & Greenwald, 2000; Cervero & Kockelman, 1997; Greenwald & Boamet,
2001). Diversity indirectly relates to active travel by shortening travel distances or
improving pedestrian safety. For example, Cervero and Kockelman (1997) included
block length and intersection type as measures of circulation access, and found that in
conjunction with other characteristics of urban form, diversity modestly correlates
with travel demand.
School travel research identifies trip distance among the more significant
factors determining mode choice (Bradshaw, 1995; Collins & Kearns, 2001;
DiGuiseppi, 1998; Krizek, 2003; McDonald, 2007; CDCP, 2005; Schlossberg,
Greene et al., 2006). For example, McDonald (2007) argues that increased distance
alone explains over half of the decline in active travel. She notes that the increased
distance may be explained by several decades of school policy changes that adjusted
catchment areas, introduced bussing and increased the size of schools.
Contemporary national school guidelines also include minimum lot sizes that
cannot be achieved in densely developed areas, pushing schools to the urban fringe
and increasing travel distance (EPA, 2003). A study by the Environmental Protection
Agency recognizes that existing regulations discourage the construction, adaptation
and maintenance of smaller schools, instead dictating the use of large new campuses
that may not be in the best interest of public health (EPA, 2003). Location aside,
52


larger schools often necessitate wider catchment areas, which equates to greater travel
distances for a portion of students, and increased transportation costs for schools.
Lomax (1977) found that inner city schools often have less defined catchments and
longer commutes than their suburban counterparts. This may be explained, in part, by
open enrollment policies.
Encouraged by the 2002 No Child Left Behind legislation, citywide
enrollment policies result in greater travel distances for a portion of students,
particularly in districts with vouchers, magnet programs and choice systems (Wilson,
Wilson et al., 2007). Private schools similarly allow enrollment that is not
geographically defined, resulting in greater travel distances (DiGuiseppi, 1998).
Advocates of the choice system argue that the market concept improves schools
accountability, encourages desegregation, and allows students and parents to be more
actively involved in educational quality control (Schellenberg & Porter, 2003;
Schneider, Teske et al., 1997; Taylor & Gorard, 2001; Wilson, Wilson et al., 2007).
In contrast with school size, open enrollment may mean that students and their
parents travel extraordinary distances to attend smaller, higher-scoring, or otherwise
specialized schools, and that district bussing is not available for them. Although
parents express satisfaction with these enrollment systems, the longer travel distances
impose costs to the families and to their communities in terms of vehicle miles,
driving time, physical activity lost and increased greenhouse gas emissions.
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Travel distance tops the list of reasons cited by parents for driving children to
school. However, a study by the Center for Disease Control found that only 31
percent of children living within one mile of the school walk or bike (CDCP, 2005).
This statistic suggests that efforts to increase rates of active travel must consider other
explanations for the historic mode change in order to influence behavior.
Design
Although few studies directly examine the influence of traffic danger on travel
mode choices, it ranks among parents top concerns regarding the school commute
(Collins & Kearns, 2001; DiGuiseppi, 1998; Gill, 1997, 2006; Hillman, Adams et al.,
1990; CDCP, 2005). Rather than examining hazards directly, researchers measure the
influence of design elements aiming to alleviate traffic dangers including road widths,
curb cuts, pedestrian crossings, parking styles and continuity, width and maintenance
of sidewalks (Appleyard, Gerson et al., 1981; Boamet, Anderson et al., 2005; Boamet
& Greenwald, 2001; Cervero & Kockelman, 1997; Cervero & Radisch, 1996).
For example, Boamet, Anderson et al. (2005) and Boamet, Day et al.(2005)
surveyed parents to find out whether construction for SR2S infrastructure projects
influenced them to walk more. Boamet, Anderson et al. (2005) used the subjects
proximity to the construction site en route to school to determine a control group,
whereas Boamet, Day et al. (2005) surveyed before and after. Both studies found that
the construction increased walking and successfully improved safety near the school.
However, those changes may also be explained by confounding variables. For
54


example, there may be a placebo effect in which parents who pass by construction
sites alter their travel behavior because they feel that someone is doing something to
improve the neighborhood, rather than changing behavior because of the new
infrastructure per se.
Policies to increase active travel often modify infrastructure in order to
improve pedestrian opportunities. For example, Gill (2006) presented case studies of
home zones in the U.K. that use a variety of tools to limit car speeds, including
traffic circles, on-street parking and other road narrowing designs. Those elements
reinforce published very low speed limits in residential neighborhoods to protect
pedestrians, and are associated with increased pedestrian safety and access to streets.
Social Environmental
As I mentioned previously, planning research identifies social environmental
factors associated with travel behavior. That research emphasizes social
environmental pathologies such as stranger danger that prevent active travel.
However, positive social environmental characteristics such as a strong sense of
community may also influence parents travel mode choices for childrens school
trips. Research in other behavioral fields examines social environmental
characteristics as influences on childrens independent mobility and other
developmental objectives. These tangential lines of research shed light on travel mode
choices for school trips.
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Social Environmental Pathologies
Stranger danger emerges as a key factor in planning research regarding active
travel (Bradshaw, 1995; Collins & Kearns, 2001; Hillman, Adams et al., 1990; Joshi
& MacLean, 1995; CDCP, 2005). Bradshaw (1995) included concerns about
personal safety of children as among the top factors influencing parents mode
choice for school trips, and finds that personal safety of the child is the most common
explanation of mode choice. Similarly, Collins and Kearns (Collins & Kearns, 2001)
and CDCP (2005) found that fear of crime and stranger danger rank among the top
reasons parents give for neighborhoods being dangerous for children.
Planning research identifies, but does little to examine social environmental
pathologies as influences on travel mode. Although Hillman et al. (1995) interpreted
their findings to emphasize traffic danger, their study concurs with Joshi and Kearns
(1995) that stranger danger inhibits a significant proportion of parents from allowing
children to walk to school. These two studies included stranger danger among lists
of obstacles for parents to select in survey form. In that context, the term might evoke
a variety of images for parents who fear social environmental pathologies as
dissimilar as abductions and bullying. Despite recognition of its significance, stranger
danger is not typically included in predictive travel behavior models.
Perceptions of criminal activity may have a greater impact on parents choices
regarding their childrens freedoms than do actual criminal events. Accordingly, few
studies discuss the impacts of objectively measured crime on travel behavior.
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However, McDonald (2008b) compared data from the 2000 Bay Area Travel Survey
and crime data from the Oakland Police Department and finds that for minority
adults, high rates of violent crime in the neighborhood negatively correlate to the
amount of time spent walking. Nonviolent criminal activity may also indirectly
impact travel behavior for children. Some studies find that poor physical
environmental conditions such as graffiti and litter help to create a fear of criminal
danger in parents that restricts their childs travel and play boundaries (McMillan,
2005; Moore, 1986; Valentine, 1997).
Behavioral researchers argue that magnified perceptions of social risks
support gendered legislation based on patriarchal ideology (Websdale, 1999). For
example, studies relate risk perceptions with increasingly cautious parenting styles,
including stricter regulation of childrens social activities (O'neil, Parke et al., 2001;
Pain, 2006) Similarly, studies suggest that fear of criminal activity, particularly
abduction, influences parents to limit their childrens travel and play boundaries,
which might also discourage active travel to school (DiGuiseppi, Roaberts et al.,
1998; Eichelberger, Gotschall et al., 1990; McMillan, 2005; Moore, 1986; Valentine,
1997).
Exploratory planning research identifies perceived threats from other children
and youth (i.e. bullying) as an influence on travel mode for school trips, but does not
examine the issue in greater detail (Joshi & MacLean, 1995). However, a substantial
body of behavioral research investigates characteristics ofbullies versus those of
57


victims to determine why bullying occurs and how to stop it (Giacomantonio, 2009;
McGuckin, Cummins et al., 2009). Other behavioral research examines the influence
of bullying on a range of developmental outcomes including socialization patterns,
self-esteem, school satisfaction, and empathy (Finkelhor, Ormrod et al., 2009;
McGrath, Brennan et al., 2009). Similar to other forms of stranger danger, the
presence of bullying is not typically included in predictive travel behavior models.
Social Environmental Assets
Focused primarily on obstacles to active travel, planning research scarcely
mentions social environmental assets as possible determinants of travel mode. Some
characteristics that appear in the literature include the presence or peer influence of
other neighborhood children (Bradshaw, 1995; Frank & Engelke, 2001; Timperio,
Ball et al., 2006) and informal surveillance (Collins & Kearns, 2001). For example,
Bradshaw (1995) found that children and parents are peer pressured into driving to
school. In contrast, Orsini (2006) described circumstances in which students who ride
bicycles to school encourage friends to do the same.
Positive social environmental factors often have reciprocal relationships with
active travel, and are recognized as potential benefits rather than determinants of
mode choice. (See Chapter One for a discussion of benefits). For example, a strong
sense of community within a neighborhood can encourage active travel by providing
informal surveillance and therefore countering fears of abduction and other crimes.
Reciprocally, some researchers argue that children who are allowed to travel
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independently create those protective social contacts en route (Kearns, Collins et al.,
2003; Neuwelt & Kearns, 2006). For example, Kearns et al. (2003) described a
number of benefits associated with the Walking School Bus system in Auckland,
New Zealand. Listed among those benefits is strong parent participation, which
subsequently makes the route safer for the activity.
Planning research about active school travel identifies a number of social
environmental factors associated with mode choice, but it does not examine them as
extensively as physical environmental factors, and tends to emphasize pathologies
over assets. Those factors tend to be excluded from predictive models, perhaps due to
the reciprocal nature of the relationships, and recognition of positive social
environmental characteristics as benefits (outcomes) rather than determinants of
active travel.
Research focused on environmental attributes naturally emphasizes the
opportunity to walk and obstacles that prevent it. That approach assumes that people
generally desire to walk, but cannot because of specific environmental obstacles.
Therefore, it guides policy to eliminate those obstacles in order to encourage active
travel for everyone. However, since there is typically a portion of the study
population that chooses to drive despite having the opportunity to walk, researchers
should examine differences in their study populations.
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Propensity-Related Factors
Planning research about active travel acknowledges, but does not fully
examine parents propensity to walk children to school. Propensity-related factors
include socio-demographic characteristics such as race and ethnicity, household
income and educational attainment, genders of children and drivers, and childrens
ages. They also include psychological factors such as convenience, control and
autonomy, identity and status.
Socio-Demographic
Planning researchers often control for socio-demographic variables in order to
test characteristics of land use and urban design for the broader population (Bemetti,
Longo et al., 2008; Boamet & Greenwald, 2000; Cervero & Kockelman, 1997; Frank,
Sallis et ah, 2006; Krizek, 2003). By assuming that parents would walk children to
school if they had the opportunity, that research takes for granted that those values are
widespread. However, some researchers argue that interventions are not likely to
affect target populations uniformly (Bemetti, Longo et ah, 2008).
Several studies discuss how socio-demographic characteristics including race,
income, gender and age influence travel behavior (Bemetti, Longo et ah, 2008;
McDonald, 2008c; McMillan, T., 2006b). However, standard socio-demographic
indicators arbitrarily sub-divide populations and imply commonalities that do not
hold true at the disaggregate level. Activity-based research expands socio-
60


demographic studies by analyzing clusters of variables to capture roles and life-
cycle stages that may influence travel mode choices (Handy, 1996; Hillman, Adams
et al., 1990). For example, Salomon and Ben-Akiva (1983) examine travel behavior
for subgroups defined by the major roles of: household member, worker, and
consumer of leisure. Although the activity-based approach provides highly
descriptive correlations, the findings still cannot explain why parents drive to school
(Handy, 1996). Solving that persistent problem requires an alternative research
approach. In the next section, I review research focusing on race and ethnicity,
parents income and education, genders of children and drivers and ages of children
in the home.
Race and Ethnicity
Researchers focused on race and ethnicity as predictors of travel mode
struggle with confounding variables that make findings difficult to interpret. Instead,
they interpret them through alternative theoretical lenses. For example, Frank (2001)
observed that environmental barriers may have disproportionate impacts on different
subgroups within the population, most especially for vulnerable groups (Frank &
Engelke, 2001, p. 209). McDonald (2008a) found that for low-income and minority
students, violent crime presents a critical obstacle to active travel. Neither of these
studies suggests that pigment has anything to do with travel behavior. Instead their
interpretations focus on social justice rather than active travel, and suggests that
61


obstacles such as neighborhood crime rates prevent vulnerable groups (i.e. minorities)
from walking more so than others.
However, studies show that minority populations walk more frequently than
white populations (McDonald, 2007, 2008a). In a longitudinal study of active travel,
McDonald (2007) found that minority students are twice as likely as white students to
walk to school. McDonald (2008a) showed significant differences in rates of active
travel between Hispanics (27.7%), non-Hispanic Blacks (15.5%), Asian and Pacific
Islanders (13.4%), respondents reporting more than one race (12.2%), and Whites
(9.4%). The strong correlation between minority status and high rates of childhood
obesity appears to contradict findings of higher rates of active travel for the same
population. This suggests one or more confounding factors, such as access to healthy
foods. McDonald (2008a) and (2008b) note that ethnicity and racial factors are often
nullified when controlling for other individual and neighborhood covariates, such as
household income, education level, rates of reported crime and other factors .
Parents' Income and Education
Research identifies household income, and education level as factors
associated with travel mode, but results are inconclusive at the disaggregate level
(Bradshaw, 1995; DiGuiseppi, 1998; McDonald, 2008a). For example, Boamet and
Sarmiento (1998) found that household income is positively associated with active
travel. In contrast, McDonald (2008a) found that students from lower-income
households walked more than twice as often as those from higher-income households.
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The discrepancy may be explained by the scale and location of the study, since
Boamet and Sarmientos (1998) study was conducted at the neighborhood level in
California, whereas McDonalds (2008a) study drew from the National Household
Travel Survey. This suggests that additional research is necessary to better understand
the role of income in active travel.
Some researchers argue that income has an indirect influence on travel mode
because it determines access to private vehicles (Bemetti, Longo et al., 2008;
Bradshaw, 1995; Cervero & Kockelman, 1997). For example, Bradshaw (1995) found
that of the households in his study sample that drive both ways to school, 73 percent
have two or more cars, in contrast to only 60 percent of the larger sample population.
Consistent with that finding, Sadler (1972) found that children from families of
professional social classes are more likely to be driven than those from unskilled
social classes. In contrast with studies focused on environmental barriers to active
travel, these studies suggest that parents are homogeneous in their desire to drive, and
that some walk only because they lack access to vehicles.
Genders of Children and Drivers
Several studies consider how gender influences travel mode for non-work
travel generally and for the school commute in particular (Boamet & Greenwald,
2000; Cervero & Kockelman, 1997; Cooper, Page et al., 2003; McDonald, 2008d;
McMillan, 2005). For example, Boamet and Sarmiento (1998) found that women
generally make more non-work trips than men. This finding is consistent with that of
63


Cervero and Radisch (1996), who found that women bear a greater share of the
responsibility for childcare and other domestic chores, and consequently more often
require the use of a car. The additional responsibility for household errands may
also contribute to trip-linking behavior.
Research also indicates that gender is significant to childrens travel mode for
school trips. In a study of childrens home range, Spilsbury (2005) found that for
10-11 year olds, perceptions of neighborhood violence resulted in more restricted
movement for girls than for boys. McDonald (2007) found that over the past several
decades, boys consistently walked to school more than girls, but the decline in active
travel has affected both genders equally. McMillan (2006a) adds that girls are 40
percent less likely to use active travel than boys.
Cooper et al. (2003) found that for boys, active travel to school is associated
with higher rates of overall physical activity patterns. That finding suggests that
active travel explains healthier lifestyles, but the association may also indicate the
reverse. Boys, who may be more active generally, may also be more likely to choose
an active travel mode for the school commute if given the opportunity.
Age and Constructions of Childhood
Modem constructions of childhood in the United States and in other western
contexts contribute to and are perpetuated by age segregation and other restrictions to
independent mobility that appear in the design of spaces and in public policy (Aries,
1962; Holloway & Valentine, 2000; Simpson, 1997). In large part, modem planning
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has considered young people in only two contexts. First, the needs of young people
have necessarily been the focus of child-specific projects such as skate parks,
playgrounds, and school settings (Eccles & Gootman, 2002; Freeman & Aitken-Rose,
2005; Mitra, 2001; Simpson, 1997; Ward Thompson, 1995; Young, 1990). Second,
young people are considered in the design and management of places where their
presence or characteristic behaviors are unwelcome3. This reinforces socio-spatial
divisions between young people and adults and draws attention to non-conforming
behaviors, such as unsanctioned adult presence on or near school grounds or youth
loitering or demonstrating in urban spaces during school-time.
Youth advocates argue that contemporary western trends of social separation
by age are problematic, as reflected in the common use of terms such as
discrimination, segregation, and restrictions, evoking images of oppression.
Indeed, social constructions of young people in the U.S. and external manifestations
of those constructions impact the quality of life for individual young people and
restrict independent mobility. For example, in many urban neighborhoods, childrens
access to natural areas is severely limited, and in some cases children are not allowed
to play outside at all for fear of abduction, traffic accident or other harm (Karsten &
vanVliet, 2006; Louv, 2006).
3 While examples of this type of exclusion or behavioral restriction are plentiful and
commonplace, I would argue that this includes most spaces that are not child-specific
and even those designed for a specific age subset of children or youth.
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Research identifies age as a significant correlate to travel mode for non-work
trips generally and for the journey to school in particular (Boamet, M. & Sarmiento,
1998; Cervero & Radisch, 1996; Greenwald & Boamet, 2001). Several studies find
differences in both the mode of travel and the likelihood of parent accompaniment
based on the age or grade of the student. For example, Joshi and McLean (1995)
found that parents are more concerned about the safety of younger children due to
age-related levels of competence and pedestrian ability. Some argue that children
have less developed perceptions of time and space, which can make it more difficult
to judge safe distances of oncoming vehicles (Administration, 2008; CDCP, 2007). In
addition, because they tend to be smaller, younger children are more difficult for
drivers to see (FHA, 2008). Geographers note that streetscapes and other public
spaces tend to be designed for able-bodied adults, which necessarily limits their
functionality and access for children (Collins & Kearns, 2001; Matthews & Limb,
1999).
Activity-based research begins to consider socio-demographic factors in
combinations that may influence travel behavior (Handy, 1996). For example,
Hillman et al. (1990) as well as Joshi and McLean (1995) combined gender, age and
numbers of children to differentiate life-stages and household roles. Both studies
found special significance in specific household roles, such as homemaker, that
transcend typical socio-demographic variables. Still, the improved categories assume
that people of similar socio-demographic categories respond to external stimuli by
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choosing the same travel mode. That assumption does not account for attitudinal
diversity.
Psychological
Exploratory research identifies several psychological factors associated with
driving including perceptions of convenience, autonomy and status (Bradshaw, 1995;
Reser, 1980). Those psychological influences receive less attention in research and
policy than physical environmental conditions and socio-demographic characteristics.
However, as rising costs of ownership and operation of private vehicles do not seem
to deter their use, some scholars suggest that an attitudinal approach is indicated.
Reser (1980) states:
The seeming insensitivity to costs and inconvenience would suggest
that the private car is serving other than utilitarian needs.... There is
substantial intuitive and theoretical justification for saying that a
social change effort of the nature suggested ... necessitates an
accurate assessment of the functions presently being served by the
complex of attitudes, values and behaviors related to the car (1980,
p. 281).
Researchers find that parents associate private automobile travel with
convenience, a welcome feeling of control and autonomy, as well as personal identity
(Black, Collins et al., 2001; Reser, 1980; Sorkin, 1992). Whereas researchers may
interpret opportunity-related factors as obstacles preventing parents from choosing
the more desirable, active modes of travel, these findings suggest that parents may
have differing value systems that underlie their inclination to walk. Behavioral
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intervention must take these value-systems into consideration if it is to influence
individuals to walk rather than drive to school.
Convenience
Several studies find that parents choose to drive because it is more convenient
than walking (Collins & Kearns, 2001; Reser, 1980). Activity-based studies indicate
that household scheduling considerations play a key role in travel mode decisions
(Bradshaw, 1995; Collins & Kearns, 2001; Crane, 1996; Jones, Dix et al., 1983). For
example, a number of studies suggest that dual-income households are less likely to
use active travel for the childrens commute to school because parents have to
negotiate the timing of school trips with their work commute (McDonald, 2008d).
McDonald (2008d) found an increase in the numbers of mothers working outside the
home in recent decades, as well as an increase in parental accompaniment. Since
working mothers take a greater share of responsibility for childcare and domestic
chores, they are more likely to link trips for the sake of convenience (Bradshaw,
1995; McDonald, 2008d).
Bradshaw (1995) found that nearly 60 percent of car drivers in her sample
continue to their place of employment after dropping the children off at school. The
strong association between commutes to school and to work may be explained by
timing as well as time spent. Even parents who only have a single trip to make in
addition to the school drop off may choose to drive if the timing of the two events
overlaps. The desire to save time reflects increasingly busy schedules as opposed to
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laziness or relaxed attitudes. Hupkes (1982) describes a law of constant travel,
which suggests that people invest a fixed proportion of their time for travel (Frank,
Sallis et al., 2006; Hupkes, 1982). The convenience of private automobile
transportation allows individuals and families to go farther and to accomplish more
while maintaining set proportions of travel time.
Control and Autonomy
Some studies describe private automobile travel as a coping mechanism that
allows drivers to establish a sense of control in an uncertain environment (Breznitz,
1980; Reser, 1980). The reference to uncertainty is consistent with discussions of
postmodemity, which suggest that rapid change drives individuals to seek identity
and rootedness in commodities (Harvey, 1989; Sorkin, 1992). Private transportation
promises control over path and schedule, which are otherwise often dictated by
external constraints, as well as control over immediate physical and social
environments (Breznitz, 1980; Reser, 1980). For some, the perceived freedom of
personal choice associated with private transportation may appear greater than the
diversity of choice available through a variety of alternative travel modes. Ironically,
the perceived sense of freedom associated with private travel often relegates
caregivers to the role of chauffeur for dependent children and limits the independent
mobility of each (McDonald, 2008d).
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Identity and Status
Mode of travel establishes and to reinforces personal identity and social status
for drivers and passengers (Reser, 1980; Sorkin, 1992). Some studies associate the
private automobile with symbolic expressions of wealth, status, sexuality and power
(Reser, 1980). For example, popular media, particularly advertisements, associate
sports cars with desirable body images (Packard, 1957). Reser (1980) notes that
research subjects do not always acknowledge identity-related influences in their travel
decisions, because they work subconsciously.
Similarly, researchers hypothesize that peer pressure and societal norms
influence parents travel mode for the school commute (Black, Collins et al., 2001;
Evenson, Motl et al., 2007). Studies show that the school or community climate
regarding travel behavior influences parents to walk or drive. For example, Evenson
et al. (2007) found that perceptions of encouragement, praise and importance placed
on active travel influences parents to walk. The perception that driving to school is
normal, expected or desired may influence parents travel mode decisions despite
other practical considerations.
Some studies find that parents drive to school to indicate high-quality
parenting. Sanger argues that in highly suburbanized Western cities, driving
provide(s) evidence of good parenting and mileage the measure of maternal
contribution to familial welfare (Sanger, 1995, p. 719). Soloman (1993) similarly
argues that contemporary, highly attentive parenting styles influence travel mode
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decisions. Researchers often interpret the need for accompaniment and automobile
travel as a reflection of perceived safety hazards on the journey to school, including
traffic danger and stranger danger (Hillman, 1995; Hillman, Adams et al., 1990; Joshi
& MacLean, 1995). However, they may also reflect behavioral norms associated with
identity, status and societal pressure to conform.
Childrens preferences also influence parents travel mode decisions for the
school commute. Their influence reflects parenting styles that emphasize attention,
accommodation and participatory decision-making. Some studies describe reciprocal
relationships between travel behavior and childrens travel preferences. For example,
Collins and Kearns (2001) found that preference for walking correlates with walking
behavior, and preference for inactivity correlates to inactive behavior. They interpret
the finding to mean that active travel can condition children to be active adults.
However, it may also indicate self-selection. Children who will grow to be active
adults choose to be active in their youth.
Environmental Determinism
Although active travel research identifies opportunity-related (environmental)
factors and propensity-related (socio-demographic and psychological) factors
associated with driving children to school, the research largely emphasizes
environmental factors, assuming that parents desire to walk, but are prevented by
external obstacles. Socio-demographic studies seem to suggest the opposite that
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parents who have the opportunity to drive will do so because they can. Ultimately,
either assumption suggests that behaviors closely follow environmental opportunities,
which reflects the environmental determinism of the architecture and planning
professions (see Chapter Two).
Summary
Fewer children walk to school than did even a few decades ago (McDonald,
2007; USDOT, 1969-2001). In response to the decline, active travel programs aim to
encourage students to walk more often (Hubsmith, 2006). Research can support
active travel intervention by finding out why parents drive children to school.
Research about travel mode for school trips identifies opportunity-related
(environmental), and propensity-related (socio-demographic and psychological)
factors associated with driving. However, studies that begin with open-ended
questions often proceed to distill findings so that they conclude with only the most
significant factors, which tend to be environmental. As a result, the recommendations
neglect other, also significant propensity-related factors.
Active travel research tends to interpret statistical significance of certain
factors to mean worthy of policy attention. That interpretation appropriately
identifies trends such as the recent decrease in active travel that policy needs to
address (McDonald, 2007). However, as a guide for specific, behavioral intervention,
it favors a majority ruling over more strategically selected target groups. For example,
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researchers point to travel distance and traffic danger as two of the strongest
correlations to private automobile travel and conclude that overcoming those
obstacles would significantly increase rates of active travel (Bradshaw, 1995; Collins
& Kearns, 2001; DiGuiseppi, 1998; Krizek, 2003; McDonald, 2007; CDCP, 2005;
Schlossberg, Greene et al., 2006). However, by focusing on those families for whom
distance and major crossings interfere with active travel, the policy misses an
important target market. For example, it overlooks the margin of families who live
close to schools and do not cross major road along the way, but choose to drive for
other reasons. This is an important focus of my research that I address by examining
the relationship between parents opportunity and propensity to use active travel for
school trips.
Quantitative travel mode research emphasizes environmental obstacles that
prevent families from having the opportunity to walk. Although exploratory studies
tend to find both social and physical environmental characteristics associated with
mode choice, research and policy emphasize changes to the physical environment to
encourage walking. Proponents of new urbanism claim that characteristics of urban
form promote pedestrian activity. Several researchers have conducted evaluations of
Safe Routes to School infrastructure projects to determine whether they increased
rates of active travel. Although they found some successes, research on those claims
remains inconclusive due to confounding factors. By emphasizing environmental
factors, the research assumes that parents share a propensity to walk their children to
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school, but lack the opportunity. That assumption disregards attitudinal diversity that
could explain parents travel mode choices.
Some researchers argue that interventions affect people differently and that it
is important to find out why (Bemetti, Longo et al., 2008). However, even research
that focuses on socio-demographic characteristics emphasizes the opportunity to
walk. As researchers find differences in travel behavior between ethnic groups, for
example, they often interpret the findings through the lens of social justice by
suggesting that vulnerable populations experience environmental obstacles more than
others. Those findings do little to explain how personal characteristics affect mode
choices, instead deferring to environmental factors.
Psychological factors receive much less attention in research than
environmental and socio-demographic factors. However, qualitative studies identify
several factors relating to parents inclination to drive children to school, including
convenience, feelings of control and social identity. Although some researchers argue
that planners ought to focus solely on environmental intervention, psychological
factors influencing behavior can render environmental planning efforts ineffectual.
My research examines parents attitudes about the school commute,
emphasizing the interaction between parents and propensity to walk to school. In the
next chapter, I describe my research methods in detail, explaining how an attitude-
based approach can improve intervention and encourage active travel for school trips.
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CHAPTER 4:
QUALITATIVE METHODS
Introduction
Over the past century, and particularly after World War II, cities in the United
States have become increasingly automobile-centered (Newman & Kenworthy,
1999). More recently, the trend towards private automobile travel has extended to
include childrens trips to and from school. Data from National Personal Travel
Surveys indicate that in the United States, the proportion of students walking to
school decreased from approximately 49% in 1969 to 15% in 2001 (USDOT, 1969-
2001).
National health authorities, including the United States Department of Health
and Human Services, recognize health problems associated with inactivity, including
childhood obesity, heart disease, diabetes and asthma. In response, they have directed
substantial public resources to increase the numbers of students walking or biking to
school (USDHHS, 2000). An example is Safe Routes to School (SR2S), a federal-aid
program of the United States Department of Transportation's Federal Highway
Administration, created by Section 1404 of the Safe, Accountable, Flexible, Efficient
Transportation Equity Act: A Legacy for Users (SAFETEA-LU). The legislation
allocated $612 million in federal funds over five fiscal years, 2005-2009, to address
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issues such as traffic danger and stranger danger associated with private automobile-
use for the journey to school (Hubsmith, 2006).
For active travel programs like SR2S to increase the numbers of active school
trips, facilitators need to know why parents choose to walk or drive. As I described in
Chapter Three, planning research examines parents perceptions of barriers to active
school travel and finds a combination of environmental, socio-demographic and
psychological factors that prevent them from walking or biking their children to
school (Ahlport, Linnan et al., 2007; Collins & Kearns, 2001; Hillman, Adams et al.,
1990; Joshi & MacLean, 1995; McMillan, 2006b). Quantitative studies measure
which factors influence parents travel mode choices to determine the most
significant barriers to active travel (McMillan, 2005; McMillan, 2006b; Schlossberg,
Greene et al., 2006).
By focusing on factors that prevent active travel, extant research implies that
parents are a homogeneous population and that they desire to walk or bike their
children to school but simply have not had the chance. Active travel programs will be
more effective if they aim intervention at target populations who can walk or bike but
who nevertheless choose to drive. Therefore, it is necessary to examine program
facilitators as well as parents perceptions and experience of the daily school
commute.
In this chapter, I describe the research methods that I used to investigate
perceptions of trips to and from elementary school in Denver Colorado. I begin by
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describing my overall research design, which includes two phases, and the research
setting, which includes three SR2S programs and twelve participating schools. Then,
focusing on the first, qualitative research phase, I describe my methods for contacting
elementary school students parents at the twelve schools and I discuss the resulting
sample of interview respondents. Finally, I present my qualitative data collection and
analysis techniques.
Data from this phase of research include transcripts from the three SR2S
programs planning meetings and events and from a series of voice-recorded, semi-
structured interviews with parents. Results from this qualitative study are presented in
Chapter Five. They also contribute to the design of a sorting exercise used in the
quantitative study described in Chapters Six and Seven.
Research Design
My mixed-methods research design included two phases that spanned three
semesters (see table 4-1). I conducted the first, qualitative phase of my research
during the 2007-2008 school year. The second, quantitative phase included a pilot
sorting exercise that I administered during the spring semester 2008, and a main
sorting exercise that I administered during the fall semester 2008. Both the qualitative
data collection and the pilot sorting exercise occurred while SR2S programs were in
progress at the respective schools. The main sorting exercise occurred in the semester
following the schools completion of the SR2S programs.
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Table 4-1: Data collection timeline
Phase Activity Schools Timing
1 Qualitative Gaining Entry Interviews Observations 12 schools Fall 2007 Spring 2008
2- Quantitative Pilot Sorting Exercise 4 schools Spring 2008
2- Quantitative Main Sorting Exercise 7 schools Fall 2008
As I explained in Chapter One, the purpose of my research was to bridge a
gap between traditional planning research regarding travel mode choices and
intervention that aims to influence travel behavior. Specifically, it aimed to
characterize parents attitudes cross-sectionally rather than to measure the influence
of intervention longitudinally. However, it was necessary to select schools that were
participating in SR2S projects so that I could include facilitators perspectives of
parents commuting experience. To that end, I conducted the qualitative phase of my
research with a sample of parents from the schools while their SR2S programs were
in progress, and the quantitative phase when the programs were recently completed.
In the next section, I describe the research setting and specific research sites,
including thirteen schools that participated in SR2S non-infrastructure programs.
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Research Setting
As a magnet city for outdoor enthusiasts, Denver Colorado is an appropriate
location to study attitudes toward active travel for school trips. Colorados front range
is widely known for its outdoor recreation amenities and the city attracts significant
numbers of tourists and full time residents for that reason. Denvers climate is
generally mild, with low relative humidity, distinct seasons and abundant sunshine
year round. During the coldest months (December and January) temperatures range
from 15-45 degrees Fahrenheit. Low extremes are unusual during the daytime,
however, and temperatures are often well above freezing throughout the school year
(September May). Denver is located in the western high plains, a high plateau that
rises gradually to the foothills of the Rocky Mountains. The topography includes low
rolling hills that increase in intensity west of the city. In short, the climate and
topography naturally accommodate active travel. Despite those qualities, the
percentages of students walking or biking to schools are similar to national levels and
have experienced a steady decline in the past four decades.
Denver is taking a leadership role in school-based active travel intervention,
which also makes it an ideal site for studying attitudes toward active travel for school
trips. Political leaders have set progressive environmental goals for the city, including
efforts to decrease automobile traffic and harmful greenhouse gas emissions (City and
County of Denver, 2006). To that end, Denvers City Council signed Proclamation 15
in March 2007, creating a Safe Routes to School Coalition to develop a short- and
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long-term action plan and to support Safe Routes to School programs throughout the
school district by 2011 (City and County of Denver, 2007). As a leader in these areas,
Denver provides a rich environment for studying attitudes about active travel, and
programs that aim to increase walking and biking to school.
Research Sites and SR2S Program Affiliation
I selected thirteen public elementary schools as research sites based on their
participation in Denvers Safe Routes to School (SR2S) non-infrastructure programs
during the 2007-2008 school year. I focused on non-infrastructure programs for two
main reasons that are described below: (1) so that my study sites would include
diverse socio-economic and racial groups, and (2) so that the programs would address
the widest possible range of attitudes about school trips.
First, my purpose was to study parents mode choices for the school commute,
which planners theorize are influenced by a combination of environmental, socio-
demographic and psychological factors that impact the opportunity and propensity to
choose active travel (Ahlport, Linnan et al., 2007; Collins & Kearns, 2001; Hillman,
Adams et al., 1990; Joshi & MacLean, 1995; McMillan, 2006b). Although an
educational component is required for SR2S infrastructure projects, the scope of that
requirement is limited and addresses a narrower range of factors that influence
parents travel mode decisions for school trips. In contrast, the scope of the non-
infrastructure grant includes a balance of activities including site assessments,
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surveys with parents and students, educational programs and events with incentives
for students who walk or bike to school. Although individual programs vary in their
specific intervention plan, I was able to observe several programs and find out how
their respective facilitators expected to influence travel behaviors.
Second, I intended to study parents perceptions without narrowing to a
specific demographic group. Because infrastructure projects are often resource
intensive and resources are so limited ($ 1.0 to $ 1.6 million per year statewide),
CDOT allows only larger grant applications ($50,000 to $250,000) coordinated by the
City, and carefully assesses them in the context of existing infrastructure plans
(CDOT, 2009). As a result, only two grants were approved for Denver in 2007 one
focused on bicycling improvements and one focused on pedestrian improvements -
and both were awarded to the same facilitator. Although the grants totaled
approximately $220,000, the projects would impact only two schools (Swansea and
Ashley Elementary Schools), both of which have predominantly Latino enrollments
and over 80% of students on the free/reduced lunch program.
In contrast, non-infrastructure projects (for education and encouragement)
require fewer resources and can be funded more widely across the city in a single
grant year. Statewide funding ranges from approximately $150,000 to $500,000 and
individual grants must be at least $3,500. In 2007, CDOT awarded SR2S non-
infrastructure grants to three Denver public/non-profit teams: Denver Public Schools
with Denver Osteopathic Foundation, Denver Environmental Health with
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Transportation Solutions, and Denver Health with DPS Rides. Although the grants
totaled approximately $130,000, the projects would impact twelve schools (see table
4-1). Please note that I selected a thirteenth school that is only included in the
quantitative study, but the site description is included in this chapter for convenience.
Munroe Elementary completed its SR2S non-infrastructure program in 2007.
Table 4-1: 2007 Denver SR2S programs and participating schools
Teams School %Black %Latino %White % Lunch # Enroll
Denver Edison 3.0 47.3 46.9 42.5 461
Public Schools Force 0.7 89.4 7.5 77.4 585
Denver Sabin 3.5 62.1 26.5 44.3 634
Osteopathic Foundation Slavens 2.2 5.7 89.3 1.5 456
Denver Environ. Health Cory 3.5 12.7 79.1 8.4 369
Bromwell 5.2 3.7 80.7 7.4 326
Transport. Solutions Steck 7.5 9.6 76.7 10.6 292
Philips 83.4 13.0 3.5 84.0 169
And Stapleton TMA Hallett 70.8 27.2 0.4 79.6 250
Smith 50.3 49.0 0.8 86.0 392
Denver Health Lowry 28.8 16.6 52.4 40.9 458
DPS Rides Valdez 1.1 95.4 2.3 87.3 434
University Munroe 1.0 94.6 2.0 74.8 574
Average 20.1 40.5 36.0 49.6 415.4
Source: Piton Foundation School Facts, Data Year 2008, http://www.piton.org/
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The thirteen participating schools represent a wide variety of Denver
neighborhoods, differing in physical design, socio-economic status and other
characteristics. The resulting site selection includes schools with clear majorities of
White, Hispanic and Black enrollment, with a range of income levels (indicated by
percentage of students in the free/reduced lunch program), and widely ranging in
enrollment size (Piton Foundation, 2007). Following are brief descriptions of the
2007-2008 SR2S non-infrastructure programs that I selected and the schools in which
they were administered.
Program 1: Denver Public Schools and Denver Osteopathic Foundation
Denver Public Schools (DPS) partnered with Denver Osteopathic Foundation
(DOF), a non-profit organization, for the first program grant. Although one
representative from each organization (Debbie from DPS and Lisa from DOF)
participated in most of the meetings and activities, DOF took the lead in designing
and carrying out the intervention.
In compliance with grant requirements, Program 1 tallied students travel
modes for one week during each semester, and conducted surveys of parents at the
beginning and end of the school year regarding barriers to active travel. In addition, it
established a School Traffic Safety Committee and assessed site conditions with the
committees assistance. This program emphasized teaching children pedestrian and
bicycle safety skills. It presented bicycle safety to grades 3-5 in the school
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auditoriums, conducted outdoor bike rodeos for a smaller number (no more than 30
per school) of students in grades 3-5, and invited local firemen to teach basic
pedestrian safety in K-2 classrooms. Building on the safety instruction, the program
encouraged students to walk by offering incentives at special events. It conducted
National Walk to School Day in October, and conducted Walking Wednesdays in the
latter part of the spring semester.
Program 1 worked with four schools with above-average enrollment that
varied widely in demographics and physical neighborhood context: Edison, Force,
Sabin and Slavens. In particular, Slaven stands out from the other schools because of
its predominantly White student population and because the school ranges from
kindergarten to 8th grade.
Edison
Edison Elementary (K-5) School is located in northwest Denver and primarily
serves Highland and West Highland neighborhoods, both of which have sizeable
Latino contingencies, low- to mid-range average household incomes, and higher than
average population densities in an urban residential setting (see table 4-2).
Table 4-2: Demographic profile of Edison neighborhoods versus City of Denver
Neighborhood % White % Latino % Black Avg. Inc. Density
Highland 29.4 66.8 1.4 $39,568 6,932
West Highland 64.5 30.9 1.9 $50,110 5,896
Average 46.95 48.85 1.65 $44,839 6,414

Denver 51.9 31.7 10.8 $55,129 3,617
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Full Text

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What Drives Parents? A Case Sensitive Inquiry into Parents' Mode Preferences for the Journey to School by Kelly Draper Zuniga B.S., The Pennsylvania State University, 1993 M.S., University of California, Davis, 2002 A thesis submitted to the University of Colorado Denver in partial fulfillment of the requirements for the degree of Doctor of Philosophy Design and Planning 2010 0 \ \

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I 0 by Kelly Draper Zuniga All rights reserved.

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This thesis for the Doctor ofPhilosophy degree by Draper Zuniga has been approved by Willem van Vliet Fahriye H. Sancar I

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Zuniga, Kelly Draper (Ph.D., Design and Planning) What Drives Parents? A Case Sensitive Inquiry into Parents' Mode Preferences for the Journey to School Thesis directed by Professor Willem van Vliet-ABSTRACT This dissertation uses a case-sensitive approach to examine an active travel intervention's diverse target wpulation. It builds on a series of travel choice models, and draws key conceptual themes from Chapin's (1974) human activity model,. which highlights opportunityre1ated and propensity-related factors associated with behaviors. The research addresses two key issues. First; planning research about active travel emphasizes environmental influences on travcl : mode and.focuses on obstacles to active travel, assuming that parents would walk if they could, : Second; previous reseaooh diminishes variance in travel behavior as it identifies most important factors associated with travel mode. I used case-sensitive mixed methods to investigate parents' perceptions of school travel, and to consider their propensity in addition to their opportunity to walk. I conducted my research in two phases with 12 schools that participated in 2007-2008 SR2S non-infrastructure programs. In phase one, I analyzed 65 short, semi-structured interviews with parents from 12 Denver elementary schools to identify local school commuting issues. In phase two, I used cluster-analysis ofa Qsortactivity completed by 650 parents from seven ofthe schools to identify and characterize attitude..based subgroups of parents. My qualitative study revealed disparity in parents' perceptions of travel related issues, which acknowledged variance in travel behavior, and also indicated variations in parents' opportunity and propensity to walk children to school. More importantly, that portion of the research provided data to further examine attitudinal diversity about active travel in Denver's public elementary schools. My quantitative study found that opportunity and propensity were positively, albeit weakly correlated However, it also found that travel characteristics were not a function of opportunity and propensity alone, as Chapin's (1974) human activity model suggested. Rather, parents that belonged to subgroups with positive attitudes about active travel described shas of travel behavior between typically walk and typically drive. These findings suggest that intervention tailored to address specific issues and motivations could encourage parents to walk more often even ifthey already walk part -ofthe time. This abstract accurately represents the content.oftbe thesis.l, recommencl-its publication. Signed Willem van VlietII

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ACKNOWLEDGMENT I feel profoundly grateful to my advisor, Willem van Vliet--, for his wisdom, patience, and kind leadership in my research. Although his specific guidance and support considerably improved the quality of the dissertation, I appreciate two things he did in particular: First, he let me decide for myself what was good for me and my family, but consistently presented professional opportunities for me to accept or decline; and Second, he did not tell me how to do a dissertation up front, but let me grow into my academic self at a natural pace. I am a better person because of it. I wish to thank all the members of my committee for their valuable participation and insight into this project. I selected each member of this eclectic group for a unique attribute that I appreciated independentlyFahriye for her notoriously high standards that scared me just a little and for reminding me that the doctorate is not just about getting a job; Pamela for her friendship, creativity, and constructive insight; Ben for reminding me to see the positive side of things I critique; and Susan for her case sensitive methodological expertise, but even more because she was easy to talk to. It was a pleasant surprise to find that the group also worked well together. I would not have been able to complete this task if not for the ongoing support of colleagues in the department and the dissertation support group. I appreciate them for reviewing articles, listening to rehearsals, holding me accountable in my endless dissertation 'to do' list and for sharing their research with me. I am grateful to my family. By their open mindedness, my parents raised me to love learning new things. By his example, my brother taught me to embrace adventure and challenge. Through decades of faithful correspondence, my grandpa Jack prepared me to love writing. Those lessons provided a firm foundation for this work. And finally, I express my love and appreciation to my sweet husband Miguel and to my beautiful daughter Haley. Thanks, Miguel, for taking late night baby duty so that I could think clearly during the day and for your generous support over the years. Thanks to Haley, who came into my life mid-way through the dissertation. I learn more from watching her figure things out than I have in my decades of schooling. These two will make the ends of the earth feel like home.

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TABLE OF CONTENTS Figures .......................................................................................... xii Tables .......................................................................................... xiv Images ........................................................................................... xvi CHAPTER 1: A Case-Sensitive Approach to the Question 'What Drives Parents?' ....................... 1 Introduction ............................................................................................................... 1 Advocates for Active School Travel ......................................................................... 3 Children's Health ................................................................................................... 3 Pedestrian Safety ................................................................................................... 4 Environmental Sustainability ................................................................................ 5 Universal Access-A Shared Political Agenda ..................................................... 8 The Policy Problem: Encouraging Active Travel.. .................................................. 1 0 Two Gaps Between Planning Research and Praxis ................................................. 12 A Case Sensitive Approach ..................................................................................... 14 Preview of Remaining Chapters .............................................................................. 15 2: A Theoretical Framework for Learning Why Parents Really Drive Their Children to School ...................................................................................................................... 18 Introduction ............................................................................................................. 18 Limitation of Correlative Studies ............................................................................ 20 Choice Models ......................................................................................................... 21 VI

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Rational Choice Models ...................................................................................... 23 Alternative Behavioral Theories .......................................................................... 25 Environmental Determinism ........................................... .................................... 31 Summary .................................................................................................................. 33 3: Why Do Parents Drive Children to School? An Interpretive Review of Literature ....................................................... ............................................................. 35 Introduction ............................................................................................................. 35 Why do parents drive children to school? ............................................................... 37 Qualitative Studies ...................................................... ..... .............................. ..... 39 Quantitative Studies ............................................................................................. 40 Distilling Findings to Emphasize Environmental Barriers ................ . ............ ... 43 Specific Variables .................................................................................................... 46 Opportunity-Related Factors ............................................................................... 47 Propensity-Related Factors .................................................................................. 60 Environmental Determinism ............................................................... ................ 71 Summary ........... ............................................................. ................................... .... 72 4: Qualitative Methods ................................................................................................ 75 Introduction .......................... ....... ........................... ................. ............ . ............... 75 Research Design ...................................................................................................... 77 Research Setting ................... .................. ................... ............ ............ ................. 79 Research Sites and SR2S Program Affiliation .................................................... 80 Vll

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Program 1: Denver Public Schools and Denver Osteopathic Foundation ........... 83 Program 2: Denver Environmental Health and Transportation Solutions ......... 93 Program 3: Denver Health and DPS Rides ........................................................ 1 06 Program 4 (Alternate): Children Youth and Environments Center for Research and Design (CYE) ............................................................ 111 Data Collection ...................................................................................................... 114 Gaining EntryParticipation in Programs ........................................................ 114 Recruitment Procedures ..................................................................................... 115 Analysis and Interpretation .................................................................................... 122 Summary ................................................................................................................ 125 5: Qualitative Findings .............................................................................................. 129 Introduction ........................................................................................................... 129 Major Themes ........................................................................................................ 130 Theme 1 : Life Pace ............................................................................................ 131 Theme 2: Nurture ............................................................................................... 141 Theme 3: Context .............................................................................................. 153 Mapping Attitudes Using Opportunity-Propensity Measures ............................... 164 Summary ................................................................................................................ 167 Chapter 6: Quantitative Methods ............................................................................... 169 Introduction ........................................................................................................... 169 Second Phase Research Design ............................................................................. 171 Vlll

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Research Settings ................................................................................................... 173 Pilot Sorting Exercise Locations ....................................................................... 173 Main Sorting Exercise Locations ...................................................................... 17 5 Data Collection ...................................................................................................... 176 Research Packets ............................................................................................... 176 Revised Research Packets-for Main Sorting Exercise ..................................... 179 Recruitment Procedures ..................................................................................... 193 Data Analysis and Interpretation ........................................................................... 208 Similarity Between Cases-the Agglomeration Schedule ................................ 209 Core Perspectives .............................................................................................. 21 0 Characterizing Core Perspectives-Ideal Types ............................................... 215 Identifying Representative Cases Best Specimens ......................................... 218 Summary ................................................................................................................ 221 7: Quantitative Findings ............................................................................................ 224 Introduction ........................................................................................................... 224 Characteristics of Cluster Analysis Cases ............................................................. 226 Range ofPerspectives ............................................................................................ 227 Opportunity and Propensity (Top Right Quadrant) ........................................... 230 Propensity Only (Top Left Quadrant) ............................................................... 235 Opportunity Only (Bottom Right Quadrant) ..................................................... 239 Neither Opportunity Nor Propensity (Bottom Left Quadrant) .......................... 240 IX

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Similarities Between Core Perspectives ................................................................ 242 Low Correlation .................................... ... .............................. ... ......................... 244 Moderate Correlation ................................... ....... ............... ............................... 245 High Correlation ................................................................................................ 24 7 Overall Similarities ................................................................................................ 248 Preference for Physical Activity ........................................................................ 248 Motivations for Travel Mode Choices ............................................................... 249 Social Conditions and Companionship .............................................................. 251 Contextual Conditions .............................................................. .... ..................... 252 General DisagreementsTime .......................................................................... 253 Summary ... ............................................................................................................. 254 8: Answering the Question 'What Drives Parents?' .................................................. 257 Introduction ....... ............. ............................................................. ..... ..................... 257 Main Contributions ................................................................................................ 257 Implications for Practice .................... .................................... .......................... 259 Implications for Research ............................................ .... ....... .... ....................... 263 Strengthening the Relationship Between Research and Practice ...................... 268 Directions for Further Research .... ..................................... .......... ........................ 271 X

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APPENDIX A. INTERVIEW SUBJECTS BY SCHOOL ............................................. 275 B. LIST OF MEETINGS .................................................................... 278 C. INTERVIEW SCHEDULE .............................................................. 280 D. MAIN STUDY AGGLOMERATION SCHEDULES .............................. 282 E. MAIN STUDY DENDROGRAMS .................................................... 289 F. PILOT STUDY IDEAL TYPES .................................. ...................... 296 G. MAIN STUDY IDEAL TYPES ................... ..................................... 302 H. PILOT STUDY BEST SPECIMENS .................................................. 348 I. MAIN STUDY BEST SPECIMENS ................................................... 350 BIBLIOGRAPHY ............................................................................ 358 XI

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LIST OF FIGURES Figure 2-1: CONCEPTUAL FRAMEWORK OF AN ELEMENTARY -AGED CHILD'S TRAVEL BEHAVIOR .................. . ... . ....... ................... ..... ......... 22 2-2: TWO UTILITY-BASED CONCEPTUAL FRAMEWORKS ..................... 25 2-3: CONCEPTUAL MODEL OF INDIVIDUAL SPATIAL CHOICE .............. 27 2-4: GENERAL MODEL FOR EXPLAINING ACTIVITY PATTERNS .......... .30 4-1: DATA COLLECTION TIMELINE ................................................. 79 5-1: ATTITUDE TYPES BY PROPENSITY AND OPPORTUNITY .............. 166 6-1: PILOT SORTING EXERCISE PACKETENGLISH (NOT TO SCALE) ... 179 6-2: MAIN SORTING EXERCISE PACKETENGLISH (NOT TO SCALE) ... 181 6-3: POSTERENGLISH (NOT TO SCALE) ......................................... 183 6-4: SAMPLE Q-SORT DISTRIBUTION DIAGRAM (NOT TO SCALE) ......... 185 6-5: PILOT Q SET ........................................................................... 188 6-6: MAIN Q SET .............................................................................. 190 6-7: REVISED QUESTIONNAIRE .......................................................... 192 6-8: PILOT STUDY AGGLOMERATION SCHEDULE ............................. 210 6-9: PILOT STUDY DENDROGRAM ................................................... 214 6-10: IDEAL TYPE FOR PILOT CLUSTER A ......................................... 217 7-1: ATTITUDE TYPOLOGY FOR SCHOOL TRAVEL ............................. 229 7-2: CLUSTER ED-A IDEAL TYPES ..................................................... 233 Xll

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7-3: CLUSTER BR-A IDEAL TYPES ........................................................ 234 7-4: CLUSTER SA-B IDEAL TYPES ........................................................ 235 7-5: CLUSTER SA-A IDEAL TYPES ........................................................ 237 7-6: CLUSTER BR-D IDEAL TYPES ........................................................ 238 7-7: CLUSTER CO-B IDEAL TYPES ....................................................... 240 7-8: CLUSTER PH-F IDEAL TYPES .................................................... 242 7-9: MAIN STUDY CLUSTER CORRELATIONS USING PEARSON'S COEFFICIENT ............................................................................... 243 8-1: REVISED CONCEPTUAL MODEL OF MODE CHOICE FOR ELEMENTARY SCHOOL TRAVEL ................................................................................................................... 264 xiii

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LIST OF TABLES Table 3-1: Range ofFactors Associated with Travel Mode .................................... 38 4-1 : 2007 Denver SR2S programs and participating schools . . . . . . . . . . . . . 82 4-2: Demographic profile ofEdison neighborhoods versus City of Denver ........ 84 4-3: Demographic profile of Force neighborhoods versus City of Denver ........... 87 4-4: Demographic profile of Sabin neighborhoods versus City of Denver ....... ... 89 4-5: Demographic profile of Slavens' neighborhoods versus City of Denver ....... 91 4-6: Demographic profile of Cory's neighborhoods versus City ofDenver ........ 95 4-7: Demographic profile of Bromwell's neighborhoods versus City of Denver ... 97 4-8: Demographic profile of Steck's neighborhoods versus City of Denver .. ..... 100 4-9: Demographic profile of Philips' neighborhoods versus City of Denver ........ 1 02 4-10: Demographic profile of Hallett and Smith s neighborhoods versus City of Denver ......... ...... . ........ ...... . ..... ......... .. ... .. ... .. .. .. ... .. .... 104 4-11: Demographic profile of Lowry's neighborhoods versus City of Denver ...... 1 07 4-12: Demographic profile ofHallett's neighborhoods versus City ofDenver ... ... 110 4-13: Demographic profile of Munroe's neighborhoods versus City of Denver .... 112 4-14 : Proportions of interview subjects at each school by gender and race ........ 117 4-15: Hierarchy of coding themes ......................................................... 126 6-1: 2008 school enrollment by race/ethnicity and free/reduced lunch program .. 17 4 6-2: Pilot study response rates ... .. . .................. .. . . .. . ....................... 194 XIV

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6-3: Main study response rates ............................................................ 195 6-4: Response rates by race/ethnicity ..................................................... 198 6-5: Pilot Q sample demographic characteristics ....................................... 201 6-6: Main study Q sample filters ......................................................... 202 6-7: Main study Q sample characteristics ................................................ 206 6-8: Core perspectives by site and combination distance .............................. 214 6-9: Pilot study best specimens ........................................................... 220 7-1 : Travel characteristics of clusters in top right quadrant ........................... 231 7-2: Travel characteristics of clusters in top left quadrant ............................. 236 7-3: Travel characteristics of single cluster in bottom right quadrant ............... 239 7-4: Travel characteristics of clusters in bottom left quadrant ........................ 241 XV

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LIST OF IMAGES Image 4-1: Edison elementary school in neighborhood context ............... ... . ........ ... 86 4-2: Force elementary school in neighborhood context ................................. 88 4-3: Sabin elementary school in neighborhood context ..................... .. . ........ 90 4-4: Slavens elementary school in neighborhood context . ........................... 93 4-5: Cory elementary school in neighborhood context . ... ............. . ... .. ...... 96 4-6: Bromwell elementary school in neighborhood context .............. .............. 99 4-7: Steck elementary school in neighborhood context ...... ..... .. .. ....... ....... I 0 I 4-8: Philips elementary school in neighborhood context .. . ........................... I 03 4-9: Hallett and Smith elementary schools in neighborhood context .. . .. .. ... ... 1 06 4-10: Lowry elementary school in neighborhood context .. . .......... .............. I 09 4-11 : Valdez elementary school in neighborhood context .................. .......... Ill 4-12: Munroe elementary school in neighborhood context ........................... 114 XVI

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CHAPTER I: A CASE-SENSITIVE APPROACH TO THE QUESTION 'WHAT DRIVES PARENTS?' Introduction Over the course of the last century, and particularly after World War II, western societies have become increasingly automobile-centered (Newman & Kenworthy, 1999). More recently, the trend towards private automobile travel has extended to include children's trips to and from school. For example, national travel surveys in the United States and Britain revealed a marked decrease in the proportion of students age 5-11 walking or biking to and from elementary school over the past four decades (McDonald, 2007; Pooley, 2004; Centers for Disease Control and Prevention [CDCP], 2005; U.S. Department of Transportation [US DOT], 1969-2001 ) Data indicate that in the United States the proportion decreased from approximately 49% in 1969 to approximately15% in 2001 (McDonald, 2007; CDCP, 2005; USDOT, 1969-2001). I begin this chapter by describing four reasons that policy-makers should be concerned about mode choice for school travel: to protect children's health, to increase pedestrian safety, to achieve environmental sustainability and to ensure

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universal access (see section 1.2). Each of those reasons supports the health, safety, and welfare mission espoused by the planning profession's codes (American Institute of Certified Planners [AICP], 2005). The legitimacy of planning intervening in school travel behavior is worthy of careful examination. However, in my dissertation, I accept the premise that intervention is justified, and focus my attention on the policy objective: to increase the proportion of active school trips. Later in this chapter, I introduce two federally funded programs that encourage active school travel -Healthy People 2010 and Safe Routes to School and note two reasons that traditional planning research regarding school travel is ill equipped to direct this type of intervention, reinforcing the gap between planning research and praxis. The purpose of my dissertation is to bridge a gap between traditional planning research regarding travel mode choices and intervention that aims to influence travel behavior. Rather than identifying and generalizing factors that uniformly explain the decrease in active travel, my research explains how parents' experiences of those factors vary. I examine parents' attitudes about school travel using a combination of qualitative and quantitative methods in a case-sensitive research design. In contrast with traditional research methods, case-sensitive research magnifies differences in research subjects' orientations towards an issue in order to improve communication of an idea or policy objective. In this case, it allows planners to direct active travel intervention to more effectively influence parents' mode choices for school trips. 2

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Advocates for Active School Travel An informal coalition of active travel advocates focuses active travel policy discourse on children's health, public safety, environmental sustainability, and universal accessibility. Popular media presents the same issues to the public and makes them a part of the views that influence parents' travel decisions. Although policy-makers may agree with the motivations for an active travel campaign, they are likely to continue to approve funding only for programs that promise and produce a substantial increase in rates of active travel. Children's Health Advocacy for children's health has been at the forefront of the policy agenda for active travel to school. Rates of overweight and obese children have increased by nearly 300% in the past decade (McDonald, 2007; Ogden, Carool, & Curtin, 2006), and health problems that have traditionally occurred in adults, including cardio pulmonary disease, diabetes, asthma, depression and anxiety are becoming more common in children as well (Frank, 2001; Kegerreis, 1993; CDCP, 2005; U.S. Department of Health and Human Services [USDHHS], 1996). Since cars and busses gather along streets to wait for children during pick up time, the concentration of fumes from idling engines also presents an health risk in the immediate vicinity of the schools (Environmental Protection Agency [EPA], 2002). In addition to its impact on the quality of life for children and their families, childhood disease is an additional 3

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burden on government healthcare programs that are already experiencing financial pressures. The recent decrease in active travel among children is symptomatic of a general decline in physical activity in the United States (USDHHS, 1996, 2000). Some researchers claim that active commuting is a critical, but overlooked source of children's daily physical activity (Tudor-Locke, Ainsworth, & Popkin, 2001). According to various studies, omission of the active trip to school significantly decreases children's achievement of health-related activity guidelines, limits academic achievement, contributes to anxiety and depression, and establishes lifelong patterns of inactivity (Dwyer, Sallis, Blizzard, Lazarus, & Dean, 2001; James, 1995; Kegerreis, 1993; Shephard, 1997; Tudor-Locke, Ainsworth, & Popkin, 2001). Pedestrian Safety The decrease in active travel is also responsible for a higher proportion of traffic injuries for pedestrians and bicyclists (Killingsworth & Lamming, 2001; McMillan, 2005). Studies find that as fewer children walk and bike to school, neighborhood streets become more dangerous for the remaining pedestrians who are more difficult for drivers to see (Jacobsen, 2003). In addition, transportation funding is typically apportioned based on counts of vehicles versus pedestrians passing a designated point in the road. As the ratio of vehicular to pedestrian activity increases, new funding reinforces the dominance of automobile traffic (Gotthelf, 2007). As a 4

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result, active travel advocates rely heavily on public perceptions and grassroots activism to counter an auto-centric establishment. Active travel advocates emphasize children's health in order to encourage action at the policy level and to influence views that shape decisions about the commute to school. Although much of the rhetoric is based on empirical research, discussion ofhealth issues bear influence even when they lack empirical support. For example, a disputed medical report from 2002 suggested that if the obesity epidemic were not controlled, life expectancy for the upcoming generation would be lower than the one preceding it (Center for Consumer Freedom [CCF], 2005; Cable News Network [CNN], 2007). The Center for Consumer Freedom describes the rapid spread of the exaggerated statement as it has "entered the lexicon of obesity scaremongers, making its way into countless articles, editorials, and even Congressional testimony all without so much as a shred of credible research to back it up" (CCF, 2005). Other sources suggest that life expectancy in the United States is still climbing, but that it lags behind at least 30 other countries (CNN, 2007). As indicators of overall national wellbeing, health-related issues carry significant political power. Environmental Sustainability A second key concern, environmental sustainability, similarly motivates the policy agenda for active travel on the commute to school. Automobile ownership has 5

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increased exponentially in the past half century and has been a subject of great concern due to its damaging impact to the environment (Black, Collins & Snell, 2001 ). Researchers estimate that vehicle ownership has increased by an average of approximately 3.69 million passenger vehicles per year since 1960 (Federal Highway Administration [FHA], 2006). Grassroots activists and transportation planners share concern for the impact of private automobile travel on the environment. Increased use of private automobiles is associated with faster depletion of petroleum and other non-renewable resources and increase of air, water and noise pollution. Research is abundant regarding ways to address these problems and includes efforts to lessen environmental impacts by improving transportation technology, as well as efforts to lessen environmental impacts by reducing rates of private automobile travel. The trip to elementary school poses particular concern for environmentalists because per distance traveled, very short trips have a disproportionately high negative environmental impact (Black, Collins, & Snell, 2001; Goodwin, 1995). Goodwin notes that very short trips, especially those within residential neighborhoods, do not provide sufficient time for cars to warm up effectively and include more frequent stops, which result in decreased fuel efficiency (Goodwin, 1995). Most importantly, short car trips are far more likely to be replaced with active modes of travel than are long trips, so it is reasonable to focus policy efforts in this area. 6

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As private automobile transportation becomes more dominant, the layouts of cities and towns evolve to accommodate it. In the book, "Crabgrass Frontier", Kenneth Jackson chronicles the rise of suburbanization and the corresponding deterioration of the pedestrian domain in the United States. He points out that development patterns "are a function of the interrelationship of technology, cultural norms, population pressure, land values, and social relationships" (Jackson, 1985, p.3). He suggests that over the past century, those systems have combined to make private automobile-use increasingly attractive, convenient, accessible, and perhaps inevitable. His description also portrays development patterns as somewhat reactionary as opposed to being the catalyst of change. That attitude differentiates his argument from advocates of new urbanism who generally expect certain changes in urban design to affect an increase of pedestrian activity (Newman & Kenworthy, 1999). Automobile-centered development also includes more impervious paved surfaces and less vegetated open-space, which impacts both environmental and social systems. At the city level, accommodation of automobiles contributes to the urban heat island effect and increased storm runoff (Oke, 1982). Automobile-centered development can also be problematic for the economic sustainability of the city and region. Since the heyday of urban renewal, planners have increasingly argued that automobile centered urban design diminishes the vitality of the streetscape (Duany, 2000; Jacobs, 1961; Kunstler, 1993). Neighborhoods and commercial districts that 7

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lack a pedestrian presence are prone to criminal activity and economic decline. Environmental and social systems are intimately and inextricably connected, and both potentially threatened by the dominance of private automobile use Universal Access -A Shared Political Agenda Quality of life issues for children, including independent mobility and universal accessibility, may not carry the same political weight as children's health or environmental sustainability in the policy discussion of active travel. In part, the lower priority of these issues reflects the ambivalence of the adult public, including policy makers, regarding children's human rights (Bessant, 2003, 2004). Since the mid-201 h century in the United States, childhood has generally been viewed as a period of preparation, during which the child is under the supervision and protection of parents and other guardians, and is generally excluded from the public realm where productive and commercial activities take place (Cahill, 1990; Mintz, 2004; Simpson, 1997). However, in many ways, children's quality of life parallels the quality of life experienced by adults, particularly caregivers and seniors, and will become more prominent as seniors become the demographic majority and gain political strength in the coming decades. Seniors and People with Disabilities Researchers identify independent mobility and access to transport as one of three key indicators of quality of life for seniors and for people with physical 8

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disabilities (Banister & Bowling, 2004; Meyers, Anderson, Miller, Shipp, & Hoenig, 2002; Stumbo, Martin, & Hedrick, 2009). This parallels discussions of independent mobility regarding children and youth, which highlight access to and quality of children's physical environments (L Chawla, 1998; Louise Chawla, 2001; Holloway & Valentine, 2000; Karsten & van Vliet 2006; Wridt, 2004). In theory, pedestrian orientation in urban design accommodates children, youth, seniors and people with disabilities even if a child-friendly, senior-friendly, or handicap-accessible focus is not the intention. Automobile travel has often been described as a key to independence (Hillman, Adams, & Whitelegg, 1990). However, since operation of motorized vehicles is restricted, pedestrian-orientation or multi-modal planning enables access to resources regardless of age or ability. For example, "Home Zones" in the U.K. as well as "Woonerven" in the Netherlands use physical design elements to significantly limit traffic speeds in residential neighborhoods, making the streets safe and accessible for pedestrians at any life stage (Gill, 1997, 2006). Caregivers Just as the layouts of cities and towns change to favor automobile travel over pedestrian travel, the lifestyles of drivers change to reinforce the dependence of non drivers. In this sense, the independent mobility of children and seniors is linked to the quality of life experienced by caregivers. Over the past several decades, rates of parental escorting have increased for children's trips to school (McDonald, 2008d). 9

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Solomon argues that the increase in escorting reflects changes in popular theories of parenting, which emphasize attention over independence (Solomon, 1993). However, the more attentive style of parenting comes at a cost to the primary caregiver, since multiple trips per child per day can mean hours spent driving. As an example of that cost, Gershuny (1993) argues that the increase in escorting children is linked to women's second-rate position in the job market. Although employers are not legally permitted to discriminate on a basis of gender or parenthood, they are within their rights to deny advancement to employees whose personal lives conflict with work schedules. Given the problems with children's health, pedestrian safety, environmental sustainability and universal access associated with increasing automobile use, it is appropriate that planners seek opportunities to advance pedestrian-orientation and multi-modal design. However, given the reactionary characteristic of development, as Jackson (1985) described, an increase in pedestrian activity may be prerequisite to achieving a more pedestrian friendly, designed physical environment. Therefore, this project focuses on efforts to increase rates of active travel within the existing physical environment. The Policy Problem: Encouraging Active Travel Policy-makers largely view the decrease in active travel to school as problematic and have directed substantial public resources to reverse it. For example, 10

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in 2000, the U.S. Department of Health and Human Services set an objective to increase rates of active travel for trips between home and school for children ages 5-15 to 50% by the year 2010 (USDHHS, 2000). To that end, the USDHHS has provided funding for public-private partnerships to implement active travel interventions within schools. Another example of federal legislation aiming to increase active travel to school is Safe Routes to School (SR2S) a Federal-Aid program of the United States Department of Transportation's Federal Highway Administration, created by Section 1404 ofthe Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU) That legislation allocated $612 million in Federal funds over five fiscal years, 2005-2009 to be administered by State Departments of Transportation. The purposes of the program are: To enable and encourage children, including those with disabilities to walk and bicycle to school ; To make bicycling and walking to school a safer and more appealing transportation alternative, thereby encouraging a healthy and active lifestyle from an early age ; and To facilitate the planning, development, and implementation of projects and activities that will improve safety and reduce traffic, fuel consumption, and air pollution in the vicinity (-2 miles) of primary and middle schools (Grades K-8). To increase rates of safe active travel, the Safe Routes to School National Partnership recommends a combination of interventions that include education encouragement, engineering, enforcement and evaluation (Safe Routes to School 11

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National Partnership [SR2S], 2007) That catch-all approach to intervention intuitively promises to increase active travel by addressing a wide variety of reasons that parents currently choose to drive their children to school. However, research about travel behavior for school trips cannot guarantee that the programs will achieve an increase in pedestrian travel, in part because they cannot adequately explain why parents drive their children to school. Two Gaps Between Planning Research and Praxis Traditional planning research is ill equipped to guide the design of active travel intervention for two reasons. First, several authors argue that correlative research findings do not indicate solutions (Crane, 2000; Handy, 1996; Richards & Ben-Akiva, 1975). Although planning research identifies many factors associated with driving to school, including safety issues, it has not and cannot establish direct, causal relationships. Researchers often describe factors associated with driving as 'obstacles' to active travel and policy-makers treat them accordingly. For example, Safe Routes to School assumes that safety issues prevent parents from having the opportunity to walk their children to school, but does not consider whether parents would be inclined to walk if they could. Further research is needed to determine why parents choose to drive rather than walk. Second, active travel programs are not likely to affect their entire target populations evenly, but few studies differentiate meaningful subgroups for tailored 12

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behavioral intervention. Traditional planning research often controls for personal characteristics rather than examining them directly in order to measure the significance of environmental features. For example, McDonald (2008a) isolates low income and minority students to identify the environmental factors that most influence travel behavior for that segment of the population. In order to evaluate the impact of land use density, diversity and design, Cervero and Kockelman (1997) control for a variety of socio-demographic and household characteristics of the trip maker. The controls are necessary because researchers expect personal characteristics to influence travel behavior. Illustrating that expectation, Boarnet and Sarmiento ( 1998) model travel demand (N) as a simple function of time cost (p ), individual income (y), and a vector of socio-demographic variables (S) in the equation N=j(p,y,S). However, some research does examine personal characteristics associated with mode choice (Bemetti, Longo, Tomasella, & Violin, 2008). These generally focus on standard socio-demographic indicators such as race and ethnicity education and/or income level, but ultimately produce new correlations that cannot explain why different behaviors occur. For example, Bemetti et al. (2008) find that in Trieste, various socio-demographic groups, such as elderly individuals and women are associated with public transit use and recommend that transportation planners take those differences into account. A few studies examine lifestyles and attitudes that 13

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underlie travel choices, but they tend to generalize findings rather than characterizing subgroups (Black, Collins, & Snell, 2001; Hillman, 1995; Joshi & MacLean, 1995). A Case Sensitive Approach My dissertation applies an alternative, case-sensitive research approach that bridges the two aforementioned gaps between planning research and praxis. First, in contrast with traditional research methods that focus on factors external to the research subjects, case-sensitive research focuses attention on the research subjects themselves to examine the attitudes that underlie their travel behaviors. That approach assumes that by addressing issues that resonate strongly with the parents, intervention can influence them to increase the proportion of school trips they make out-of-car. Second, case-sensitive research magnifies differences in research subjects' attitudes towards an issue in order to improve communication of an idea or policy objective. It does not differentiate subgroups of the population based on standard socio-demographic indicators such as race or ethnicity, gender, age or educational attainment. Although those factors are useful as predictors of behavior, they do not indicate solutions to the policy problem. The case-sensitive approach assumes that subgroups of parents share value-orientations about the school commute, and that planners can tailor intervention to maximize its influence on those subgroups. It also assumes that some groups of parents will be more amenable to behavioral change than others, and that it is reasonable to focus intervention within those margins. 14

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Preview of Remaining Chapters In Chapter Two, I examine a theoretical model that explains parents' travel mode choices for elementary school trips (McMillan 2005). Although the model captures key elements of the planning field's prominent activity-choice models, I argue that it emphasizes the opportunity to walk children to school, neglecting parents' propensity to do so if given the opportunity. The model magnifies that shortcoming by failing to explain the significance of personal characteristics and their relationship to other factors. For active travel programs like SR2S to increase the numbers of active school trips, facilitators need to know why parents choose to walk or drive. A growing body of active travel research identifies factors associated with the decline in active travel and with parents choice to drive children to school. In Chapter Three, I review research in this area, noting the accomplishments and limitations of traditional research approaches In Chapter Four I describe my research design and the qualitative methods that I used to examine Denver parents' experiences of the school commute. I conducted short semi-structured interviews with 64 parents from 12 elementary schools that participated in CDOT SR2S non-infrastructure programs and used content analysis to identify themes in the transcripts. Then I compared prominent discursive themes from that study with those from extant literature. 15

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I present my findings from the qualitative study in Chapter Five. The three main themes that resulted from my analysis -life pace, nurture, and context-are consistent with findings from extant qualitative research. In contrast, the variety of statements that composed each thematic category suggests that parents' experiences of the school commute differ even when influenced by similar external conditions. These findings indicate that personal characteristics, including attitudes and perceptions, are closely linked to travel mode choices. They also point to the significance of propensity as a determinant of mode choice. I used data from the qualitative study to develop the tool that I used for a sorting exercise detailed below. In Chapter Six, I describe the quantitative methods that I used to examine parents' attitude types about school travel, and to investigate the relationship between opportunity-related and propensity-related factors. 650 parents from seven elementary schools completed a sorting exercise and questionnaire for this quantitative research phase. I used cluster analysis to identify and compare attitude-based clusters of parents at each school. For each cluster, I then measured levels of opportunity and propensity to use active travel and evaluated those measures as an attitude-based typology. I present my findings from the quantitative study in Chapter Seven. My analysis resulted in 31 discrete clusters of parents at the seven schools. I graph the clusters based on opportunity and propensity measures, and find that even within quadrants of that graph attitude types substantively vary. This finding suggests that 16

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attitude-based evaluation would benefit active travel intervention, even at schools whose populations appear to be culturally homogeneous. In Chapter Eight, I consider the implications of my research findings in terms of the methodological approach for studying and influencing travel behavior, the professional ideology and practice of planning, and a revised conceptual model for predicting travel mode choices. I conclude by offering recommendations for further research. 17

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CHAPTER2: A THEORETICAL FRAMEWORK FOR LEARNING WHY PARENTS REALLY DRIVE THEIR CHILDREN TO SCHOOL Introduction Children walk to school less frequently than they once did (McDonald, 2007; CDCP, 2005; US DOT, 1969-2001 ). As Chapter One describes, researchers blame driving for environmental degradation, children's health problems, and limited independent mobility for children, seniors, people with disabilities and caregivers. Policy-makers address those issues by allocating substantial public resources to programs like Safe Routes to School that encourage children to walk (Hubsmith, 2006). Supporting the policy objective to increase proportions of active trips to school, planning researchers study why parents drive their children to school (McMillan, 2005). Despite progress, some scholars claim that extant work cannot sufficiently guide intervention and that researchers should situate it within a theoretical framework that explains relationships in causal terms (Crane, 2000; Handy, 1996). 18

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In this chapter, I examine McMillan's (2005) model of children's school travel and contrast it with other choice models in planning. Expanding on rational choice theory, alternative conceptual frameworks include environmental, socio demographic and psychological characteristics associated with activity choices. McMillan's (2005) model covers those key dimensions oftravel choice, roughly describing the environmental characteristics as mediating/actors and the social and personal characteristics as moderating factors. However, McMillan's (2005) model only vaguely references interactions between the different types of factors. For example, it is not clear why and to what extent socio-demographic and psychological factors modify the influence of environmental factors on parents' travel mode choices. Because the model focuses on the influence of urban form as an intervention it emphasizes environmental factors over socio-demographic and psychological factors, suggesting that the influences of the latter two are secondary. The emphasis of McMillan's (2005) model on environmental factors reflects a contemporary bias of planning research on the subject. Whereas early works in urban sociology emphasized social and personal systems and only superficially acknowledged the role of the physical setting (Michelson, 1976), contemporary planning research reflects architectural determinism in its assumption that modifications to the built environment can cause and/or direct behavioral change. 19

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My dissertation research builds on McMillan's (2005) and Chapin's (1974) models of travel choice by examining parents' attitudes about the various facets of travel choice (environmental, socio-demographic and psychological factors) and how they relate to the opportunity and propensity to walk to elementary school. Limitation of Correlative Studies Two oft-cited reviews of planning literature about travel choice conclude that extant research cannot sufficiently guide intervention to change behavior (Crane, 2000; Handy, 1996). Both authors explain that travel-demand modeling and multinomiallogit modeling can describe conditions and correlations, but not causal relationships. As a result, the findings of those studies cannot explain why individuals choose to drive rather than walk. That statement echoes Richards and Ben-Akiva's (1975) critique oftravel-demand models from decades earlier. "Traditional models can be regarded as simply simulation models in the sense that, while reproducing a known situation, they have few, if any, explanatory powers These are usually correlative models rather than causal" (1975, p 7). Correlative studies, including aggregate-level and disaggregate-level analyses, leave a gap in understanding because the relationships between factors may be indirect or may be entirely coincidental. For example, McDonald (2008a) describes a correlation between ethnicity and rates of walking to school, but then qualifies the finding, suggesting that the correlation misleads readers. 20

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"However, models controlling for several individual and neighborhood covariates found no differences among racial groups. This suggests that differences in observed rates of active transportation result from differences in the underlying distribution of explanatory factors rather than varied behavior patterns across racial groups" (2008a, p 343). In order for researchers to learn why parents drive their children to school and to guide active travel intervention, they must examine relationships among environmental, socio-demographic and psychological factors. Choice Models Handy ( 1996) explains that even strong relationships between physical environmental factors such as block length or sidewalk width and driving do not guarantee that "pedestrian-oriented" urban form interventions will influence people to walk more often. She recommends framing travel behavior research theoretically in order to understand the nature of independent variables on mode choice. Choice models reflect theories that account for the complex range of factors influencing people's choices, as opposed to focusing on one type of influence (Handy, 1996). That breadth allows the research to guide intervention and influence people's travel decisions more efficiently. McMillan (2005) proposes a conceptual model to explain children's travel for the journey to school (see figure 2-1 ). She bases the model on a review of planning literature focused on efforts to influence travel mode choices for short school trips. 21

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Through this model, McMillan (2005) considers how urban form changes such as widened sidewalks might indirectly affect mode-choices. She assumes that parents determine their children's travel behavior and illustrates parents' travel mode choices as a complex function involving mediating and moderating factors. MEDIATING FACTORS: -Neighborhood Safety -Traffic Safety -Household Transportation Options r URBAN FORM 1-----+ X MODERATING FACTORS : -Social/cultural norms -Socio-demographics -Parental Attitudes 1 PARENTAL DECISION MAKING 1 .._ _____ __, CHILDREN'S TRAVEL BEHAVIOR FIGURE 2-1: CONCEPTUAL FRAMEWORK OF AN ELEMENTARY-AGED CHILD'S TRAVEL BEHAVIOR, Source: Adapted from McMillan (2005) The model roughly outlines mediating factors as physical and social environmental characteristics such as neighborhood safety and traffic safety. It also includes household transportation options as a mediating factor, although that characteristic might be interpreted as socio-demographic rather than environmental if it describes access to private automobiles as opposed to public transportation. The model outlines moderating factors as social and personal characteristics including social/cultural norms, socio-demographics, and parental attitudes. McMillan (2005) uses a multiplication sign (x) to indicate interaction between mediating and moderating factors, and posits that personal characteristics magnify 22

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parents' responses to physical and social environmental characteristics. However, the model focuses on the influence of urban form (physical environmental factors) as determinants of the mediating factors, and ultimately the guiding influence in parents' travel decisions. Rational Choice Models Travel behavior research relies heavily on rational choice theory, the dominant paradigm of microeconomics, which suggests that people choose actions that promise to maximize benefits and minimize personal costs. Although the research nominally emphasizes personal choice in decision-making, some researchers argue that rational choice theory is inherently deterministic because individuals' responses to external stimuli are predetermined (Scott, 2000). Planning research often describes travel behavior in terms of rational choice theory without explicitly framing the discussion within a theoretical travel choice model. For example, Boarnet and Sarmiento (1998) describe travel demand (N) as a function oftime cost (p), individual income (y), and socio-demographic variables (S), and further defines time cost as a conglomerate of urban form characteristics (L) such as density, street grid orientation, and land use mix. They express the model as an equation. N=f(p,y; S), where p= f (L) 23

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Cervero and Kockelman (1997) describe a rational-choice based conceptual framework. The theories they cite assume that trip demand is a function of the alternative destinations' attributes such as quality of service or relative prices at commercial establishments They argue, however, that the framework should also include site characteristics, particularly what they call the 3 Ds of the built environment: density, diversity and design. This expanded model strongly resembles Boarnet and Sarmiento's ( 1998) equation. I present Cervero and Kockelman' s ( 1997) conceptual framework with factors from Boarnet and Sarmiento's (1998) equation in parentheses to illustrate their similarities and to show that the relationships between factors also roughly parallel McMillan's (2005) model (see figure 2-2). The frameworks include socio-demographic variables primarily for control purposes', but acknowledge that those factors are likely to moderate the influence of time cost factors derived from characteristics of the built environment. All three models focus on urban form as a predictor of travel demand Although McMillan (2005) qualifies that urban form changes only indirectly influence parents' decisions, all three models treat relationships between physical environmental characteristics and mode choice as causal. 1 ( 1998) This model also includes transportation options and distance between home and school as control variables. 24

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BUlL T ENVIRONMENT (p -time cost) -Densities (L density) -Diversity (L -land use mix) -Design (L-street grid orientation) 1 SOCIO-DEMOGRAPHICS (control variables) -Gender, Race (S-sociodemographic variables) X -Education, Household income (y-individual income, S household characteristics) (Transportation supply and services, Distance1 ) 1 II TRAVEL DEMAND (and mode) II FIGURE 2-2: TWO UTILITY -BASED CONCEPTUAL FRAMEWORKS Alternative Behavioral Theories Planners and behavioral scientists have developed a number of conceptual choice models that address deficiencies in rational choice theory. Critiques of rational choice theory challenge the expectation that people make choices based on complete knowledge of alternatives. They also dispute the singular focus on self-interest as a motivation for choice. Although these critiques have led to refinements in rational choice models, they still use the deterministic cost/benefit analysis as a predictor of choice. Bounded Rationality Some scholars critique rational-choice theories because they assume that people make perfectly rational decisions based on complete knowledge of alternatives 25

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(Lindblom, 1959; Simon, 1957). Simon (1957) argues that in reality people (including policy-makers and the policies' target populations) make decisions based on bounded sets of alternatives. However, the theory of bounded rationality concurs that within the bounds of limited understanding, people generally work to maximize benefits and minimize costs. For example, Lindblom (1959) argues that planners select from among a few familiar policies rather than seeking the 'best' option because resources prevent thorough examination of alternatives. Similarly, people who are unfamiliar with a bus system may not be inclined to take the bus, even if it promises to reduce the time and monetary costs oftravel. Thus, the theory of bounded rationality suggests that policy can influence mode choice by expanding public knowledge of alternatives. Schuler's (1979) choice model reflects Simon's (1957) bounded rationality theory by emphasizing cognition within an otherwise rational-choice model (see figure 2-4). It describes a linear process by which individuals select retail outlets, starting by perceiving attributes of alternative venues, then assigning values to them and then ranking them by preference. Presumably the ranking follows a rational analysis of costs and benefits. However, Schuler (1979) extends the rational-choice model by acknowledging that cognition impacts the ways that people weigh costs and benefits. For example, people can only compare the characteristics of alternative venues with which they are somewhat familiar, as per Simon's (1957) bounded rationality theory. 26

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Cognition about attributes Subjective assignment Assignment of values to retail outlets associated with spatial alternatives: -commodity traits -outlet factors -location features i of values (weights) to attributes assumed to underlie preference for spatial alternatives Shopping behavior pattern Ranking of outlets in accordance with consumer's values for attributes FIGURE 2-3: CONCEPTUAL MODEL OF INDIVIDUAL SPATIAL CHOICE. Source: Adapted from Schuler (1979) The emphasis on cognition also suggests that people perceive known alternatives differently, which means that environmental factors cannot solely predict mode choices. Michelson (1976, 1977) argues this point, and suggests that antecedent cultural and social systems influence perceptions of physical environmental conditions, thus moderating activity choices. Consistent with that argument, Schuler's ( 1979) model describes a feedback loop in which previous shopping behavior influences cognition of behavioral alternatives. However, it does not specify what types of factors influence people to perceive alternatives differently. McMillan's (2005) model addresses Michelson's (1977) and Schuler's (1979) cognitive emphasis by indicating interaction between mediating and moderating factors. That connection implies that certain personal characteristics may precondition 27

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or predispose individuals to respond to environmental conditions differently. McMillan (2005) only vaguely describes the nature of that interaction. She states: "There may also be factors that have no apparent relationship to urban form and are not seen as intervening causal variables, yet affect parental decision making about the trip to school (e.g., household income, number and age of children in family, cultural norms). Such variables may be moderators, meaning that the strength of the relationship between an intermediate variable and parental decision making may vary for different levels of a variable (such as age or gender) (McMillan, 2005, p. 449). According to her statement, personal characteristics may explain the cognitive limits that Simon (1957) describes in his theory of bounded rationality. Still, she describes the influence of environmental factors as causal, and the influence of personal characteristics as having a secondary, moderating role in determining mode choice. Complex Motivations Some scholars critique rational choice theory's emphasis on self-interest and argue that individuals may be motivated by other psychological influences. For example, Weber (1920) and Parsons (193 7) consider rational, emotional and valueoriented influences on behavior. Their work influenced urban sociologists to examine relationships among cultural, social and personality systems (Michelson, 1976). While these types of factors begin to convey the complexity of psychological influences on people's choices, they can still be operationalized as part of the deterministic cost/benefit analysis of rational-choice theory. 28

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Chapin's (1974) general model ofhuman activity differentiates and elaborates 'opportunity-related' and 'propensity-related' influences, but emphasizes environmental factors in its discussion of intervention (see figure 2-3). In this model, propensity-related influences include a range of motivations beyond utility maximization, including enjoyment, thoughtways, roles and personal characteristics predisposing and preconditioning action. Those elements are weakly comparable to McMillan's (2005) moderating factors, although Chapin ( 197 4) differentiates psychological and socio-demographic factors as two facets of propensity. Opportunity-related factors in Chapin's (1974) model include congeniality of surroundings and availability and quality of facilities or services. Those elements are comparable to McMillan's (2005) mediating factors, and include physical and social environmental characteristics. Like the mediating factors of McMillan's (2005) model, 'congeniality' includes characteristics like neighborhood safety and traffic safety that are influenced by urban form. Chapin's (1974) model does not describe a relationship between opportunity and propensity, except inasmuch as they both determine behavior. 29

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External Sources of Change (e.g. Economic, Population, Cultural) Motivations, Enjoyment& Thoughtways Predisposing Action Roles & Person Characteristics Preconditioning Action Congeniality of Surroundings '-........ L--.-------1 Availability and Qualityof Facilities or Services Satisfaction-Dissatisfaction Levels Producing Change in Propensity Propensity to Engage in the Activity Opportunity to Engage in the Activity Activity Pattern Public and Private Sector Response External Producing Change in + Sources Opportunities of Change (e.g Investments & Regulation Technol?gical, by Public Sector Economtc) Investments & Practices of Private Sector FIGURE 2-4: GENERAL MODEL FOR EXPLAINING ACTIVITY PATTERNS Source: Adapted from Chapin (1974). 30

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The model depicts two feedback loops. In the first, the activity pattern influences policy-makers to intervene with investments and regulation. External conditions such as economic population or cultural changes also influence those policy efforts which focus on modifying the physical built environment and thereby modifying the opportunity to engage in the activity. The model does not explain why the activity pattern would elicit a policy response but it may be assumed that policy makers perceive it to impact the health, safety and/or welfare of the public In the second feedback loop, the activity produces a level of satisfaction that influences people's motivation to participate in it again. The model does not describe policy efforts to address satisfaction levels or to otherwise modify the propensity to engage in the activity. Chapin s (1974) model emphasizes opportunity-related factors by suggesting that public and private sector responses address them alone McMillan's (2005) model similarly emphasizes opportunity-related mediating factors by hypothesizing that urban form indirectly determines mode choice. That emphasis on environmental factors reflects a bias in planning literature on the subject, which often examines elements of urban form as influences on travel behavior Environmental Determinism Planning researchers debate the role of the built environment in shaping social behavior (Lipman, 1969 ; Michelson 1976) Michelson (1976) argues that early 31

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planning research examined urban phenomena using a human-ecological approach that treated the physical built environment as a staging area in which social behaviors and pathologies could or could not occur. For example early urban scholars described the city in terms of concentric zones, influenced by the growth of the central business district. Zones located farther from the CBD were negatively correlated with rates of gambling and other vices because their lower land values did not invite speculation (Michelson 1976) In that respect, the physical environment was not seen as a direct influence on behavior except inasmuch as it housed economic activities Michelson (1976) suggested that planners should examine the combined influences of environmental, social and personal systems in order to understand urban processes, and emphasized that researchers should not overlook the influence of urban form on social behavior. In contrast, Lipman (1969) argues that the architecture profession embraces a belief system that strategically exaggerates the influence of the built environment on social behavior. The "architectural belief system" that Lipman (1969) recounts begins withfunctionalism-the understanding that form follows function (Blake 1963 ; Gropius, 1959). It then proceeds to determinism-the understanding that form causes function (Brody, 1966). Finally, it concludes with social engineering-the understanding that architects should use the built environment to shape social behavior. Lipman (1969) suggests that architecture has solidified its professional role by emphasizing its ability to direct social behavior. 32

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Active travel research examines travel mode choices using conceptual models that reflect a deterministic rational choice theory. McMillan's (2005) model emphasizes the influence of environmental factors, but includes social and personal factors as secondary explanations of social behavior. In this respect, planning theory reflects the environmental determinism that is characteristic of the architectural belief system. Summary Supporting intervention to increase rates of active travel, researchers identify various types of factors and their correlations with mode choices. Correlation studies describe conditions surrounding travel behaviors, but do not explain why parents choose to drive rather than walk their children to school (Handy, 1996; Richards & Ben-Akiva, 1975). As a result, some scholars argue that planning research cannot effectively guide intervention (Crane, 2000; Handy, 1996). McMillan (2005) proposes a theoretical model to explain children's travel behavior for the trip to elementary school, and to guide active travel intervention. The model includes key elements of choice models from other behavioral sciences, but does not detail relationships between factors. Like other choice models in planning, McMillan's (2005), model emphasizes environmental factors that provide the opportunity to walk over personal factors that may incline parents to engage in active travel for the trip to school. That emphasis reflects a bias in the planning literature, 33

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which largely aims to determine how changes in urban form can affect travel demand and mode choice (Boarnet & Sarmiento, 1998; Cervero & Kockelman, 1997; McMillan, 2005). That bias reflects a professional belief system, which posits that built form can be used as an intervention to influence social behavior (Lipman, 1969). My dissertation research builds on McMillan's (2005) and Chapin's (1974) theoretical models to study parents' mode choices for trips to school. I examine parents' attitudes about the school commute, and consider the interaction between environmental, social and personal factors as they relate to the opportunity and propensity to use active travel modes. In the next chapter, I review planning research in more detail, and consider why the emphasis on opportunity-related, environmental factors can limit the capacity of planners to guide intervention. 34

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CHAPTER3: WHY DO PARENTS DRIVE CHILDREN TO SCHOOL? AN INTERPRETIVE REVIEW OF LITERATURE Introduction Percentages of children walking to school have declined between 1969 and 2001 (McDonald, 2007; USDOT, 1969-2001). In Chapter One, I discussed three concerns associated with that trend and policy that aims to reverse it. Policy-making organizations at the national level, including the U.S. Department of Transportation, aim to encourage more children to walk to school (Hubsmith, 2006). However, some scholars argue that transportation planning research has not explained the problem why people drive-sufficiently to guide policy changes (Crane, 2000; Handy, 1996). To encourage children and their parents to walk to school, researchers need to first conceptualize parents' mode choices as part of a comprehensive explanatory model (Handy, 1996). In Chapter Two, I examined McMillan's (2005) model of children's school travel and noted that the model captures key elements ofboth Chapin's (1974) and Schuler's (1979) activity-choice models. However, McMillan's (2005) model emphasizes the opportunity to walk children to school, neglecting parents' propensity to do so if they have the chance. Magnifying that emphasis, 35

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the model also does not explain relationships between key elements such as sociodemographic and environmental characteristics. I borrow concepts from those models to frame my empirical study. In this chapter, I review research that explains why parents drive children to school instead of walking. Similar to McMillan's (2005) model, I argue that researchers' interpretations of findings tend to emphasize opportunity-related factors and to diminish parents' attitudinal diversity. Planning research about school travel addresses the fundamental question-why do parents drive children to school? -using qualitative and quantitative methods. Both approaches richly describe the school commute, but neither guides policy to strategically target parents who drive. The chapter is organized to first briefly review qualitative and quantitative research methods used to study school travel. Then it discusses opportunity-related and propensity-related factors associated with mode choice for school trips Qualitative studies associate mode choice for the school trip with a variety of social and physical environmental factors that either enhance or restrict parents' opportunity to walk children to school. They also associate mode choice with socio-demographic and psychological factors that affect parents' propensity to walk. However, most studies use quantitative methods to distill those findings, concluding that policy should target key environmentae obstacles that prevent children from walking. 2 Note that throughout my dissertation, the term 'environmental' refers to both social and physical characteristics unless otherwise specified. 36

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Travel mode research tends to focus on conventional environmental and socio-demographic variables, neglecting the psychological facets of mode-choice. It also emphasizes the opportunity to walk even in studies that prioritize examining socio-demographic variables. As a result, only limited research addresses parents' inclination to drive, or the diversity of their attitudes about the commute. Why do parents drive children to school? Exploratory and descriptive studies use a variety of methods to identify opportunity-related (environmental) and propensity-related (socio-demographic and psychological) factors associated with driving children to school (Black, Collins et al., 2001; Bradshaw, 1995; Collins & Kearns, 2001; DiGuiseppi, 1998; Hillman, Adams et al., 1990; Joshi & MacLean, 1995; McDonald, 2007; McMillan, 2006b; Pooley, 2004; CDCP, 2005; Schlossberg, Greene et al., 2006; Timperio, Ballet al., 2006). For example, data collection methods include open-ended oral histories and group discussions as well as structured interviews, questionnaires and large-scale surveys. The more open their data collection, the more the studies' findings reflect psychological factors in addition to environmental and socio-demographic factors (see Table 3-1 ). In the next section, I review studies that use qualitative methods followed by studies that use quantitative methods. 37

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Table 3-1: Range of Factors Associated with Travel Mode ..... "' Opportunity-related Propensity-related 0 Q) -Q.. Social/Physical Socio-demographic Psychological u ;;:... Environmental "' Distance Car access Enjoyment -.2 Proximity Life pace ..... ..... 0 Social contact Otii :.a Risk perception Bullies Age Car-centredness Crime Car access Child preference Crossings Gender Contact (social/teacher) "' Distance Maturity (child) Convenience/time Q.. :l e Dogs Number of children Encouragement/praise Oil Driveways Youngest child Enjoyment "' :l Parking problems Environmental awareness u 0 '"'" Stranger Danger Habit "' Surveillance Health benefits Q) Traffic danger/congestion Importance .E Q) Visibility Individual responsibility -= Weather School (materials) 1::: Social norms Vl Q) '"0 Q.. Trip-linking 0 0 ..c: ..... Bus routes Age Activities (caregiver/student) d) "' Block length / design Car access Attitudes about active travel ;;:... ..c: Q) Crime Education Convenience u c: !a :l Crossings Employment Cost d) C/) Vl "' Cui de sacs Gender Family circumstances d) Q) ..... Directness Household size Habits ::l "' Distance Income School (materials, programs) Q) E Housing type Language Social norms Q) > Incomplete sidewalks Licenced Peer pressure .;:; u Infrastructure Marital status Trip-linking Q) B Intersections Physical condition Values 0 "'Neighborhood children Race Q) Percent grid Socio-economic status "0 Population density Tenure d) Retail density Weight > co Service density .... E-Stranger Danger "' Q) Topography ..... "C
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Qualitative Studies As an example of qualitative data co11ection methods, Pooley et a1. (2004) conducted 156 oral history interviews of children, parents and grandparents and used qualitative analysis techniques to find out what was happening during their elementary school years, how they got to school and why they picked the travel mode they reported. This example of "open" methods identified environmental, socio demographic, and psychological, reasons for changes in travel behavior. Specifically, Pooley et al. (2004) found four broad trends associated with increased car use: increasing distances from home to school, increasing access to private vehicles, faster pace of life, and increasing perceptions of risk. The study also describes continuities of walking associated with opportunities to socialize, personal enjoyment and some families' proximity between home and school. The open interview oral history approach al1owed results to include two seemingly contradictory factors: distance and proximity. This finding suggests that factors significant to some parents are not significant to all parents. Similarly, Joshi and McLean (1995) facilitated group discussions with open ended questions and use qualitative analysis techniques to find out why parents choose a particular travel mode. Their study found fifteen reasons that parents drive to school. Collins and Kearns (200 I) surveyed to find out mode frequencies, but include open-ended questions on the self-completion form. By asking parents the 39

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open-ended question, "Why do you drive?" both studies elicited responses that included environmental, socio-demographic and psychological dimensions of choice. In contrast, Black et al. (2001) conducted interviews for a pilot study, but directed parents to "explain in detail and with free responses their understanding of, and attitudes to, traffic and transport problems" (Black, Collins et al., 2001, p. 1129). Although the interview allowed parents to respond openly, it limited the input to specific facets of the policy issue rather than openly soliciting their reasons for driving. That approach ruled out specific intervention tactics, but did not find new ones. Qualitative data collection methods are useful for exploring why parents drive their children to school. They allow respondents to define and/or expand the list of factors that explain their trip decisions, and they allow respondents to respond freely, rather than forcing them to select from among limited alternatives. However, research objectives may not always warrant using qualitative data collection methods. Open ended interviews and systematic observations are resource intensive, typically involve smaller numbers of respondents and cannot generalize findings beyond the study area. In the next section, I review studies that use quantitative research methods. Quantitative Studies Studies of school travel using structured data collection methods such as questionnaires and large-scale surveys with single or multiple-answer, multiple40

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choice questions find combinations of previously defined environmental, socio demographic and psychological factors rather than learning new ones. That approach can be useful for determining population parameters. For example, it can find out what proportion of the study population expresses concern over stranger danger. However, because the choices are limited, it forces respondents to select something as their explanation for travel mode choice, even if their true explanation is not listed. Also, the multiple-choice format may plant ideas that otherwise were not a concern. For example, by reading that stranger danger can be a problem, parents may adopt it as a personal concern even if they previously had not considered it one. Bradshaw ( 1995) and DiGuiseppi et al. (1998) conducted self-completion multiple-choice surveys using conventional econometric variables such as distance, traffic safety, stranger-danger, and conventional socio-demographic variables such as age, gender, and household car-ownership. However, Bradshaw (1995) also included convenience, trip-linking and peer pressure, which reflect psychological explanations. Similar to Bradshaw (1995), McMillan (2006b) measured psychological facets of choice by including likert-scaled attitudinal measures of neighborhood safety, household transportation options, caregiver attitudes, and social/cultural norms. These studies hypothesized relationships between variables rather than broadly identifying reasons that parents drive. As Handy (1996) points out, those studies can describe correlations, but cannot explain travel decisions without a strong theoretical model. 41

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CDCP (2005) and Schlossberg et al. (2006) conducted surveys that included conventional socio-demographic variables but also asked respondents to mark which of several predefined obstacles prevent them from walking. CDCP (2005) defined obstacles primarily as environmental variables-distance to school, traffic danger, crime and weather conditions. Schlossberg et al. (2006) borrowed their Jist of obstacles from Smart Ways to School, a program in Lane County, Oregon, funded by the U.S. Environmental Protection Agency and emphasized personal/psychological variables convenience trip linking heavy backpack, after school programs, and projects or instruments to transport. They also included environmental obstaclesstranger danger and weatheras well as objective measures of neighborhood urban form identified by gee-coding the surveys. Timperio et al. (2006) similarly paralleled perceived and objective measures of environmental variables, but also included perceived and objective measures of personal fitness. By including pre-defined variables these studies measured what portion of the target population each obstacle influences. Doing so focuses attention on the obstacles rather than the affected population McDonald (2007) analyzed data from several years ofNational Personal Travel Surveys. Using the existing data set had the potential to limit her analysis to trip, child, and household characteristics. However, because the study was longitudinal, she introduced and associated additional historical trends such as changing school enrollment policies and childhood obesity levels with changing rates 42

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of active travel to school. As a result, her structured study has an exploratory quality that allowed it to identify reasons parents drive to school. It may not be necessary for every study to identify new factors that parents associate with their travel choices for school trips. However, by limiting the selection of variables in surveys, research may mislead policymakers by overemphasizing certain factors, or by influencing respondents' perceptions. In the next section, I explain how interpretation of survey data overemphasizes environmental barriers to active travel. Distilling Findings to Emphasize Environmental Barriers Most studies that use qualitative data collection methods and content analysis also use a second, quantitative method of data collection and analysis that reduces findings to the most significant factor(s) (Black, Collins et al., 2001; Collins & Kearns, 2001; Hillman, Adams et al., 1990; Joshi & MacLean, 1995). Studies that only use quantitative data collection methods similarly reduce findings (Bradshaw, 1995; DiGuiseppi, 1998; McDonald, 2007; CDCP, 2005). It is significant to note that the reduction of findings tends to emphasize students' opportunity to walk to school in terms of environmental barriers. For example, in addition to the open-ended question and qualitative analysis mentioned above, Collins and Keams (200 1) used a mapping exercise to ask respondents, "Where are dangerous places?" The second interview question focused 43

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responses on geographically specific environmental safety concerns. Despite the broader range of responses from their first question, the authors concluded with recommendations that only address traffic safety. Schlossberg et al. (2006) similarly focused their findings through a mapping exercise that diminished non-environmental explanations for car travel. That approach led respondents to claim environmental explanations for their choices, even if those explanations were secondary. In addition to their open-ended interviews, Hillman et al. (1990) and Joshi and McLean (1995) used self-completion questionnaires that asked parents to pick from among several reasons that they drive to school. These two studies differ because Joshi and McLean (1995) allowed parents to mark more than one answer, although they also ask them to point out their main reason. Both studies reduced findings to the single most significant factor. Hillman et al. (1990) identified traffic danger whereas Joshi and McLean (1995) identified stranger danger. By distilling findings, these studies' appear to contradict each other by suggesting different 'most' important factors. While they both looked for a single factor, what they found was a combination of factors relevant to parents' travel choices. Bradshaw (1995), DiGuiseppi (1998), CDCP (2005) and McDonald (2007) used statistical methods to reduce survey results to the most significant factors. Excepting Bradshaw ( 1995), whose findings included trip-linking, all of the studies emphasized environmental obstacles to active travel. All four studies concluded that distance between home and school was the best predictor of mode choice, although 44

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CDCP (2005) also mentioned traffic danger. Their agreement reinforces the perceived significance of distance from school, which suggests that school districts should change their catchment areas in order to encourage more students to walk. That recommendation is valid if travel mode is given highest priority. However, open enrollment policy also provides families with educational and practical alternatives for their children's education that may outrank travel mode Black et al. (200 1) departed from the environmental focus of the literature by emphasizing attitudinal factors. They conducted an attitudinal, Likert-scaled analysis to find out which of thirty-one statements resonate strongly with respondents. They distilled the findings from that questionnaire to the three strongest attitudinal factors: environmental awareness, personal responsibility and car-centredness. Factor analysis further distilled the results and concluded that environmental awareness is not as strong a predictor as the other two. This study breaks new ground by measuring attitudinal factors for school travel in a probability model. However, similar to other quantitative studies, emphasizes a single factor. In contrast with studies identifying a single prominent factor, McMillan (2006b) and Timperio et al. (2006) used statistical methods to analyze results of questionnaires and surveys, and concluded that a range of environmental, socio demographic and psychological factors influence travel mode to greater and lesser degrees Rather than isolating the most significant factor overall, Timperio et al. (2006) described the relative influence of findings for specific socio-demographic 45

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subgroups. The study by Pooley et al. (2004) also stands out because it presented findings from open-ended interviews without reducing them beyond the initial content analysis. As a result, their findings suggested a range of options for active travel intervention, each of which may target some portion of students and their parents. By emphasizing opportunity-related environmental barriers, extant research tends to assume that parents would allow their children to walk if they could. This is important because that assumption diminishes parents' attitudinal diversity and prevents the research from directing intervention to address strategic subgroups of parents. Specific Variables As described above, travel mode research identifies opportunity-related (environmental), and propensity-related (socio-demographic and psychological) variables associated with parents' mode choice for school trips. Quantitative research tends to include conventional environmental and socio-demographic variables, neglecting the psychological facet of mode-choice It also emphasizes the opportunity to walk even when it purports to address socio-demographic variables. As a result, only limited research addresses parents' propensity to drive, or their attitudes about the commute to school. Extant research generally assumes that parents are homogeneous in their attitudes about school travel, which suggests that intervention 46

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should be narrowly defined and broadly administered to increase pedestrian activity. In the next section, I review opportunity-related and propensity-related explanations for travel mode choice. Opportunity-Related Factors Much planning research assumes that opportunity-related, environmental obstacles prevent active travel behavior. Although the research identifies physical and social environmental characteristics, it rarely differentiates the two and often uses the titles interchangeably. For example, O'neil et al. (2001) list objective physical characteristics of neighborhoods that influence children's home range and include traffic patterns, lighting, levels of crime and vandalism, muggings, poverty and lack of social control. Although each of those environmental factors may influence independent mobility, all but the first two describe social environmental neighborhood characteristics. This distinction is important because, as McMillan (2005) illustrates in her model of children's travel behavior, characteristics of urban form only indirectly impact parents' travel mode choices by influencing primarily social environmental conditions Paradoxically, planning research and policy that aim to encourage active travel focus almost exclusively on physical environmental variables Although qualitative studies identify several social environmental factors associated with mode 47

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choice such as stranger danger and bullying, there are few studies that specifically examine them in that context. Physical Environmental Research focused on physical environmental variables examines how characteristics of land use, urban form and distance to school influence mode choice and how they might be manipulated to encourage active travel (Boamet & Greenwald, 2001; Boarnet & Sarmiento, 1998; Cervero & Kockelman, 1997; Cervero & Radisch, 1996; Crane, 1996, 1998, 2000; Greenwald & Boarnet, 2001; Handy, 1996; McMillan, 2005). Studies of land use and urban form fit into a broader discourse of pedestrian oriented design. In my review, I include research regarding the influence ofNew Urbanism on travel behavior. Evaluations of Safe Routes to School infrastructure improvements similarly test whether urban form influences travel mode (Boamet, Anderson et al., 2005; Boamet, Day et al., 2005; McMillan, 2006b). In that respect, school travel research fits neatly into broader discussions of pedestrian-orientation. Challenging New Urbanism Advocates of new urbanism conceptualize opportunity in terms of time costs, and expect land use characteristics to influence travel mode (Appleyard, Gerson et al., 1981; Katz, 1993). However, empirical evidence to support their claims remains inconclusive (Boarnet & Sarmiento, 1998; Crane, 2000; Handy, 1996). Several 48

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studies challenge claims of new urbanism, testing the hypothesized relationship between urban form and travel mode. In general, the studies have been moderately successful in establishing broad correlations between characteristics of urban form and travel mode. However they do not explain changes of behavior. For example, Cervero and Radisch (1996) and Cervero and Kockelman (1997) compared two San Francisco neighborhoods and found that clusters of measures representing density, diversity and design correlate with mode choice. They qualified the findings saying that urban design characteristics are more strongly associated with mode choice for non-work trips than for work-related trips. Those findings might apply to school trips and would suggest that urban form influences mode choice for students and their caregivers. However, in a comparative study ofwalkable and non-walkable neighborhoods, Frank et al. (2006) found that active travel is associated positively with active lifestyles regardless of environmental context. That is, if people are inclined to walk, they walk, even if there are no sidewalks. Researchers have had difficulty establishing causal relationships between urban form and travel mode, especially where behavioral change is concerned. Crane ( 1996) describes an econometric model of travel demand based on the time costs of various neighborhood characteristics and finds that models cannot accurately predict changes of behavior. Crane (1998) later used travel diaries and GIS data but still found only weak correlations that were not suited to guide intervention. One problem with correlative urban form research is that people may self-select to live in urban 49

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neighborhoods because they enjoy more active lifestyles (Handy, 1996). To guide active travel intervention, researchers need to examine the impact that urban fonn changes have on travel behavior. In the next section, I review research that examines density, diversity and distance, and design. Density Research is well developed regarding the impact of density on travel demand (Cervero & Radisch, 1996; Levinson & Wynn, 1963). However, demand can been accommodated with a variety of travel modes. More significant to advocates of active travel, researchers have begun to investigate the impacts of density on travel mode, particularly the choice between motorized and non-motorized modes for work and non-work trips (Boarnet & Greenwald, 2000; Cervero & Radisch, 1996; Greenwald & Boarnet, 2001). Researchers hypothesize that housing density and subsequent population density influence travel mode in at least two ways. First, the increase of demand facilitates alternative modes of transportation, such as public bussing or ride sharing in lieu of single occupant, private automobiles. However, demand for alternative transportation does not necessarily influence active fonns of travel, except for the portion of each trip from origin and destination to transit stops, which is often disregarded in research. 50

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Second, higher population density can facilitate informal surveillance, what Jacobs (1961) calls 'eyes upon the street,' making streets safer for pedestrians. She explains thus: "The public peace -the sidewalk and street peace -of cities is ... kept primarily by an intricate, almost unconscious, network of voluntary controls and standards among the people themselves, and enforced by the people themselves" (Jacobs 1961, p. 40). As an example of that phenomenon, Ross (2007) found that children's active travel to school reinforces informal surveillance because of the 'weak ties' they form through repeated short interactions with people along the way. Consistent with this theory, Appleyard (2003) found that high neighborhood vehicular traffic correlates to fewer social connections. That suggests that people who do not take as many walking trips make subsequent walks more hazardous. Researchers similarly hypothesize that commercial density including service and retail influences travel demand and mode. That relationship largely depends on access to employment and on distances between origin and destination for work and non-work trips. Studies show correlations between non-motorized trips and land use diversity (Boamet & Greenwald, 2000; Cervero & Kockelman, 1997). However the correlations are stronger for non-work trips (Boamet & Greenwald, 2000; Cervero & Kockelman, 1997; Greenwald & Boamet, 2001). That finding suggests that people continue to drive to work, even if they use active travel for shopping trips. 51

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Land Use Diversity and Distance Researchers hypothesize that land use diversity may encourage active travel (Boarnet & Greenwald, 2000; Cervera & Kockelman, 1997; Greenwald & Boarnet, 200 I). Diversity indirectly relates to active travel by shortening travel distances or improving pedestrian safety. For example, Cervera and Kockelman (1997) included block length and intersection type as measures of circulation access, and found that in conjunction with other characteristics of urban form, diversity modestly correlates with travel demand. School travel research identifies trip distance among the more significant factors determining mode choice (Bradshaw, 1995; Collins & Kearns, 2001; DiGuiseppi, 1998; Krizek, 2003; McDonald, 2007; CDCP, 2005; Schlossberg, Greene et al., 2006). For example, McDonald (2007) argues that increased distance alone explains over half of the decline in active travel. She notes that the increased distance may be explained by several decades of school policy changes that adjusted catchment areas, introduced bussing and increased the size of schools. Contemporary national school guidelines also include minimum lot sizes that cannot be achieved in densely developed areas, pushing schools to the urban fringe and increasing travel distance (EPA, 2003). A study by the Environmental Protection Agency recognizes that existing regulations discourage the construction, adaptation and maintenance of smaller schools, instead dictating the use of large new campuses that may not be in the best interest of public health (EPA, 2003 ). Location aside, 52

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larger schools often necessitate wider catchment areas, which equates to greater travel distances for a portion of students, and increased transportation costs for schools. Lomax (1977) found that inner city schools often have less defined catchments and longer commutes than their suburban counterparts. This may be explained, in part, by open enrollment policies. Encouraged by the 2002 No Child Left Behind legislation, citywide enrollment policies result in greater travel distances for a portion of students, particularly in districts with vouchers, magnet programs and choice systems (Wilson, Wilson et al., 2007). Private schools similarly allow enrollment that is not geographically defined, resulting in greater travel distances (DiGuiseppi, 1998). Advocates of the choice system argue that the market concept improves schools' accountability, encourages desegregation, and allows students and parents to be more actively involved in educational quality control (Schellenberg & Porter, 2003; Schneider, Teske et al., 1997; Taylor & Gorard, 2001; Wilson, Wilson et al., 2007). In contrast with school size, open enrollment may mean that students and their parents travel extraordinary distances to attend smaller, higher-scoring, or otherwise specialized schools, and that district bussing is not available for them. Although parents express satisfaction with these enrollment systems, the longer travel distances impose costs to the families and to their communities in terms of vehicle miles, driving time, physical activity lost and increased greenhouse gas emissions. 53

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Travel distance tops the list of reasons cited by parents for driving children to school. However, a study by the Center for Disease Control found that only 31 percent of children living within one mile of the school walk or bike (CDCP, 2005). This statistic suggests that efforts to increase rates of active travel must consider other explanations for the historic mode change in order to influence behavior. Design Although few studies directly examine the influence of traffic danger on travel mode choices, it ranks among parents' top concerns regarding the school commute (Collins & Kearns, 2001; DiGuiseppi, 1998; Gill, 1997, 2006; Hillman, Adams et al., 1990; CDCP, 2005). Rather than examining hazards directly, researchers measure the influence of design elements aiming to alleviate traffic dangers including road widths, curb cuts, pedestrian crossings, parking styles and continuity, width and maintenance of sidewalks (Appleyard, Gerson et al., 1981; Boarnet, Anderson et al., 2005; Boamet & Greenwald, 2001; Cervero & Kockelman, 1997; Cervero & Radisch, 1996). For example, Boamet, Anderson et al. (2005) and Boarnet, Day et al.(2005) surveyed parents to find out whether construction for SR2S infrastructure projects influenced them to walk more. Boamet, Anderson et al. (2005) used the subjects' proximity to the construction site en route to school to determine a control group, whereas Boamet, Day et al. (2005) surveyed before and after. Both studies found that the construction increased walking and successfully improved safety near the school. However, those changes may also be explained by confounding variables. For 54

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example, there may be a placebo effect in which parents who pass by construction sites alter their travel behavior because they feel that someone is doing something to improve the neighborhood, rather than changing behavior because of the new infrastructure per se. Policies to increase active travel often modify infrastructure in order to improve pedestrian opportunities. For example, Gill (2006) presented case studies of "home zones" in the U.K. that use a variety of tools to limit car speeds, including traffic circles, on-street parking and other road narrowing designs. Those elements reinforce published very low speed limits in residential neighborhoods to protect pedestrians, and are associated with increased pedestrian safety and access to streets. Social Environmental As I mentioned previously, planning research identifies social environmental factors associated with travel behavior. That research emphasizes social environmental pathologies such as stranger danger that prevent active travel. However positive social environmental characteristics such as a strong sense of community may also influence parents' travel mode choices for children's school trips. Research in other behavioral fields examines social environmental characteristics as influences on children's independent mobility and other developmental objectives. These tangential lines of research shed light on travel mode choices for school trips. 55

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Social Environmental Pathologies Stranger danger emerges as a key factor in planning research regarding active travel (Bradshaw, 1995; Collins & Kearns, 2001; Hillman, Adams et al., 1990; Joshi & MacLean, 1995; CDCP, 2005). Bradshaw (1995) included 'concerns about personal safety of children' as among the top factors influencing parents' mode choice for school trips, and finds that personal safety of the child is the most common explanation of mode choice. Similarly, Collins and Keams (Collins & Keams, 2001) and CDCP (2005) found that fear of crime and stranger danger rank among the top reasons parents give for neighborhoods being dangerous for children Planning research identifies, but does little to examine social environmental pathologies as influences on travel mode. Although Hillman et al. (1995) interpreted their findings to emphasize traffic danger, their study concurs with Joshi and Keams (1995) that stranger danger inhibits a significant proportion of parents from allowing children to walk to school. These two studies included "stranger danger" among lists of obstacles for parents to select in survey form. In that context, the term might evoke a variety of images for parents who fear social environmental pathologies as dissimilar as abductions and bullying. Despite recognition of its significance, stranger danger is not typically included in predictive travel behavior models. Perceptions of criminal activity may have a greater impact on parents' choices regarding their children's freedoms than do actual criminal events. Accordingly, few studies discuss the impacts of objectively measured crime on travel behavior. 56

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However, McDonald (2008b) compared data from the 2000 Bay Area Travel Survey and crime data from the Oakland Police Department and finds that for minority adults, high rates of violent crime in the neighborhood negatively correlate to the amount of time spent walking. Nonviolent criminal activity may also indirectly impact travel behavior for children. Some studies find that poor physical environmental conditions such as graffiti and litter "help to create a fear of criminal danger in parents that restricts their child's travel and play boundaries" (McMillan, 2005; Moore, 1986; Valentine, 1997). Behavioral researchers argue that magnified perceptions of social risks support gendered legislation based on patriarchal ideology (Websdale, 1999). For example, studies relate risk perceptions with increasingly cautious parenting styles, including stricter regulation of children's social activities (O'neil, Parke et al., 2001; Pain, 2006) Similarly, studies suggest that fear of criminal activity, particularly abduction, influences parents to limit their children's travel and play boundaries, which might also discourage active travel to school (DiGuiseppi, Roaberts et al., 1998; Eichelberger, Gotschall et al., 1990; McMillan, 2005; Moore, 1986; Valentine, 1997). Exploratory planning research identifies perceived threats from other children and youth (i.e. bullying) as an influence on travel mode for school trips, but does not examine the issue in greater detail (Joshi & MacLean, 1995). However, a substantial body of behavioral research investigates characteristics of 'bullies' versus those of 57

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'victims' to determine why bullying occurs and how to stop it (Giacomantonio, 2009; McGuckin, Cummins et al., 2009). Other behavioral research examines the influence of bullying on a range of developmental outcomes including socialization patterns, self-esteem, school satisfaction, and empathy (Finkelhor, Ormrod et al., 2009; McGrath, Brennan et al., 2009). Similar to other forms of stranger danger, the presence of bullying is not typically included in predictive travel behavior models. Social Environmental Assets Focused primarily on obstacles to active travel, planning research scarcely mentions social environmental assets as possible determinants of travel mode. Some characteristics that appear in the literature include the presence or peer influence of other neighborhood children (Bradshaw, 1995; Frank & Engelke, 2001; Timperio, Ballet al., 2006) and informal surveillance (Collins & Keams, 2001). For example, Bradshaw (1995) found that children and parents are peer pressured into driving to school. In contrast, Orsini (2006) described circumstances in which students who ride bicycles to school encourage friends to do the same. Positive social environmental factors often have reciprocal relationships with active travel, and are recognized as potential benefits rather than determinants of mode choice. (See Chapter One for a discussion of benefits). For example, a strong sense of community within a neighborhood can encourage active travel by providing informal surveillance and therefore countering fears of abduction and other crimes. Reciprocally, some researchers argue that children who are allowed to travel 58

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independently create those protective social contacts en route (Kearns, Collins et al., 2003; Neuwelt & Kearns, 2006). For example, Kearns et al. (2003) described a number of benefits associated with the Walking School Bus system in Auckland, New Zealand. Listed among those benefits is strong parent participation, which subsequently makes the route safer for the activity. Planning research about active school travel identifies a number of social environmental factors associated with mode choice, but it does not examine them as extensively as physical environmental factors, and tends to emphasize pathologies over assets. Those factors tend to be excluded from predictive models, perhaps due to the reciprocal nature ofthe relationships, and recognition of positive social environmental characteristics as benefits (outcomes) rather than determinants of active travel. Research focused on environmental attributes naturally emphasizes the opportunity to walk and obstacles that prevent it. That approach assumes that people generally desire to walk, but cannot because of specific environmental obstacles. Therefore, it guides policy to eliminate those obstacles in order to encourage active travel for everyone. However, since there is typically a portion of the study population that chooses to drive despite having the opportunity to walk, researchers should examine differences in their study populations. 59

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Propensity-Related Factors Planning research about active travel acknowledges, but does not fully examine parents' propensity to walk children to school. Propensity-related factors include socio-demographic characteristics such as race and ethnicity, household income and educational attainment, genders of children and drivers, and children s ages. They also include psychological factors such as convenience, control and autonomy, identity and status Socio-Demographic Planning researchers often control for socio-demographic variables in order to test characteristics of land use and urban design for the broader population (Bemetti, Longo et al. 2008; Boamet & Greenwald, 2000; Cervero & Kockelman 1997; Frank, Sallis et al. 2006 ; Krizek 2003). By assuming that parents would walk children to school if they had the opportunity, that research takes for granted that those values are widespread. However some researchers argue that interventions are not likely to affect target populations uniformly (Bemetti Longo et al., 2008). Several studies discuss how socio-demographic characteristics including race, income, gender and age influence travel behavior (Bemetti, Longo et al. 2008; McDonald 2008c; McMillan T., 2006b ). However standard socio-demographic indicators arbitrarily sub-divide populations and imply commonalities that do not hold true at the disaggregate level. Activity-based research expands socio60

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demographic studies by analyzing clusters of variables to capture "roles" and "lifecycle stages" that may influence travel mode choices (Handy, 1996; Hillman, Adams et al., 1990). For example, Salomon and Ben-Akiva (1983) examine travel behavior for subgroups defined by the major roles of: household member, worker, and consumer of leisure. Although the activity-based approach provides highly descriptive correlations, the findings still cannot explain why parents drive to school (Handy, 1996). Solving that persistent problem requires an alternative research approach. In the next section, I review research focusing on race and ethnicity, parents' income and education, genders of children and drivers and ages of children in the home. Race and Ethnicity Researchers focused on race and ethnicity as predictors of travel mode struggle with confounding variables that make findings difficult to interpret. Instead, they interpret them through alternative theoretical lenses. For example, Frank (2001) observed that "environmental barriers may have disproportionate impacts on different subgroups within the population, most especially for vulnerable groups" (Frank & Engelke, 2001, p. 209). McDonald (2008a) found that for low-income and minority students, violent crime presents a critical obstacle to active travel. Neither of these studies suggests that pigment has anything to do with travel behavior. Instead their interpretations focus on social justice rather than active travel, and suggests that 61

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obstacles such as neighborhood crime rates prevent vulnerable groups (i.e. minorities) from walking more so than others. However, studies show that minority populations walk more frequently than white populations (McDonald, 2007, 2008a) In a longitudinal study of active travel, McDonald (2007) found that minority students are twice as likely as white students to walk to school. McDonald (2008a) showed significant differences in rates of active travel between Hispanics (27.7%), non-Hispanic Blacks (15.5%), Asian and Pacific Islanders (13.4%), respondents reporting more than one race (12.2%), and Whites (9.4%). The strong correlation between minority status and high rates of childhood obesity appears to contradict findings of higher rates of active travel for the same population. This suggests one or more confounding factors, such as access to healthy foods. McDonald (2008a) and (2008b) note that ethnicity and racial factors are often nullified when controlling for other individual and neighborhood covariates, such as household income, education level, rates of reported crime and other factors Parents' Income and Education Research identifies household income, and education level as factors associated with travel mode, but results are inconclusive at the disaggregate level (Bradshaw, 1995; DiGuiseppi, 1998; McDonald, 2008a). For example, Boamet and Sarmiento (1998) found that household income is positively associated with active travel. In contrast, McDonald (2008a) found that students from lower-income households walked more than twice as often as those from higher-income households. 62

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The discrepancy may be explained by the scale and location of the study, since Boamet and Sarmiento's (1998) study was conducted at the neighborhood level in California, whereas McDonald's (2008a) study drew from the National Household Travel Survey. This suggests that additional research is necessary to better understand the role of income in active travel. Some researchers argue that income has an indirect influence on travel mode because it determines access to private vehicles (Bemetti, Longo et al., 2008; Bradshaw, 1995; Cervero & Kockelman, 1997). For example, Bradshaw (1995) found that of the households in his study sample that drive both ways to school, 73 percent have two or more cars, in contrast to only 60 percent of the larger sample population. Consistent with that finding, Sadler (1972) found that children from families of professional social classes are more likely to be driven than those from unskilled social classes. In contrast with studies focused on environmental barriers to active travel, these studies suggest that parents are homogeneous in their desire to drive, and that some walk only because they lack access to vehicles. Genders of Children and Drivers Several studies consider how gender influences travel mode for non-work travel generally and for the school commute in particular (Boamet & Greenwald, 2000; Cervero & Kockelman, 1997; Cooper, Page et al., 2003; McDonald, 2008d; McMillan, 2005). For example, Boamet and Sarmiento (1998) found that women generally make more non-work trips than men. This finding is consistent with that of 63

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Cervero and Radisch (1996), who found that women bear a greater share ofthe responsibility for childcare and other domestic chores, and consequently more often require the use of a car. The additional responsibility for household 'errands' may also contribute to trip-linking behavior. Research also indicates that gender is significant to children's travel mode for school trips. In a study of children's "home range", Spilsbury (2005) found that for 1 0-11 year olds, perceptions of neighborhood violence resulted in more restricted movement for girls than for boys. McDonald (2007) found that over the past several decades, boys consistently walked to school more than girls, but the decline in active travel has affected both genders equally. McMillan (2006a) adds that girls are 40 percent less likely to use active travel than boys. Cooper et al. (2003) found that for boys, active travel to school is associated with higher rates of overall physical activity patterns. That finding suggests that active travel explains healthier lifestyles, but the association may also indicate the reverse. Boys, who may be more active generally, may also be more likely to choose an active travel mode for the school commute if given the opportunity. Age and Constructions of Childhood Modem constructions of childhood in the United States and in other western contexts contribute to and are perpetuated by age segregation and other restrictions to independent mobility that appear in the design of spaces and in public policy (Aries, 1962; Holloway & Valentine, 2000; Simpson, 1997). In large part, modem planning 64

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has considered young people in only two contexts. First, the needs of young people have necessarily been the focus of child-specific projects such as skate parks, playgrounds, and school settings (Eccles & Gootman, 2002; Freeman & Aitken-Rose, 2005; Mitra, 2001; Simpson, 1997; Ward Thompson, 1995; Young, 1990). Second, young people are considered in the design and management of places where their presence or characteristic behaviors are unwelcome3 This reinforces socio-spatial divisions between young people and adults and draws attention to non-conforming behaviors, such as unsanctioned adult presence on or near school grounds or youth loitering or demonstrating in urban spaces during 'school-time'. Youth advocates argue that contemporary western trends of social separation by age are problematic, as reflected in the common use of terms such as 'discrimination', 'segregation', and 'restrictions', evoking images of oppression. Indeed, social constructions of young people in the U.S. and external manifestations of those constructions impact the quality of life for individual young people and restrict independent mobility. For example, in many urban neighborhoods, children's access to natural areas is severely limited, and in some cases children are not allowed to play outside at all for fear of abduction, traffic accident or other harm (Karsten & van Vliet, 2006; Louv, 2006). 3 While examples of this type of exclusion or behavioral restriction are plentiful and commonplace, I would argue that this includes most spaces that are not child-specific and even those designed for a specific age subset of children or youth. 65

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Research identifies age as a significant correlate to travel mode for non-work trips generally and for the journey to school in particular (Boarnet, M. & Sarmiento, 1998; Cervero & Radisch, 1996; Greenwald & Boarnet, 2001). Several studies find differences in both the mode of travel and the likelihood of parent accompaniment based on the age or grade ofthe student. For example, Joshi and McLean (1995) found that parents are more concerned about the safety of younger children due to age-related levels of competence and pedestrian ability. Some argue that children have less developed perceptions of time and space which can make it more difficult to judge safe distances of oncoming vehicles (Administration, 2008; CDCP, 2007). In addition, because they tend to be smaller, younger children are more difficult for drivers to see (FHA, 2008). Geographers note that streetscapes and other public spaces tend to be designed for able-bodied adults, which necessarily limits their functionality and access for children (Collins & Keams, 2001; Matthews & Limb, 1999). Activity-based research begins to consider socio-demographic factors in combinations that may influence travel behavior (Handy, 1996). For example, Hillman et al. (1990) as well as Joshi and McLean (1995) combined gender, age and numbers of children to differentiate life-stages and household roles. Both studies found special significance in specific household roles, such as homemaker, that transcend typical socio-demographic variables. Still, the improved categories assume that people of similar socio-demographic categories respond to external stimuli by 66

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choosing the same travel mode. That assumption does not account for attitudinal diversity. Psychological Exploratory research identifies several psychological factors associated with driving including perceptions of convenience, autonomy and status (Bradshaw, 1995; Reser, 1980). Those psychological influences receive less attention in research and policy than physical environmental conditions and socio-demographic characteristics. However, as rising costs of ownership and operation of private vehicles do not seem to deter their use, some scholars suggest that an attitudinal approach is indicated. Reser (1980) states: "The seeming insensitivity to costs and inconvenience would suggest that the private car is serving other than utilitarian needs .... There is substantial intuitive and theoretical justification for saying that a social change effort of the nature suggested ... necessitates an accurate assessment of the functions presently being served by the complex of attitudes, values and behaviors related to the car" (1980, p. 281). Researchers find that parents associate private automobile travel with convenience, a welcome feeling of control and autonomy, as well as personal identity (Black, Collins et al., 2001; Reser, 1980; Sorkin, 1992). Whereas researchers may interpret opportunity-related factors as obstacles preventing parents from choosing the more desirable, active modes of travel, these findings suggest that parents may have differing value systems that underlie their inclination to walk. Behavioral 67

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intervention must take these value-systems into consideration if it is to influence individuals to walk rather than drive to school. Convenience Several studies find that parents choose to drive because it is more convenient than walking (Collins & Kearns, 2001; Reser, 1980). Activity-based studies indicate that household scheduling considerations play a key role in travel mode decisions (Bradshaw, 1995; Collins & Kearns, 2001; Crane, 1996; Jones, Dix et al., 1983). For example, a number of studies suggest that dual-income households are less likely to use active travel for the children's commute to school because parents have to negotiate the timing of school trips with their work commute (McDonald, 2008d). McDonald (2008d) found an increase in the numbers of mothers working outside the home in recent decades, as well as an increase in parental accompaniment. Since working mothers take a greater share of responsibility for childcare and domestic chores, they are more likely to link trips for the sake of convenience (Bradshaw, 1995; McDonald, 2008d). Bradshaw (1995) found that nearly 60 percent of car drivers in her sample continue to their place of employment after dropping the children off at school. The strong association between commutes to school and to work may be explained by timing as well as time spent. Even parents who only have a single trip to make in addition to the school drop off may choose to drive ifthe timing ofthe two events overlaps. The desire to save time reflects increasingly busy schedules as opposed to 68

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laziness or relaxed attitudes. Hupkes ( 1982) describes a 'law of constant travel', which suggests that people invest a fixed proportion of their time for travel (Frank, Sallis et al., 2006; Hupkes, 1982). The convenience of private automobile transportation allows individuals and families to go farther and to accomplish more while maintaining set proportions of travel time. Control and Autonomy Some studies describe private automobile travel as a coping mechanism that allows drivers to establish a sense of control in an uncertain environment (Breznitz, 1980; Reser, 1980). The reference to uncertainty is consistent with discussions of postmodernity, which suggest that rapid change drives individuals to seek identity and rootedness in commodities (Harvey, 1989; Sorkin, 1992). Private transportation promises control over path and schedule, which are otherwise often dictated by external constraints, as well as control over immediate physical and social environments (Breznitz 1980; Reser, 1980). For some, the perceived freedom of personal choice associated with private transportation may appear greater than the diversity of choice available through a variety of alternative travel modes. Ironically, the perceived sense of freedom associated with private travel often relegates caregivers to the role of chauffeur for dependent children and limits the independent mobility of each (McDonald, 2008d). 69

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Identity and Status Mode of travel establishes and to reinforces personal identity and social status for drivers and passengers (Reser, 1980; Sorkin, 1992). Some studies associate the private automobile with symbolic expressions of wealth, status, sexuality and power (Reser, 1980). For example, popular media, particularly advertisements, associate sports cars with desirable body images (Packard, 1957). Reser (1980) notes that research subjects do not always acknowledge identity-related influences in their travel decisions, because they work subconsciously. Similarly, researchers hypothesize that peer pressure and societal norms influence parents' travel mode for the school commute (Black, Collins et al., 2001; Evenson, Motl et al., 2007). Studies show that the school or community 'climate' regarding travel behavior influences parents to walk or drive. For example, Evenson et al. (2007) found that perceptions of encouragement, praise and importance placed on active travel influences parents' to walk. The perception that driving to school is normal, expected or desired may influence parents' travel mode decisions despite other practical considerations. Some studies find that parents drive to school to indicate high-quality parenting. Sanger argues that in highly suburbanized Western cities, 'driving provide(s) evidence of good parenting and mileage the measure of maternal contribution to familial welfare" (Sanger, 1995, p. 719). Soloman (1993) similarly argues that contemporary, highly attentive parenting styles influence travel mode 70

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decisions. Researchers often interpret the need for accompaniment and automobile travel as a reflection of perceived safety hazards on the journey to school, including traffic danger and stranger danger (Hillman, 1995; Hillman, Adams et al., 1990; Joshi & MacLean, 1995). However, they may also reflect behavioral norms associated with identity, status and societal pressure to conform. Children's preferences also influence parents' travel mode decisions for the school commute Their influence reflects parenting styles that emphasize attention, accommodation and participatory decision-making. Some studies describe reciprocal relationships between travel behavior and children's travel preferences. For example, Collins and Kearns (200 1) found that preference for walking correlates with walking behavior, and preference for inactivity correlates to inactive behavior. They interpret the finding to mean that active travel can condition children to be active adults. However, it may also indicate self-selection. Children who will grow to be active adults choose to be active in their youth. Environmental Determinism Although active travel research identifies opportunity-related (environmental) factors and propensity-related (socio-demographic and psychological) factors associated with driving children to school, the research largely emphasizes environmental factors, assuming that parents' desire to walk, but are prevented by external obstacles. Socio-demographic studies seem to suggest the opposite that 71

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parents who have the opportunity to drive will do so because they can. Ultimately either assumption suggests that behaviors closely follow environmental opportunities, which reflects the environmental determinism of the architecture and planning professions (see Chapter Two). Summary Fewer children walk to school than did even a few decades ago (McDonald, 2007; USDOT, 1969-2001). In response to the decline, active travel programs aim to encourage students to walk more often (Hubsmith, 2006). Research can support active travel intervention by finding out why parents drive children to school. Research about travel mode for school trips identifies opportunity-related (environmental), and propensity-related ( socio-demographic and psychological) factors associated with driving. However, studies that begin with open-ended questions often proceed to distill findings so that they conclude with only the most significant factors, which tend to be environmental. As a result, the recommendations neglect other, also significant propensity-related factors. Active travel research tends to interpret statistical significance of certain factors to mean "worthy of policy attention" That interpretation appropriately identifies trends such as the recent decrease in active travel that policy needs to address (McDonald, 2007). However, as a guide for specific, behavioral intervention, it favors a majority ruling over more strategically selected target groups For example 72

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researchers point to travel distance and traffic danger as two of the strongest correlations to private automobile travel and conclude that overcoming those obstacles would significantly increase rates of active travel (Bradshaw 1995; Collins & Keams, 2001; DiGuiseppi 1998; Krizek, 2003; McDonald 2007 ; CDCP, 2005 ; Schlossberg Greene et al., 2006) However, by focusing on those families for whom distance and major crossings interfere with active travel, the policy misses an important target market. For example, it overlooks the margin of families who live close to schools and do not cross major road along the way but choose to drive for other reasons. This is an important focus of my research that I address by examining the relationship between parents' opportunity and propensity to use active travel for school trips. Quantitative travel mode research emphasizes environmental obstacles that prevent families from having the opportunity to walk. Although exploratory studies tend to find both social and physical environmental characteristics associated with mode choice, research and policy emphasize changes to the physical environment to encourage walking. Proponents of new urbanism claim that characteristics of urban form promote pedestrian activit y Several researchers have conducted evaluations of Safe Routes to School infrastructure projects to determine whether they increased rates of active travel. Although they found some successes, research on those claims remains inconclusive due to confounding factors. By emphasizing environmental factors, the research assumes that parents share a propensity to walk their children to 73

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school, but lack the opportunity. That assumption disregards attitudinal diversity that could explain parents' travel mode choices. Some researchers argue that interventions affect people differently and that it is important to find out why (Bernetti, Longo et al., 2008). However, even research that focuses on socio-demographic characteristics emphasizes the opportunity to walk. As researchers find differences in travel behavior between ethnic groups, for example, they often interpret the findings through the lens of social justice by suggesting that vulnerable populations experience environmental obstacles more than others. Those findings do little to explain how personal characteristics affect mode choices, instead deferring to environmental factors. Psychological factors receive much less attention in research than environmental and socio-demographic factors. However, qualitative studies identify several factors relating to parents' inclination to drive children to school, including convenience, feelings of control and social identity. Although some researchers argue that planners ought to focus solely on environmental intervention, psychological factors influencing behavior can render environmental planning efforts ineffectual. My research examines parents' attitudes about the school commute, emphasizing the interaction between parents' and propensity to walk to school. In the next chapter, I describe my research methods in detail, explaining how an attitude based approach can improve intervention and encourage active travel for school trips. 74

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CHAPTER4: QUALITATIVE METHODS Introduction Over the past century, and particularly after World War II, cities in the United States have become increasingly automobile-centered (Newman & Kenworthy, 1999). More recently, the trend towards private automobile travel has extended to include children's trips to and from school. Data from National Personal Travel Surveys indicate that in the United States, the proportion of students walking to school decreased from approximately 49% in 1969 to 15% in 2001 (USDOT, 19692001). National health authorities, including the United States Department of Health and Human Services, recognize health problems associated with inactivity, including childhood obesity, heart disease, diabetes and asthma. In response, they have directed substantial public resources to increase the numbers of students walking or biking to school (USDHHS, 2000). An example is Safe Routes to School (SR2S), a federal-aid program ofthe United States Department of Transportation's Federal Highway Administration, created by Section 1404 ofthe Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU). The legislation allocated $612 million in federal funds over five fiscal years, 2005-2009, to address 75

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issues such as traffic danger and stranger danger associated with private automobile use for the journey to school (Hubsmith, 2006). For active travel programs like SR2S to increase the numbers of active school trips, facilitators need to know why parents choose to walk or drive. As I described in Chapter Three, planning research examines parents' perceptions of barriers to active school travel and finds a combination of environmental, socio-demographic and psychological factors that prevent them from walking or biking their children to school (Ahlport, Linnan et al., 2007; Collins & Kearns, 200 I; Hillman, Adams et al., 1990; Joshi & MacLean, 1995; McMillan, 2006b). Quantitative studies measure which factors influence parents' travel mode choices to determine the most significant barriers to active travel (McMillan, 2005; McMillan, 2006b; Schlossberg, Greene et al., 2006). By focusing on factors that prevent active travel, extant research implies that parents are a homogeneous population and that they desire to walk or bike their children to school but simply have not had the chance. Active travel programs will be more effective if they aim intervention at target populations who can walk or bike but who nevertheless choose to drive. Therefore, it is necessary to examine program facilitators' as well as parents' perceptions and experience of the daily school commute. In this chapter, I describe the research methods that I used to investigate perceptions of trips to and from elementary school in Denver Colorado. I begin by 76

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describing my overall research design, which includes two phases, and the research setting, which includes three SR2S programs and twelve participating schools. Then focusing on the first, qualitative research phase, I describe my methods for contacting elementary school students' parents at the twelve schools and I discuss the resulting sample of interview respondents. Finally, I present my qualitative data collection and analysis techniques. Data from this phase of research include transcripts from the three SR2S programs' planning meetings and events and from a series of voice-recorded, semi structured interviews with parents. Results from this qualitative study are presented in Chapter Five. They also contribute to the design of a sorting exercise used in the quantitative study described in Chapters Six and Seven. Research Design My mixed-methods research design included two phases that spanned three semesters (see table 4-1). I conducted the first, qualitative phase of my research during the 2007-2008 school year. The second, quantitative phase included a pilot sorting exercise that I administered during the spring semester 2008, and a main sorting exercise that I administered during the fall semester 2008. Both the qualitative data collection and the pilot sorting exercise occurred while SR2S programs were in progress at the respective schools The main sorting exercise occurred in the semester following the schools' completion of the SR2S programs. 77

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Table 4-1: Data collection timeline Phase Activity Schools Timing 1-Qualitative Gaining Entry 12 schools Fall2007 Interviews Spring 2008 Observations 2Quantitative Pilot Sorting 4 schools Spring 2008 Exercise 2Quantitative Main Sorting 7 schools Fall2008 Exercise As I explained in Chapter One, the purpose of my research was to bridge a gap between traditional planning research regarding travel mode choices and intervention that aims to influence travel behavior. Specifically, it aimed to characterize parents' attitudes cross-sectionally rather than to measure the influence of intervention longitudinally. However, it was necessary to select schools that were participating in SR2S projects so that I could include facilitators' perspectives of parents' commuting experience. To that end, I conducted the qualitative phase of my research with a sample of parents from the schools while their SR2S programs were in progress, and the quantitative phase when the programs were recently completed. In the next section, I describe the research setting and specific research sites, including thirteen schools that participated in SR2S non-infrastructure programs. 78

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Research Setting As a magnet city for outdoor enthusiasts, Denver Colorado is an appropriate location to study attitudes toward active travel for school trips. Colorado's front range is widely known for its outdoor recreation amenities and the city attracts significant numbers oftourists and full time residents for that reason. Denver's climate is generally mild, with low relative humidity, distinct seasons and abundant sunshine year round. During the coldest months (December and January) temperatures range from 15 45 degrees Fahrenheit. Low extremes are unusual during the daytime, however, and temperatures are often well above freezing throughout the school year (SeptemberMay). Denver is located in the western high plains, a high plateau that rises gradually to the foothills of the Rocky Mountains. The topography includes low rolling hills that increase in intensity west of the city. In short, the climate and topography naturally accommodate active travel. Despite those qualities, the percentages of students walking or biking to schools are similar to national levels and have experienced a steady decline in the past four decades. Denver is taking a leadership role in school-based active travel intervention, which also makes it an ideal site for studying attitudes toward active travel for school trips. Political leaders have set progressive environmental goals for the city, including efforts to decrease automobile traffic and harmful greenhouse gas emissions (City and County of Denver, 2006). To that end, Denver's City Council signed Proclamation 15 in March 2007, creating a Safe Routes to School Coalition to develop a shortand 79

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long-term action plan and to support Safe Routes to School programs throughout the school district by 2011 (City and County of Denver, 2007). As a leader in these areas, Denver provides a rich environment for studying attitudes about active travel, and programs that aim to increase walking and biking to school. Research Sites and SR2S Program Affiliation I selected thirteen public elementary schools as research sites based on their participation in Denver's Safe Routes to School (SR2S) non-infrastructure programs during the 2007-2008 school year. I focused on non-infrastructure programs for two main reasons that are described below: (1) so that my study sites would include diverse socio-economic and racial groups, and (2) so that the programs would address the widest possible range of attitudes about school trips. First, my purpose was to study parents' mode choices for the school commute, which planners theorize are influenced by a combination of environmental, socio demographic and psychological factors that impact the opportunity and propensity to choose active travel (Ahlport, Linnan et al., 2007; Collins & Kearns, 2001; Hillman, Adams et al., 1990; Joshi & MacLean, 1995; McMillan, 2006b). Although an educational component is required for SR2S infrastructure projects, the scope of that requirement is limited and addresses a narrower range of factors that influence parents' travel mode decisions for school trips. In contrast, the scope of the non infrastructure grant includes a balance of activities including site assessments, 80

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surveys with parents and students, educational programs and events with incentives for students who walk or bike to school. Although individual programs vary in their specific intervention plan, I was able to observe several programs and find out how their respective facilitators expected to influence travel behaviors. Second, I intended to study parents' perceptions without narrowing to a specific demographic group. Because infrastructure projects are often resource intensive and resources are so limited ($1.0 to $1. 6 million per year statewide) CDOT allows only larger grant applications ($50,000 to $250 000) coordinated by the City, and carefully assesses them in the context of existing infrastructure plans (CDOT, 2009). As a result, only two grants were approved for Denver in 2007one focused on bicycling improvements and one focused on pedestrian improvements and both were awarded to the same facilitator Although the grants totaled approximately $220,000, the projects would impact only two schools (Swansea and Ashley Elementary Schools) both of which have predominantly Latino enrollments and over 80% of students on the free/reduced lunch program. In contrast, non-infrastructure projects (for education and encouragement) require fewer resources and can be funded more widely across the city in a single grant year. Statewide funding ranges from approximately $150 000 to $500,000 and individual grants must be at least $3,500. In 2007, CDOT awarded SR2S non infrastructure grants to three Denver public/non-profit teams: Denver Public Schools with Denver Osteopathic Foundation Denver Environmental Health with 81

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Transportation Solutions, and Denver Health with DPS Rides. Although the grants totaled approximately $130,000, the projects would impact twelve schools (see table 4-1 ). Please note that I selected a thirteenth school that is only included in the quantitative study, but the site description is included in this chapter for convenience. Munroe Elementary completed its SR2S non-infrastructure program in 2007. Table 4-1: 2007 Denver SR2S programs and participating schools Teams School %Black %Latino %White %Lunch #Enroll Denver Edison 3.0 47.3 46.9 42.5 461 Public Schools Force 0.7 89.4 7.5 77.4 585 Denver Sabin 3.5 62.1 26.5 44.3 634 Osteopathic Foundation Slavens 2.2 5.7 89.3 1.5 456 Denver Cory 3.5 12.7 79.1 8.4 369 Environ. Health Bromwell 5.2 3.7 80.7 7.4 326 Transport. Steck 7.5 9.6 76.7 10.6 292 Solutions Philips 83.4 13.0 3.5 84.0 169 And Hallett 70.8 27.2 0.4 79.6 250 Stapleton TMA Smith 50.3 49.0 0.8 86.0 392 Denver Health Lowry 28.8 16.6 52.4 40.9 458 DPS Rides Valdez 1.1 95.4 2.3 87.3 434 University Munroe 1.0 94.6 2.0 74.8 574 Average 20.1 40.5 36.0 49.6 415.4 Source: Piton FoundatiOn School Facts, Data Year 2008, http://www.p1ton.org/ 82

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The thirteen participating schools represent a wide variety of Denver neighborhoods, differing in physical design, socio-economic status and other characteristics. The resulting site selection includes schools with clear majorities of White, Hispanic and Black enrollment, with a range of income levels (indicated by percentage of students in the free/reduced lunch program), and widely ranging in enrollment size (Piton Foundation, 2007). Following are brief descriptions of the 2007-2008 SR2S non-infrastructure programs that I selected and the schools in which they were administered. Program 1: Denver Public Schools and Denver Osteopathic Foundation Denver Public Schools (DPS) partnered with Denver Osteopathic Foundation (DOF), a non-profit organization, for the first program grant. Although one representative from each organization (Debbie from DPS and Lisa from DOF) participated in most of the meetings and activities, DOF took the lead in designing and carrying out the intervention. In compliance with grant requirements, Program I tallied students travel modes for one week during each semester, and conducted surveys of parents at the beginning and end of the school year regarding barriers to active travel. In addition, it established a School Traffic Safety Committee and assessed site conditions with the committee's assistance. This program emphasized teaching children pedestrian and bicycle safety skills. It presented bicycle safety to grades 3-5 in the school 83

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auditoriums, conducted outdoor bike "rodeos" for a smaller number (no more than 30 per school) of students in grades 3-5, and invited local firemen to teach basic pedestrian safety in K-2 classrooms. Building on the safety instruction, the program encouraged students to walk by offering incentives at special events. It conducted National Walk to School Day in October, and conducted Walking Wednesdays in the latter part of the spring semester Program 1 worked with four schools with above-average enrollment that varied widely in demographics and physical neighborhood context: Edison, Force, Sabin and Slavens. In particular, Slaven stands out from the other schools because of its predominantly White student population and because the school ranges from kindergarten to 81h grade. Edison Edison Elementary (K-5) School is located in northwest Denver and primarily serves Highland and West Highland neighborhoods, both of which have sizeable Latino contingencies, lowto mid-range average household incomes, and higher than average population densities in an urban residential setting (see table 4-2). Table 4-2: Demographic profile of Edison neighborhoods versus City of Denver Neig_hborhood %White %Latino %Black Avg. Inc. Density Highland 29.4 66.8 1.4 $39,568 6,932 West Highland 64.5 30.9 1.9 $50,110 5,896 Average 46.95 48.85 1.65 $44,839 6,414 Denver 51.9 31.7 10.8 $55,129 3,617 84

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As indicated in table 4-2, Edison's neighborhoods have a slightly higher than average Latino population, slightly lower than average Black population, and have lower average household income than Denver. Those differences are more pronounced for the Highland neighborhood by itself. The demographic profile of Edison Elementary School is consistent with the neighborhoods it serves, with 47.3% Latino, 46.9% White, and 3.0% Black students. Consistent with economic indicators in those neighborhoods 42.5% of students are enrolled in the free or reduced lunch program at the school. The neighborhoods also have roughly twice the average population density of Denver and include mixed-use and residential developments organized around a tight grid pattern of local streets (see image 4-1). According to pedestrian-oriented development theories that combination of qualities should coincide with higher than average rates of active travel and attitudes consistent with active travel behavior. 85

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Image 4-1: Edison elementary school in neighborhood context Source: Google Maps (b.ttp://maps.google.com) Force Force Elementary (K-5) School is located in south-central Denver and primarily serves Mar Lee and Westwood neighborhoods, both of which have sizeable Latino contingencies, low average household incomes, and higher than average population densities in an urban residential setting (see table 4-3). 86

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Table 4-3: Demographic profile of Force neighborhoods versus City of Denver Neighborhood %White %Latino %Black %Other Av lncome Density Mar Lee 36.4 55.3 1.4 5 .8 $42 614 6,291 Westwood 16.9 76.0 1.2 5.7 $37,961 7,724 Average 26.65 65.65 1.3 5.75 $40,288 7,008 Denver 51.9 31.7 10.8 5 6 $55,129 3,617 As indicated in table 4-3, Force's neighborhoods have about double Denver's average Latino population, half Denver's average White population, lower than average Black population, and have considerably lower average household income than Denver. Those differences are more pronounced for the Westwood neighborhood alone. The demographic profile of Force Elementary School is inconsistent with the neighborhoods it serves, with 89.4% Latino, 7.5% White, and 7% Black students. Consistent with economic indicators in those neighborhoods, however, 77.4% of students are enrolled in the free or reduced lunch program at the school. Force's neighborhoods also have roughly twice the average population density of Denver, but unlike Edison's, they include primarily single-use residential developments organized around a looser grid pattern of local streets, with commercial strips located along highways (see image 4-2) Although pedestrian-oriented development theories associate grid street patterns with active travel, heavy traffic along commercial strips is often described as an obstacle. 87

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Image 4-2: Force elementary school in neighborhood context Source: Google Maps (http : //maps.google.com) S11bin Sabin Elementary (K-5) School is located in southeast Denver and primarily serves Bear Valley, Fort Logan and Harvey Park South neighborhoods, all of which have higher than average White contingencies, average household incomes similar to those of Denver, and lower than average population densities in a suburban residential setting (see table 4-4) 88

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Table 4-4: Demographic profile of Sabin neighborhoods versus City of Denver Neidlborhood %White %Latino %Black %Other Av.lncome Density Bear Valley 70.6 21.5 1.2 5.2 $54,301 --Fort Logan 72.3 20.2 2.0 4.4 $67,922 2,846 Harvey Park (S) 58.8 31.7 1.4 6.6 $54,613 4,585 Average 67.2 24.5 1.5 5.4 $58,945 3,716 Denver 51.9 31.7 10.8 5.6 $55,129 3,617 As indicated in table 4-4, Sabin's neighborhoods have about 15% higher than Denver's average White population, and nearly 1 00/o lower than average Latino and Black populations. They collectively have an average household income similar to the City of Denver. However, Fort Logan's relatively high average balances the lower averages of Bear Valley and Harvey Park. The demographic profile of Sabin Elementary School is inconsistent with the neighborhoods it serves, with 62.1% Latino, 26.5% White, and 3.5% Black students. Also inconsistent with social indicators in those neighborhoods, 44.3% of students are enrolled in the free or reduced lunch program at the school. Sabin's neighborhoods also collectively have roughly the same population density as the City of Denver, but the lower density of Fort Logan is balanced by a higher density in Harvey Park. Population density data were not available for Bear Valley, but the development pattern suggests similar if not slightly lower density than Harvey Park South. These neighborhoods include primarily single-use residential developments organized around a loose, curvilinear grid pattern of local streets, with 89

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commercial strips located at the intersections of major arterials (see image 4-3). Pedestrian-oriented development theories associate these housing densities and street patterns with automobile dependence. Image 4-3: Sabin elementary school in neighborhood context Source: Google Maps (http://maps.google.com) Sl11vens Slavens (K-8) School is located in south Denver and primarily serves University and Wellshire neighborhoods, although the University neighborhood has 90

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far fewer students in elementary school due to the campus location. Both neighborhoods have predominantly White contingencies, but have highly contrasting average household incomes and population densities in contrasting physical settings (see table 4-5). Table 4-5: Demographic profile of Slavens' neighborhoods versus City of Denver Neighborhood %White %Latino %Black Av Income Density University 84.0 6.4 2.4 $48,910 5 242 Wellshire 92.0 4.0 0.9 $103,952 2,455 Average 88.0 5.2 1.7 $76,431 3,849 Denver 51.9 31.7 10.8 $55,129 3,617 As indicated in table 4-5, Slavens' neighborhoods have about a 35% higher average White population than Denver, and very small minority Latino and Black populations. The neighborhoods collectively have an average household income that is significantly higher than the City of Denver. However, a low average household income for University neighborhood balances Wellshire's much higher average household income. The demographic profile of Slavens (K-8) School is consistent with the neighborhoods it serves (given the uneven proportions of students from each), with 89.3% White, 5.7% Latino, and 2.2% Black students. Also consistent with social indicators in those neighborhoods, only 1.5% of students are enrolled in the free or reduced lunch program at the school. 91

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Slavens' neighborhoods also collectively have a slightly higher population density than the City of Denver, but the higher density in the University neighborhood balances the low density ofWellshire. The University neighborhood includes mixed-use and residential developments organized around a tight grid pattern of local streets. In contrast, the Wellshire neighborhood includes primarily single-use residential housing organized around a loose, curvilinear grid pattern of local streets, with commercial strips located at the intersections of major arterials (see image 4-3). In contrast with the highly walkable physical context of the University campus neighborhood, pedestrian-oriented development theories associate low housing density and suburban street patterns like Wellshire's with automobile dependence. 92

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Image 4-4: Slavens elementary school in neighborhood context Source: Google Maps (http://maps.google.com) Program 2: Denver Environmental Health and Transportation Solutions Denver Environmental Health (DEH) partnered with Transportation Solutions (TS) and Stapleton Area transportation management association (STMA), non-profit organizations, for the second program grant. For this grant, DEH designed the program, but representatives from TS and STMA carried it out, making adjustments as needed during the year. 93

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Program 2 expanded the survey of parents by administering walking audits and photo-journals of parents and students regarding barriers to active travel and using the data to create a map of safe pedestrian and bicycling routes in the neighborhood to distribute to parents. Program 2 also encouraged students to walk by offering incentives at special events. It conducted National Walk to School Day in October, and Walking Wednesdays in the latter part of the spring semester. Facilitators presented program plans to parents during PTSA meetings and leveraged parent support for program activities. Program 2 worked with four schools with below-average enrollment that varied widely in demographics and physical neighborhood context: Cory, Bromwell, Steck, Philips, Hallett and Smith. One of the program's goals was to conduct activities in equal numbers of advantaged and disadvantaged schools (based on percentages of minority enrollment and students included in the free or reduced lunch program) in order to compare survey results based on socio-demographic factors. However, the program for the three disadvantaged schools (Philips, Hallett and Smith) got a late start and ultimately failed to complete the activities before two of the schools (Hallett and Smith) closed in 2008. The six schools are located east ofl-25, with the disadvantaged schools in the northern part of the city. 94

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Cory Cory Elementary (K-5) School is located in south-central Denver and primarily serves Belcaro and Cory-Merrill neighborhoods, both of which have predominantly White contingencies with high average household incomes and lower than average population densities in suburban residential settings (see table 4-6). Table 4-6: Demographic profile of Cory's neighborhoods versus City of Denver %White %Latino %Black Av. Income Density Belcaro 94.4 2.5 0.6 $163,553 2.663 Cory-Merrill 88.6 5.9 0.7 $77,254 3 225 Average 91.5 4.2 0.7 $120,404 2,944 Denver 51.9 31.7 10.8 $55,129 3,617 As indicated in table 4-6, Cory's neighborhoods have about 40% higher average White population than Denver, and very small minority Latino and Black populations. The neighborhoods collectively have an average household income that is more than two times higher than Denver. The demographic profile of Cory Elementary (K-5) School is almost consistent with the neighborhoods it serves, but the 79 .I% White student population is relatively low and the, 12.7% Latino, and 3.5% Black student populations are high. Also inconsistent with social indicators in those neighborhoods, 8.4% of students are enrolled in the free or reduced lunch program at the school. 95

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Cory's neighborhoods have slightly lower population densities than the City of Denver. These neighborhoods include primarily single-use residential developments organized around a curvilinear pattern of local streets, with commercial strips located at the intersections of major arterials (see image 4-5). Pedestrian oriented development theories associate these housing densities and street patterns with automobile dependence. Image 4-5: Cory elementary school in neighborhood context Source: Google Maps (http://maps.google.com) 96

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Bromwell Bromwell Elementary (K-5) School is located in central Denver and primarily serves Cheesman Park, Congress Park, Cherry Creek and Country Club neighborhoods, all of which have predominantly White contingencies with high to extremely high average household incomes and high to extremely high population densities in a wide range of physical settings (see table 4-7). Table 4-7: Demographic profile of Bromwell's neighborhoods versus City of Denver Neighborhood %White %Latino %Black Av Income Density Cheesman 79.4 9.2 6.1 $52,866 12,181 Congress 79.1 9.2 0.4 $62 925 7,770 Cherry Creek 90.5 4 1.1 $95 237 ---Country Club 94.1 2.5 0.4 $156,035 ---Average 85.8 6.2 2.0 $91,766 9,976 Denver 51.9 31.7 10.8 $55,129 3,617 As indicated in table 4-7, Bromwell's neighborhoods have about 35% higher average White population than Denver, and very small minority Latino and Black populations. The neighborhoods collectively have an average household income that is nearly twice that of Denver, although the individual neighborhood vary considerably. The demographic profile of Bromwell Elementary (K-5) School is fairly consistent with the neighborhoods it serves, with 80 7% White, 5.2% Black, and 3 7% Latino students, although Latino enrollment is relatively low and Black enrollment is relatively high. Also consistent with social indicators in those 97

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neighborhoods, only 7.4% of students are enrolled in the free or reduced lunch program at the school. Bromwell's neighborhoods have significantly higher population densities than average for the City of Denver. These neighborhoods include single-family homes as well as town homes, high-rise condominiums, apartment buildings and mixed-use developments organized around a tight grid pattern of local streets and arterials (see image 4-6) The high-end Cherry Creek mall and Denver Country Club interrupt those patterns south of 181 A venue. According to pedestrian-orientated development theories that combination of qualities excepting the mall and country club should coincide with higher than average rates of active travel, and attitudes consistent with active travel behavior. However, the school's proximity to several major arterials, University Avenue, 1st Avenue and 61h Avenue, potentially hinders active travel for some families. 98

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Image 4-6: Bromwell elementary school in neighborhood context Source: Google Maps (http://maps.google.com) Steck Steck Elementary (K-5) School is located in east-central Denver and primarily serves Congress Park, Belcaro, Cherry Creek, Hale and Hilltop neighborhoods, overlapping with Bromwell Elementary's catchment area. Similar to Bromwell, Steck's neighborhoods have a predominantly White contingency with high to extremely high average household incomes, but they have a wider range of population densities and physical settings (see table 4-8). 99

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Table 4-8: Demographic profile of Steck's neighborhoods versus City of Denver Neighborhood %White %Latino %Black Av.lncome Density Congress 79.1 9.2 0.4 $62,925 7,770 Belcaro 94.4 2.5 0.6 $163,553 2,663 Cherry Creek 90.5 4.0 1.1 $95,237 n/a Hale 94.4 2.5 0.6 $54,830 5,872 Hilltop 88.8 3.5 3.6 $117,835 3 862 Average 89.4 4.3 1.3 $98,876 5,042 Denver 51.9 31.7 10.8 $55,129 3,617 As indicated in table 4-8, Steck's neighborhoods have about 40% higher White population than Denver, and very small minority Latino and Black populations. The neighborhoods collectively have an average household income that is nearly twice that of Denver, although the individual neighborhoods vary considerably. The demographic profile of Steck Elementary (K-5) School is mostly consistent with the Congress Park neighborhood, with 76.7% White, 9.6% Latino, and 7.5% Black students, since the other neighborhoods have higher percentages of White residents. Also consistent with social indicators in Congress Park, 10.6% of students are enrolled in the free or reduced lunch program at the school. Steck's neighborhoods range from very low population densities to significantly higher population densities than average for the City of Denver. These neighborhoods include single-family homes as well as town homes, apartment buildings and mixed-use developments organized around a grid pattern of local streets and arterials (see image 4-7). According to pedestrian-orientated development 100

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theories that combination of qualities should coincide with higher than average rates of active travel, and attitudes consistent with active travel behavior. However, the school's proximity to a major arterial, Colorado Avenue, potentially hinders active travel for some families. Image 4-7: Steck elementary school in neighborhood context Source: Google Maps (http://maps.google.com) 101

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Philips Philips Elementary (K-5) School is located in east-central Denver and primarily serves North Park Hill and South Park Hill neighborhoods, overlapping with Hallett Elementary's catchment area. Both of Philips' neighborhoods have sizeable Black contingencies, moderate to high average household incomes, and higher than average population densities in an urban residential setting (see table 4-9). Table 4-9: Demographic profile of Philips' neighborhoods versus City of Denver Neighborhood %White %Latino %Black Av. Income Density N Park Hill 27.6 10.9 56.0 $58,392 5 198 SPark Hill 74.7 8.1 12.8 $88,479 4,440 Average 51.2 9.5 34.4 $73 436 4,819 Denver 51.9 31.7 10.8 $55,129 3,617 As indicated in table 4-9, Philips' neighborhoods collectively have lower than average Latino and higher than average Black populations. Individually, however, North Park Hill has a much stronger Black contingency and South Park Hill has a stronger White contingency. The neighborhoods collectively have an average household income that is significantly higher than Denver, although the individual neighborhoods vary considerably. The demographic profile of Philips Elementary (K5) School is inconsistent with the neighborhoods it serves, with 83.4% Black, 13.00/o Latino, and 3.5% White students. Also inconsistent with social indicators in those neighborhoods, 84.0% of students are enrolled in the free or reduced lunch program at the school. 102

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Philips' neighborhoods have significantly higher population densities than average for the City of Denver. The neighborhoods include primarily single-use residential developments organized around a loose grid pattern of local streets (see image 4-8). According to pedestrian-orientated development theories that combination of qualities should coincide with moderate rates of active travel. However, the school's proximity to a major arterial, Monaco Parkway, potentially hinders active travel for some families. Image 4-8: Philips elementary school in neighborhood context .. .,. .. " -;; ..... -r ... . _,. -. .... . .. I . ...... -. ';: "\"'; .... ,.;:--, < ...--; ... ..i -lo ... .. "' ... ,__-. .. ) -c. -, ... ..... . :-a .. --' . ,.. .. . --. .. ... "l ....... --------. .. ...... -I i r -... ..... 31 -;, \. : .... . .-:;,7:=- ..,._ .. < .. ,l...o ---::.:::.. ___ \. --1 : -Z" -:" . ., L Jl.' ... I J l(i < .... ..: .. ... -_ ... ... .<{' _I. -. ... ... .I !J ,. "' ...:: __ .... ..J. _,. ..l-"J: ---f--1 J p ----.... ( .. ) ....... ..... :,..f._. ., .., "' ./ 'I '"':Jj --..... -l;\)\ )k Source: Google Maps (http://maps.google.com) 103

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Hallett and Smith Hallett and Smith Elementary (K-5) Schools are located in northeast Denver and primarily served North Park Hill and Northeast Park Hill neighborhoods until their closures in 2008. Both neighborhoods have sizeable Black contingencies, low to moderate average household incomes, and higher than average population densities in an urban residential setting (see table 4-1 0). Table 4-10: Demographic profile of Hallett and Smith's neighborhoods versus City of Denver Neighborhood %White %Latino %Black Av. Income Density N Park Hill 27.6 10.9 56 $58,392 5,198 NE Park Hill 4.7 23. 8 68 5 $37,468 1,329 Average 16.15 17.35 62.3 $47,930 3,264 Denver 51.9 31.7 10.8 $55,129 3,617 As indicated in table 4-10, these neighborhoods have about 50% higher Black population than Denver, and much smaller than average White and Latino populations. The neighborhoods collectively have an average household income that is on par with Denver, although the individual neighborhoods vary considerably. The demographic profile of Hallett Elementary (K-5) School is not entirely consistent with the neighborhoods it serves, because the 70.8% Black and 27.2% Latino student populations are relatively high and the .2% White student population is low. However, consistent with social indicators in those neighborhoods, 79.6% of students are enrolled in the free or reduced lunch program at the school. 104

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The demographic profile of Smith Elementary (K-5) School is reasonably consistent with the neighborhoods it serves, with 50.3% Black students. However, the 49 0% Latino student population is relatively high, and the .8% White student population is low. Consistent with social indicators in those neighborhoods, 86.0% of students are enrolled in the free or reduced lunch program at the school. These neighborhoods have significantly higher population densities than average for the City of Denver, although the density of Northeast Park Hill appears very low due to the golf course in the northwest portion of the neighborhood and the light industrial zone to the north The neighborhoods include mixed-use and residential developments organized around a tight grid pattern of local streets (see image 4-9). According to pedestrian-oriented development theories that combination of qualities should coincide with higher than average rates of active travel, and attitudes consistent with active travel behavior. However, the schools' proximity to major arterials, Martin Luther King Boulevard and Monaco Parkway, potentially hinders active travel for some families. 105

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Image 4-9: Hallett and Smith elementary schools in neighborhood context Source: Google Maps (http://maps.google.com) Program 3: Denver Health and DPS Rides Denver Health teamed with DPS Rides, a non-profit organization, for the third program grant. A representative from Denver Health planned the intervention and worked together with 2-3 representatives of DPS Rides to carry it out. In addition to the required tallies of student travel modes and surveys of parents, Program 3 emphasized a health education component in its intervention. It included teaching brain injury prevention and empathy seminars, conducting bike 106

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safety courses during gym classes, and providing free bike helmets for students in all grades. To encourage students to walk, Program 3 organized a walking and wheeling week during the spring semester with a two-hour event and school safety walk at each school. Program 3 worked with two schools with above-average enrollment that differed significantly in demographics and physical neighborhood context: Lowry and Valdez. The two schools were also located in opposite quadrants of the city. Lowry Lowry Elementary (K-5) School is located in southeast Denver and serves the Lowry Field neighborhood, which has a similar demographic profile to Denver, slightly higher average household income and much lower than average population density in a New Urban setting (see table 4-1 1). Table 4-11: Demographic profile of Lowry's neighborhoods versus City of Denver %White Av. Income 55.8 $69 034 Denver 51.9 31.7 10.8 $55,129 3,617 As indicated in table 4-11 Lowry's neighborhood has roughly the same White population as Denver, with much lower Latino and slightly higher Black populations. The neighborhood has an average household income about 25% higher than Denver. The demographic profile of Lowry Elementary (K-5) School is nearly consistent with 107

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the neighborhoods it serves, with 52.4% White, 28.8% Black, and 16.6% Latino students, although the percentage of Black students is relatively high. Also consistent with social indicators in those neighborhoods, only 40 9% of students are enrolled in the free or reduced lunch program at the school. Lowry Field neighborhood technically has a far lower population density than the City of Denver. However, the low density is primarily due to the golf course and undeveloped space on the eastern side of the development. The neighborhood includes single-family homes as well as town homes, high-rise condominiums, apartment buildings and mixed-use developments organized around a curvilinear pattern oflocal streets (see image 4-10). Pedestrian-oriented development theories associate this type of land use and street pattern with walkability. 108

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Image 4-10: Lowry elementary school in neighborhood context Source : Google Maps (http://maps google.com) Valdez Valdez Elementary (K-5) School is located in northwest Denver and primarily serves Highland and Jefferson Park neighborhoods, both of which have sizeable Latino contingencies, lowto mid-range average household incomes, and higher than average population densities in an urban residential setting (see table 4-12). 109

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Table 4-12: Demographic profile of Hallett's neighborhoods versus City of Denver Neilzhborhood %White %Latino %Black Av. Income Density Highland 29.4 66.8 1.4 $39,568 6,932 Jefferson Park 14.1 82.6 0.9 $43,088 --Average 21.75 74.7 1.15 $41 328 6,932 Denver 51.9 31.7 10 8 $55,129 3,617 As indicated in table 4-12, Valdez's neighborhoods have over double the average Latino population of Denver, lower than average Black population, and have lower average household income than Denver. The demographic profile of Valdez Elementary (K-5) School is inconsistent with the neighborhoods it serves, with a predominantly 95.4% Latino student population, and very small minorities of White (2.3% ), and Black ( 1.1%) students. Consistent with social indicators in those neighborhoods, 87.3% of students are enrolled in the free or reduced lunch program at the school. The neighborhoods also have roughly twice the average population density of Denver, and include mixed-use and residential developments organized around a tight grid pattern oflocal streets (see image 4-11). According to pedestrian-oriented development theories, that combination of qualities should coincide with higher than average rates of active travel, and attitudes consistent with active travel behavior. However, the school's proximity to several major arterials, including Speer Boulevard and Federal Boulevard, potentially hinders active travel for some families. 110

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Image 4-11: Valdez elementary school in neighborhood context Source: Google Maps (http://maps.google.com) Program 4 (Alternate): Children Youth and Environments Center for Research and Design (CYE) Following the qualitative phase of research, I selected one school as an alternate to replace two predominantly Latino schools (Hallett and Smith) that closed after the 2007-08 school year. This school is included in the quantitative data collection and analysis, presented in Chapters Six and Seven. 111

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The alternate, Munroe Elementary School, completed an SR2S noninfrastructure project in Fall2007, facilitated by the University of Colorado's Children, Youth and Environment's Center for Research and Design. CYE emphasized child and community engagement during a thorough site assessment. The program tallied students' travel modes for one week, administered walking audits with parents at the beginning of the school year regarding barriers to active travel, conducted a traffic study at the school entrances, and worked with OWL students to conduct paper and digital mapping exercises with the fifth grade classes. Program facilitators coordinated activities with a committee of school staff and parents. Munroe Munroe Elementary (K-5) School is located in west-central Denver and primarily serves Athmar Park, Valverde and Westwood neighborhoods, all of which have higher than average Latino contingencies, lower average household incomes, and average to higher than average population densities in a suburban residential setting (see table 4-13). Table 4-13: Demographic profile of Munroe's neighborhoods versus City of Denver Neighborhood %White %Latino %Black Av Income Densi!Y_ Athmar Park 29.5 65.2 0.8 $47,932 4,403 Valverde 18.2 74.6 2.5 $35,918 3,272 Westwood 16.9 76.0 1.2 $37,961 7,724 Avemge 21.5 71.9 1.5 $40,604 5,133 Denver 51.9 31.7 10.8 $55,129 3,617 112

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As indicated in table 4-13, Munroe's neighborhoods have over double the average Latino population of Denver, lower than average Black population, and have lower average household income than Denver. The demographic profile of Munroe Elementary (K-5) School is inconsistent with the neighborhoods it serves, with a predominantly 94.6% Latino student population, and very small minorities of White (2.0%), and Black (1.0%) students. Consistent with social indicators in those neighborhoods, 74.8% of students are enrolled in the free or reduced lunch program at the school. Munroe's neighborhoods collectively have a much higher population density than the City of Denver. The low density of Valverde contrasts with much higher densities in Athmar Park and Westwood only because the eastern portion of the development is zoned for commercial use. The neighborhoods include primarily single-use residential developments organized around a grid pattern of local streets, with commercial strips located along highways, and particularly along Interstate 25 (see image 4-11). Although pedestrian-oriented development theories associate grid street patterns with active travel, heavy traffic along commercial strips is often described as an obstacle. 113

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Image 4-11: Munroe elementary school in neighborhood context Source: Google Maps (http://maps.google.com) Data Collection G11ining Entry in Progr11ms To gain entry into the research sites, I participated in each of the three Denver Safe Routes to School programs and in Denver's SR2S Coalition during the 20072008 school year. I volunteered with Programs 1-3 by taking notes at planning and PTSA meetings and lending a hand during classroom bike and pedestrian safety 114

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instruction and activities at schools that had weak parent involvement. I volunteered with the Education Subcommittee of the Denver SR2S Coalition and assisted the group by sharing information from current SR2S programs, recording and sharing meeting transcriptions, developing a framework for the planning process, and presenting the subcommittee's findings to the coalition. I also served as a research assistant for the alternate program's site assessment in spring 2007. Participating in the SR2S programs at each school allowed me to observe variations in active travel intervention based on the objectives of the facilitating partner organizations and based on differences in contextual conditions. It also provided me with access to key informants that included staff and parents from each school. Recruitment Procedures I recruited parents for interviews by contacting them in person during drop off and pick up times at school entrances in February 2008. I approached parents at their cars or on the sidewalk, briefly introduced myself and the research project and asked them if they would participate in an interview either right away or within the next few weeks. The recruitment process resulted in 64 interview respondents, whose characteristics reflect the larger population of parents in their gender, travel mode, distance, number of children and racial/ethnic backgrounds (see Appendix A). 115

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Contacting parents on school grounds strongly influenced data collection. Because district-wide school policy prohibited me from using enrollment lists with contact information, I could not directly contact parents at their homes. The schools sent information to parents each week in students' folders, but I expected to have more success if I recruited using face-to-face introductions. Although several parents agreed to be interviewed right away, the majority gave their names, phone numbers and/or email addresses so that a member of the research team could contact them. A large portion of the initial contact list later declined the interview, so the process resulted in an uneven distribution of respondents across schools and possibly across ethnic backgrounds as well (see table 4-14). For example, only one parent from Force Elementary School's contact list ultimately agreed to an interview. Although that respondent was a Latina, consistent with the school's enrollment, the poor response rate at that school lowered the proportion of Latino interviews ( 19%) relative to the proportion of Latino students at the twelve participating schools (35.9%). However, because Latino respondents typically reported having 3-4 children compared to the White parents' 1-2 children, the proportions may more accurately reflect the demographics of parents than of students (see Appendix A). I consider this issue in more detail in my discussion of response rates in Chapter Six. For the purposes of this research, the response rates adequately capture the ethnic diversity of the larger parent population. More importantly, I continued data collection until the responses became so predictable that I felt they had reached saturation. 116

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Table 4-14: Proportions of interview subjects at each school by gender and race -4.1 :a: -; 4.1 Q ... -4.1 .... .!3 4.1 -; E CJ .... ell -= .... School :5 = 0 Bromwell 3 0 3 0 0 0 3 Cory 5 4 9 0 0 0 9 Edison 6 3 6 2 0 1 9 Force 1 0 0 1 0 0 1 Hallett 4 2 1 1 4 0 6 Lowry 1 I 2 0 0 0 2 Philips 7 1 1 1 6 0 8 Sabin 3 0 1 2 0 0 3 Slavens 8 I 9 0 0 0 9 Smith 4 I I 3 I 0 5 Steck 5 1 6 0 0 0 6 Valdez 2 1 0 2 1 0 3 Total(#) 49 15 39 12 12 I 64 Total(%) 77 23 61 19 19 2 100 The disproportionate number of female respondents may be explained in several ways. First, consistent with Hillman (Hillman, Adams et al., 1990), women more often take responsibility for their children's transportation to and from school. Because I contacted respondents during drop off and pick up times, I was more likely to encounter mothers. However, some schools (i.e. Cory and Edison) appeared to have stronger involvement of fathers in the commute, so the proportions are not consistently low. In many cases, one parent dropped children off and the other picked them up. Depending on their household schedules, I was more likely to encounter one 117

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of the two parents. In other cases, both parents were present in the car, but only one participated in the interview. As a result, the proportions of respondents may not accurately reflect the involvement of each parent in the commute. That said, I conducted a sufficient number of interviews with men and women to ensure that gender-relevant issues would be included in the results. Meetings and Focus Groups As a participant in the SR2S programs, I observed, recorded and transcribed meetings and events to learn each organization's plans for intervention, to find out how the plans were carried out, and to learn how the funding and implementation process influenced intervention (see Appendix B). In a few instances, scheduled meetings or events overlapped and I relied on a colleague to observe and record them on my behalf Data include transcripts from 22 planning meetings, including 7 meetings for the SR2S Coalition, 9 School Traffic Safety (STS) Committee meetings for Program 1, 3 planning meetings with school principals for Program 2, and 3 PTSA meetings for Program 2. During several meetings for Programs 1 and 2 and Denver's SR2S Coalition I had the opportunity to facilitate impromptu focus-group discussions centered on the question, "why do you think parents drive their kids to school?" Those discussions guided the development of my interview protocol, described below (see Appendix C). Additional data include notes, recordings and photographs of 25 118

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events, including 11 bicycle/pedestrian safety presentations and bicycle activities for Program 1, 12 safety presentations and bicycle activities for Program 3, and 2 special walking events for Program 3. Interviews I conducted semi-structured interviews at each of the twelve participating schools to capture the main conversational themes about school travel for a sorting exercise discussed in Chapters Six and Seven. The interviews provided insight into parents' perceptions of the school commute, and factors that influence their travel mode choices. The interview protocol described my research succinctly as "a study of parents' experiences of the school commute", and included clusters of questions relating to four topics: (1) decisions about the trip to school, (2) companionship on the trip to school, (3) mode of travel on the trip to school, and (4) changing commuting behavior. Interviews approached the topic of school travel directly, but withheld discussion of active travel as a policy goal until the end to encourage respondents to answer openly and comfortably. I directed all interviewers to pose questions from the protocol and to use other probes as needed to direct the conversation. However, I instructed them to avoid questions that would pass judgment on or lead respondents to simply confirm or reject interviewer-defined explanations for their travel behavior. For example, the question "what kinds ofthings influence decisions about your 119

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child's trip to school?" allowed parents to describe any issues they deemed relevant to the school commute. In contrast, the question "what obstacles prevent you from walking your child to school?" would imply that active travel is preferable to driving, and could lead respondents to withhold explanations. The open questions also allowed parents to describe positive and/or negative explanations for their decisions, rather than assuming that their choices necessarily respond to obstacles that prevent active travel. Questions about commuting decisions, companionship and travel mode provided insight into parents' travel behavior from their point of view. However, those questions implied typical behavior and might have obscured day-to-day variations. To clarify, interviewers invited respondents to describe trips that they made in the past three days including destinations, distance, travel time and vehicle used. The retrospective travel diary helped parents to focus on specific trips and to describe their responses to day-to-day conditions. Questions about changing commuting behavior gave respondents the opportunity to speculate about conditions that might influence them to walk or bike more often than they currently do. That part of the interview introduced an active travel policy objective in some cases, but did not present specific elements of intervention. Rather, during that part of the discussion respondents tended to clarify day-to-day variations in their travel behaviors, and to explain why their typical plans were sometimes thwarted. 120

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Interviews lasted between five and forty-five minutes and were digitally recorded and transcribed. I conducted most of the interviews personally, and took observational notes while voice recording the interview with a hand-held digital recorder. After each interview I recorded additional observations about the context of the interview, the respondents' body language, and other characteristics of the person and his or her family. I also trained a team of undergraduate students to conduct, transcribe and expand additional interviews. The students completed one interview each as part of a class project for ENVD 3001Environment and Behavior. Several student researchers conducted interviews in Spanish, and translated the transcripts to English. The Human Subjects Research Committee approved the students' participation in the research and the larger research project based on certain conditions. Because the nature of questioning was sufficiently harmless, the Committee permitted researchers to obtain consent verbally during the initial contact and at the start of interviews However, during transcription researchers replaced the subjects' names with pseudonyms to protect their privacy The pseudonyms also appear in the presentation of findings in Chapter Five and in Appendix A. I did not attempt to conceal the names or locations of the twelve school sites, because the schools' participation in 2007-08 SR2S non-infrastructure programs is a matter of public record, and because the physical context and location of the schools is integral to parents' attitudes about mode choice. 121

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Analysis and Interpretation The two central objectives of this qualitative research were to capture key discursive themes relating to the school commute for later use in a sorting exercise and to characterize and compare parents' attitudes about mode choice for school travel with active travel program facilitators' expectations about how their interventions might influence travel behaviors. The analytical approach that I used to achieve those objectives most resembles the "constant comparative method" prescribed by Glaser and Strauss (1967). Using the constant comparative approach, a researcher analyzes transcripts by applying code words or phrases to incidents as they are encountered in repeated readings of the text. Miles and Huberman (1994) argue that this process of reductionism represents one of several methodological possibilities for qualitative data analysis, and that it is most appropriate for anthropological or otherwise ethnographic research in which the researcher relies heavily on observing and recording patterns and anomalies in social interactions. In contrast, describe phenomenology as relying more heavily on interview transcripts to uncover the essence of the subjects' account, and the meaning of the studied behavior (Miles & Huberman, 1994). My research includes element of both approaches because it involves investigating patterns of attitudes and behaviors regarding active travel that must be understood from the perspective of the actors themselves and not from first hand observation of social behaviors. 122

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Although my analysis involved looking for patterns within the text of interview transcripts, I ultimately compared incidents by sorting the electronic text into thematic clusters rather than applying codes to individual incidents. My sorting process provided an equivalent to the "open coding" which is the first step in grounded theory (Glaser & Strauss, 1967). However, whereas the open coding process typically establishes a few categories as the highest level of the coding hierarchy, my sorting process established many codes as the lowest level of the hierarchy. To facilitate the sorting process, I first condensed the data by highlighting statements within the full transcripts that explained travel mode choices and by creating a separate report of those relevant incidents. A research assistant repeated the coding process and we compared results to ensure reliability in our interpretation. The constant comparative method can be employed using qualitative analysis software or can be performed manually with printed or electronic transcripts by noting codes in the margins. Miles and Huberman (1994) argue that computer-aided analysis provides necessary structure for managing large data sets, and for developing and revising codes. I used a combination of these methods initially condensing the text electronically, using QSR Nvivo software and sorting the statements manually to define a coding structure. I found that QSR Nvivo software benefited the initial process of condensing data because I could produce the report while maintaining the link to the full interview transcripts However the second stage of computer-aided coding was problematic because it encouraged me to predefine coding themes. 123

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According to Glaser and Strauss (1967), codes may be inspired by the text, and/or may be drawn from external theoretical sources, depending on the research objectives. For example, research that aims to test hypotheses about relationships between certain variables would begin with an externally-defined coding structure. In contrast, grounded theory requires the coding structure to emerge from the data during analysis. In either case, coding themes either emerge or are applied as the researcher encounters multiple incidents of similar content. The researcher keeps track of coding themes, organizing and editing them to capture the properties of the text. Because my goal was to explore perceptions of the school commute, I allowed codes to emerge from the content of the transcribed interviews and meetings. Thirty-six initial coding categories emerged from my sorting of the condensed transcript text (see table 4-15). I constructed titles for some ofthe themes to capture the properties of the clusters of incidents and borrowed some titles directly from the language of the data. In the second and third stages of analysis, I repeated the coding process using QSR Nvivo software, first by recoding with the thirty-six themes, then grouping those themes into hierarchical trees with similar properties. Table 4-15 shows the hierarchy of codes that emerged during the three stages of analysis. Specific relationships between thematic groups are explained in greater detail in Chapter Five. 124

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Summary In this chapter, I presented the qualitative methods that I utilized in the first phase of my larger dissertation research project. The purpose of this qualitative study was twofold. First, it was to examine SR2S active travel interventions (including design and execution) and how their facilitators expect to increase numbers of active trips to school. Second, it was to learn from the perspective of elementary school students' parents why they choose certain travel modes for escorting their children to and from school. The relationship between those perspectives is critical for designing effective active travel intervention. I examined the three Safe Routes to School non-infrastructure programs that were awarded grants in Denver during the 2007-2008 school year. Although the programs completed certain tasks required by the Safe Routes to School grants that they received, their specific interventions reflected the facilitating organizations' missions The three programs operated in a total of twelve public elementary schools in the city of Denver, representing a wide range of socio-demographic groups and neighborhood contexts 125

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Table 4-15: Hierarchy of coding themes Initial Coding Categories Secondary Clusters Emergent Themes I. Inclination 2. Parking Routine 3. Traffic congestion 4. Multitasking 5 Rushing/ Being on time Protecting Slowness Life Pace 6. Schedules 7. School meetings/ practices Habit 8. Early mornings 9. Sleep IO. Age II. Ability Responsibility and 12. Trust/ Obedience 13. Accompaniment Maturity I4. Neighborhood children I5. Trusted neighbors I6. Activity Developmental Nurture I7. Disease Opportunities I8. Exercise I9. Bonding Time 20.Fun 21. Instruction 22. School Choice School Requirements 23. Academics 24. Materials 25. Bad drivers 26. Busy streets Traffic Dangers 27. Dangerous crossings 28. Traffic enforcement 29. Crime Context 30. Strangers/ Transience Social Danger 31. Bullies 32. Dogs 33. Bus Service 34. Car Access/ Carpool Transportation 3 5. Gas prices Options 36. Pollution 126

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Next, I discussed school travel with parents of elementary school children. Although children actively influence decisions that impact their lives, parents bear legal responsibility for their children's welfare and ultimately have the authority to make commuting decisions. It was therefore appropriate that I should interview parents about their experience of the school commute. I recruited 64 parents from twelve Denver public elementary schools to participate in interviews, based on the schools' participation in the three Safe Routes to School non-infrastructure programs. The purposive sample of parents reflected the larger population of parents from the twelve schools in their gender, travel mode, distance, number of children and racial/ethnic backgrounds. In contrast with extant research regarding school travel, I explored parents' school commuting experience without assuming that their travel choices result from barriers that prevent more desirable, active options. Semi-structured interviews served that purpose well, because they allowed respondents to explain their travel preferences without being led to defend or excuse their choices or to explain them by selecting from a list of predefined factors. Since I ultimately aim to produce a typology of attitudes about school travel, I used a constant comparative approach in the anthropologists' reductionist tradition to analyze transcript text and to identify a wide range of thematic categories. After initially coding the transcripts to produce a report explaining commuting choices, I coded by sorting incidents from the report into 36 thematic groups, and then sorting 127

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those groups into hierarchical trees with three main headings: life pace, nurture and context. Informants and colleagues reviewed the coding, allowing me to confirm and adjust the themes that I had identified in my analysis The categories that emerged through my analysis provide the framework for the presentation of findings in Chapter Five, and contribute to the design of a sorting exercise used in the quantitative study described in Chapters Six and Seven. 128

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CHAPTERS: QUALITATIVE FINDINGS Introduction In response to a decline in active school travel, planners have investigated reasons that parents choose to drive their children to school rather than walk or ride bicycles and have found a variety of environmental socio-demographic and psychological factors significant to travel mode choices. As I explained in Chapter Three, planning research primarily identifies environmental obstacles that prevent or discourage walking. I use the term 'environmental' to include physical environmental elements, such as discontinuous sidewalks and busy intersections as well as social environmental elements, such as real and perceived stranger danger. Planning research guides policy to eliminate those and other environmental obstacles in order to improve the opportunity to use active travel modes. Researchers investigating school travel largely interpret and reduce findings to identify the most significant obstacles to active travel diminishing the significance of deviation from the norm. That interpretation implies that students and their parents would prefer walking to driving if opportunity allowed. By making that assumption, researchers presume that parents are homogeneous in their attitudes about the school commute which suggests that a single intervention approach would suffice to encourage walking 129

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However, some researchers take a closer look at variations in travel behavior and/or perceptions of the school commute based on socio-demographic variables such as race, gender, age and employment characteristics. They find that a variety of personal factors mediate the influence of environmental factors (Bemetti, Longo et al., 2008; McMillan, 2005). In short, they find that people respond differently to similar opportunities and seek to explain why that is so. The purpose of my research is to explain how attitudes differ between people so that active travel programs can tailor intervention to specific target groups and thereby magnify their impact on commuting behavior. To that end, it is necessary for me to examine perceptions of the school commute without assuming that parents value active travel over driving or vice versa. In Chapter Four, I outlined the qualitative methods that I used in the first phase of my research, which involved recording SR2S planning meetings and interviews with parents of elementary school students, followed by content analysis of transcripts. In this chapter, I present the findings from that qualitative study, organized around the three major themes that emerged from the data. Major Themes The analysis that I introduced in Chapter Four resulted in three broad coding themes that I titled "Life Pace," "Nurture," and "Context" (see table 4-15). On the surface, each of the themes is consistent with findings from extant research, and 130

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includes environmental, socio-demographic, and psychological elements. However deeper examination of the transcript text shows that perceptions and attitudes of each theme differ considerably, representing a wide range of experiences with the school commute. Below, I present each theme in the context of behavioral research, and then highlight areas in which my data show distinction. For a more detailed review and discussion of the literature on these topics, see chapter Three. The examples that I use to illustrate each point come from transcribed text of interviews with parents (see Appendix A) and SR2S planning meetings (see Appendix B). Theme 1: Life Pace Studies show that parents associate school travel decisions with the pace of their daily lives. For example, Pooley (2005) learned from interviews across several generations in England that people perceive life pace to be increasing and that travel by private automobile accommodates contemporary, more hectic household schedules. Several studies indicate that household scheduling considerations in particular largely determine travel mode decisions for school trips (Bradshaw, 1995; Collins & Kearns, 2001; Crane, 1996; Jones, Dix et al., 1983 ). Some researchers contend that the private automobile serves practical purposes, affording convenience and allowing parents to multi-task by dropping off or picking up their children en route to work or errands (Bradshaw, 1995; Collins & Kearns, 2001; Hupkes, 1982) Other researchers argue that it affords parents a sense of control over path and 131

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schedule and serves as a psychological coping mechanism for a rapidly changing environment (Breznitz, 1980; Harvey, 1989; Reser, 1980; Sorkin, 1992). In the discussion of life pace, a common thread is a sense that people currently struggle to accomplish more activities in less time. My interviews with Denver parents similarly reveal concern about life pace and household scheduling, but suggested that those issues influence travel decisions in more nuanced ways for example, occurring as a part of their typical daily routine or changing in response to disruptions in the typical routine. Further, interviews revealed that the concern for life pace included a desire to protect slowness in addition to the effort required to accommodate hectic schedules. In this section, I present several sub-themes that reveal parents' nuanced experiences of life pace. Routine Respondents described travel mode choice as one element of a complex, albeit typical daily routine. They explained that their choice of travel mode helps them to accommodate early mornings, school, work and extracurricular schedules for multiple household members, which suggests a cost-benefit decision-making strategy. In the following examples, Colleen and Janice both associate their travel routines with their effort to get the kids to school on time, although Colleen typically walks or drives her child to school while Janice's child walks to the bus stop. A parent volunteer at 132

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Edison explains that she and other parents drive primarily because they are on their way to work. Mostly time, if we are on time, or running late. (Colleen, Interview, #BR6) It makes things a lot easier, I don't have to worry about getting him to school on time; I just have to worry about getting him out the front door on time (Janice, Bromwell PTSA, 1 1-16-07) I think there are a lot of parents here that are working, and so they drive for the fact of where they are going, not the fact of dropping their kids off I'm an example of that I live two blocks away and my kids walk to school by themselves. But if I have this meeting, I'm driving because I don 't have time to run and get my car and go back. I think that's the biggest reason that people drop off (Katie Edison STSC, 9-18-07) The desire to save time can encourage parents to violate traffic and pedestrian safety laws. For example, Pauline describes one location in which the site layout encourages jaywalking because observing safety rules takes extra time. And one thing that we always have to remember ... kids will take the shortest path, and so that's something that we do as adults as well ... even though there 's a sign there that says go to the corner to cross, there's a continuous stream of people running across the street in the middle of the block because ... it's the shortest path. (Pauline, Coalition, 1 -23-08) Some parents attribute lasting value to the routine quality of the journey to school. In the following example, Shawna describes the potential of her children's 133

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walking routine to evoke positive memories of childhood and of their relationship later in their lives It also establishes sort of a ritual for a child, especially the times he would meet his step-brother and step sister under the flagpole to walk home with them .from school ... and that's something that they'll never forget, you know. It's a ritual. So there are some definite advantages to allowing a child to have that freedom to walk home with his siblings or friends. (Shawna, Cory PTSA, 4-4-08) planning for disruption. Parents explained that any changes in their travel behavior would require additional planning, and implied that the familiarity of their typical mode is an important determining factor in their decision-making In the following examples, Alexis, Kerry and Mary describe the changes that they would need to make in order to accommodate active travel. Mary describes a loose hold on her afternoon plans and the resulting need to rush to school during pick up time All three respondents associated their explanations with the need to drive. We leave between ten of and a quarter of 8:00, so we would have to leave at 7:30 which means that it would have to put them to bed earlier, get up earlier, get more organized. (Alexis, Interview, #SL9) I mean you just have to ... maybe more time frame because it takes a little more time to go on a bicycle. It 's more planning initially. (Kerry, Interview, #LR6) I work at home and I just get involved in what I'm doing and I remember to get them a couple minutes before ... (Mary, Interview, #EDO) 134

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In the following examples, Kami and Cathy express reluctance to adjust an established morning routine because it would take time away from other priorities. However, neither parent associates her explanation with driving. If me [sic} or my husband had to start driving my son to school it would take time away from us and we would have to adjust our schedules. (Kami, Cory PTSA, 4-4-08) Okay, if the bus environment became bad. In other words, If kids start acting poorly on the bus, or if there were bullies, or if the bus driver was never punctual and so the child was late to school every day. (Cathy, Sabin STSC, 9-11-07) While some parents take more time to think about their travel mode decisions than others, commuting routines may become entrenched because of their familiarity. Intervention for parents in this situation would require an uncomfortable disruption and could encourage active travel by mitigating for that discomfort. unexpected disruption. Parents described some variation in travel preferences that result from unexpected or unusual disruptions of their typical or desired daily routines. Obstacles such as illnesses or injuries, missed wake-up alarms slow dressing, forgotten homework, poor weather and other circumstances could disrupt their typical routine In the following examples, Jackie describes the chaos that followed the start of daylight savings time. Robin, Amy and Ernie describe other disruptions to their morning routines that require a change of plans. 135

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Since daylight savings happened, everyone is running late, sort of crazy. I know it's an hour earlier but people are still staying up an hour later. Anyway, just the whole ... we're tired. We're all driving like crazy, got real dangerous that week. We almost had somebody run a kid down again. (Jackie, Coalition-sub, 11-15-07) If they were sick and didn't go. Or if I was sick and I needed someone to take them to school, and someone came by to pick them up, because that is probably the only time ... [that it would change} ... because I would have needed help. (Robin, Bromwell PTSA, 11-16-07) ... if our car's broken, we walk home, which has happened a few times. (Amy, Bromwell PTSA, 11-16-07) Only when the weather is bad, and if there is an issue I need to be at school with them. You know, a meeting ... I'll go with them, and meet with a teacher to find out what they want to talk about. But other than that they basically walk everyday by themselves. (Ernie, Interview, #ED3) In contrast, many parents expressed firmness in their commuting behavior, and suggested that few circumstances, including weather, would disrupt their routines. Even if it is snowing buckets we make them walk, ha ha, even in a blizzard they walk. It isn't a big deal. (Nancy, Interview, #SL4) On any weather days ... I just don 't like them walking to school period. (Monty, Interview, #PHI) As illustrated by the contrasting views of Nancy and Monty, the degree of parents' fluidity in the morning routine did not correspond to a specific travel mode. 136

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comfort with change. Parents expressed varying degrees of comfort with flexibility and change in their daily commute to school. In the following examples, Mary and Georgia describe conditions that influence them to change their typical mode of travel on a moment's notice. In contrast with Mary's expression of remorse for failing to walk to school, Georgia describes a more comfortable, but similarly fluid decisionmaking process. I'm half a block from here and I drove ... I should walk. I should walk but I drove because we were going to go to the store afterwards. (Mary, Interview, #EDO) Depends on the weather, what's happening on that day or during the week, or what I feel/ike at that point in time (Georgia, Interview, #CR8) Georgia's description of a fluid decision-making process indicates a higher level of comfort with change, and suggests that day-to-day variations are anticipated and even welcomed. Intervention for parents in that situation could leverage modal flexibility and encourage additional walking trips by introducing incentives for continuous active travel. Slowness Some parents emphasized the need to take time in the morning, either to sleep or to set the pace for the day. In the following examples, Alicia, Debbie and Lyssa 137

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describe the decision to drive so that their children can take a more leisurely pace with their morning routine. Although Alecia' s son might find additional time by waking up earlier, 'sleeping in' is integral to the family's sense of pace. Debbie and Lyssa similarly describe protecting their children from having to wake up any earlier than necessary. All three parents choose to drive rather than take advantage of the school bus that is available in their neighborhoods To make sure that he gets to school on time ... my son gets to his school like half an hour before classes even start That is valuable time that he could've spent sleeping ... so that he can sleep in that extra half hour, so he can eat a warm breakfast and not be rushed, also so we can say prayers together in the morning. (Alicia Interview, #SA4) Well, there's really ... actually there is a bus that my one child could get on to go to school, but it leaves so early in the morning, it leaves at 6:3 0 and so I've just kind of saved her the agony of getting up that early by taking her when I have the time. (Debbie, Interview, #CRIB) We would have to get up pretty early to walk regularly (Lyssa Interview, #STO) Families that emphasized the slower, more deliberate morning pace often included a full time parent, as opposed to two parents with fullor part time jobs outside of the home. However, the pace was not uniquely associated with one mode of travel or another. Rather parents strategically planned their commute (including mode of travel) to accommodate their desired life pace. 138

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Habit Some parents expressed that they do not actively think about how they will get to school. Instead, they described the behavior as second nature, or a part of their daily rituals or habits. In that context, the description of driving as "convenient" may refer to its familiarity, or the ability to accomplish the task without thought, as much as relative time and energy costs for various travel modes. In the following examples, Teresa, Ann and Mary explain how the trip to school affects their day. Their answers illustrate the habitual quality of their daily commute. Mary's response in particular, illustrates a lack of forethought about the commute and time costs that result. I've never thought about it, so I'll say ... (Teresa, Interview, #PH7) It doesn't. It's part of the normal routine and because we're so close to school it's inconsequential. (Ann, Interview, #ST8) And parking is a pain. That's why I'm always embarrassed to drive, because frankly driving around and finding a place to park takes almost as long as the actual time ... It is a royal pain to stop your car out here. It is very hard to find a parking place. Quite often I'll get in the car, race over here and then think why did I do this? I could have been in my bedroom already. (Mary, Interview, #EDO) Although I asked how they 'typically' get their children to school, many parents described shades oftravel preference between 'usually walk' and 'usually drive' and variations in their routines that depend on a variety of day-to-day conditions. The following examples illustrate varying degrees of fluidity in travel decisions. Nancy describes a relatively stable routine with occasional disruption for 139

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appointments. In contrast, Amy begins by describing her typical travel mode, but then qualifies the description, explaining that good weather can influence her to stray from her normal routine, despite a considerable difference in time cost. Alberta describes her child's influence and an apparent contrast between their travel preferences that results in a more fluid decision-making process. About the only time we change the routine is if they have an appointment where it would be necessary for them to either get to school/ate or go to school early. (Nancy, Interview, #SL4) S: So how do you get there? Do you drive pretty much all the time? A : Yeah, we do drive. Yeah, pretty much. We sometimes, when the weather's nice we can walk, but we're fourteen blocks and that adds ... twenty jive minutes to our morning, so ... (Amy, Interview, #SL5) We drive, unless they whine loud enough, then we ride our bikes ... I would have to ride my bike, and then ride back home, and then get into the car and drive to work, and I don't have time to do that. Every once in a while I do that, like I said, if they beg. (Alberta Interview, #SL3) The fluidity of travel mode decisions also varies between morning and afternoon commutes. In the following example, Ashley describes differences within her daily schedule that helps to explain why car traffic increases at the school at pick-up time. That's what I meant. Well you just figure like, 3:00 and you know, you are driving from somewhere and I know I need to be here at 3 :00. I just kind of work out my day around that. There are actually a lot more people in cars on the way home, so at pick up. (Ashley, Slavens STSC mtg. 9-19-07) 140

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Whereas Alberta and Amy both typically drive but walk or ride on occasion, Nancy expressed the opposite-typically walking but driving under certain circumstances. Other parents, including Ashley, described a middle ground-walking one day and driving the next, or even walking one direction and driving the other. Those shades of travel preference are often lost in the literature that focuses on the most frequently used travel mode. In addition to the practicality of planning trips to school, parents also described the school trip as an opportunity to nurture their children. In the next section, I present findings that relate to nurture the second main thematic group including consideration of the child's age and ability, developmental opportunities during the commute, and school requirements that influence travel behaviors. Theme 2: Nurture Researchers describe several developmental hazards associated with driving children to school and imply corresponding benefits associated with active travel. For example, Hillman et al. (1990) find that automobile travel for school trips limits children's independent mobility and caregivers' independence, thereby suggesting that independent travel would restore those developmental benefits. Tudor-Locke, Ainsworth et al. (2001) argue that active commuting is a critical, but overlooked source of children's daily physical activity. According to various studies, omission of the active trip to school significantly decreases children's achievement of health-141

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related activity guidelines, limits academic achievement, contributes to anxiety and depression, and establishes lifelong patterns of inactivity (Dwyer, Sallis et al., 2001; James, 1995; Kegerreis, 1993; Shephard, 1997; Tudor-Locke, Ainsworth et al., 2001) These studies similarly imply that active travel would restore the developmental benefits lost to private automobile travel. Responsibility, Maturity and Trust In contrast with the extant literature, Denver parents' explanations for school travel behaviors focused more on benefits than obstacles, and used positive language regardless of mode choice. They often described deliberate efforts to address several issues relating to their desire to nurture their children. Regarding the option of walking, many parents expressed a desire for their children to learn responsibility and to gain autonomy. In the following examples, Shawna and Ernie describe the responsibility gained and required as children travel by themselves to school. Ernie describes practical considerations that help the child to develop that level of maturity. I guess one of the advantages to walking to school [is} that it teaches a child responsibility. It sends a message to your child that you trust him, that he can take care of himself, and can make sure he gets from one point to the other, safely, and on his own. (Shawna, Cory PTSA, 4-4-08) It gives them responsibility and to know what time to get up and that they will be walking to school, and to dress properly for the weather, and what have you. So, there 's a little bit involved. There 's a bit that 142

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goes into it. It's not just throwing on a sweater and driving them to school. They have to prepare for what they are doing and wear the proper shoes or boots for the weather and what have you (Ernie, Interview, #ED3) maturity. Although they recognized the benefits of autonomy, many parents wondered whether their children were mature or skilled enough to make the trip on foot, and at what age they might handle the school trip without adult supervision. Some parents implied a level of maturity defined by skills or physical stature, while others cited specific ages that seemed appropriate for independent travel. In the following examples, Alberta's diminutive description of her sons suggests a lack of confidence in their maturity based on their size, while Jane's and Robin's concerns relate to their children's cognitive capacity In contrast, Brenda's explanation suggests concern for emotional maturity marked by their children s attention to a serious, adult objective. I just don't trust these two little guys yet. They have a lot more growing to do. (Alberta, Interview #SL3) I just don't trust these two to be conscious of their environment, you know street crossings and cars backing out of driveways I would worry too much, and that would ruin my day (Jane, Interview #PH6) They also have to prove to me that they know the rules of the road, like crossing the street, looking both ways, and not to talk to strangers, those kinds ofthings. (Robin, Bromwell PTSA, 11-16-07) 143

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They would have to be mature in their decision-making. I don't trust them to make it. They would have to prove to me that they are ready to make good decisions day-in and day-out. They're the kind of kids that walk by an empty lot and end up chasing squirrels and playing with worms. (Brenda, Interview, #SA 7) In the following examples, Dave, Amy and Chris describe maturity in terms of age, arguing that children are prepared to take on certain responsibilities at certain defined intervals. Dave's intuitive comparison of eightand ten-year-olds' abilities are consistent with Amy's and Chris's recollections recent scientific studies. Just his maturity. He's eight years old and I'm figuring around ten. And you know as a kid I walked to school everyday from kindergarten on. I think the times are just different now and he needs to use more discretion with his judgments as far as dealing with people, who he's riding with, if he accepts rides, you know, if he's got the focus enough to walk home by himself and not mess around with his friends, or if he wants to change his plans to be responsible enough to call, things like that. (Dave, Interview, #SMJ) I read an article a few years ago that talked about at what age what children can ascertain things. Uh, one of them was about crossing busy streets and so forth and I remember it said something I think it has to do with their vision and depth perception that they might be able to see a car coming and know the car 's coming but they can 't judge how fast it's coming they can't judge for sure if they can get across the street or not. (Amy, Interview, #CR9) They just don't have the depth perception or the speed adjustment that we all have intuitively. Kids just don 't have that. (Dr. Urbine, Coalition, I 0-24-07) trusted companions. Due in part to perceptions of their children's maturity, many parents hesitated to allow their children make the trip to school alone. As a result, the 144

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availability of suitable alternative companions was a key factor in their travel mode decisions. Parents often expressed concern about the numbers and trustworthiness of other children in the neighborhood. In the following examples, Brook and Mary describe opposing experiences of neighborhood peer groups for their children, although they both live within a short distance of the same school. Both parents associated their explanation with the need to drive, although they also walk on occaston. If there were a few then I would say yes, but there is not. There [are} really no kids that live over by us. There aren 't too many kids in our area. (Brooke, Interview, #ED4) I don 't think that in elementary school I would have them walk by themselves ... a group though ... we have enough kids over by where I am. (Mary, Interview, #EDO) One SR2S facilitator explained a mapping intervention as an effort to encourage students to cluster along key routes as they walk to school. Part of the reasonfor coming up with the better routes is that the more kids that are walking it, the safer it is for the kids because bigger groups are much easier to see than one or two kids walking or biking by themselves. And it's also more likely just because that way you know that there are more parents that are walking that route. There are more kids watching out for your kids. (Rebecca, Bromwell PTSA, 11-16-07) Parents' concern regarding safe companions for the trip to school naturally extends from indecision regarding their children's developmental capacity. Intervention might leverage these related concerns by organizing active travel days in 145

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conjunction with instruction that contributes to the acquisition of developmental skills required for an independent commute. Developmental Opportunities Denver parents associate the school trip with several developmental opportunities. They expressed their children's and their own enjoyment of the trip, explaining that it affords them time to act independently or conversely time to bond. In the following two examples, Patricia and Amy describe the ways that they have adapted the school commute to help them instruct and play with their children. Although the two mothers associated common benefit with the school trip, Patricia was referring to a walking commute while Amy was referring to her daily drive. It s really good bonding time for me and my kids We play games on the way to school and get to bond together. We'// play counting games and we stop and talk to our neighbors and friends if we see them around the school or home (Patricia, Interview, #ED8) I give them encouragement and advise for things before I drop them off I look forward to the trips to school everyday (Amy, Interview, #CR9) instruction. Parents who described the school trip as an opportunity to bond also explained that it allows them to instruct their children and to ask them about school lessons and other activities. In the following examples, Ramona, Jeff and Kami describe the instructional benefits of their commuting time. Kami's comment differs 146

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from the other two because she describes the commute as an object lesson for learning safety skills in addition to reviewing course materials. I enjoy taking my children to school It gives me a few minutes with them to ask them questions I maybe forgot to ask and talk to them and see if there 's anything they need to do. I enjoy every, every morning I get a kiss and a hug goodbye, and every time when I pick them up I get a kiss and a hug and that s my favorite. And then I get to talk to them about their day and what went on. (Ramona, Interview #SA5) Those are my favorite times of day I get to hear the stories right when I pick them up from school so I don t miss anything and I give them encouragement/advice for the things before I drop them off I look forward to the trips to school everyday. (Jeff, Interview, #CRJJ) It is a great opportunity to talk to my son but I also want my boys to know their parents are there to support and protect them and to help develop a sense of security in the world they are growing up in. We live in a pretty safe neighborhood but it is a great opportunity to teach them basic street safety lessons and just have some time together. (Kami, Cory PTSA, 4-4-08) Although parents expressed concern regarding their children's ability to handle certain traffic and social hazards en route to the school, they more frequently described the trip as an opportunity to review classroom lessons than street safety and may therefore be overlooking an important opportunity. Intervention could address that gap by instructing and encouraging parents to reinforce safety lessons. health benefits. By prioritizing classroom lessons and other activities, parents might also miss the opportunity to provide health and wellness instruction and to associate 147

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those lessons with the school commute. Some parents suggested that their children get sufficient exercise from other activities and that walking or biking to school could detract from their fitness by causing them to wake up earlier in the morning, or by interfering with scheduled activities. In the following example, Andrea acknowledges that active travel would be important for 'some people' but excludes her family on the basis that they get sufficient exercise in other ways. Well, we would have to get up earlier. They get so much active sports and activities that it isn 't so important for us as it would be for some people. (Andrea, Interview, #BR8) However, several Denver parents and officials associated active travel with health benefits and considered the walk to school to be an opportunity for them to exercise with their children, or for the children to get much needed physical activity on their own. In the following examples, Brigitte, Rory, Janice and Ernie relate the health benefits of active travel with academic achievement and general success in life. Although these parents agreed with officials that active commuting offered certain health and academic advantages, only two ofthe three (Janice and Ernie) allowed their children to walk regularly. The other piece that we know is the benefit, because when you are active, you're academic achievement truly can increase and benefit from that. (Brigitte, Coalition, 10-24-07) In today's day and age when so many kids sit infront oftelevisions all day, kids are getting obese for obvious reasons. And with them not walking to school every day, you know, without exercise you will see their grades start to lag behind. There are so many studies that show 148

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that kids who get exercise regularly are more productive (Rory Interview, #CR1) It's really kind of nice to not have to spend that travel time. He's a little lazy anyway, so it gets him out there walking or riding his bike depending on what mood he s in. (Janice, Bromwell PTSA, 11-16-07) I think it's good for them. It helps them with their physical condition as well. Because L you know, don't want them to become lazy, (Ernie, Interview #ED3) academic objectives. Like Rory, many parents explained that they adjust travel patterns to accomplish academic goals. However, the adjustments did not typically favor an active commute. For example, as a result of Denver Public School s open enrollment policy many families travel outside of their neighborhoods so that their children can attend schools with special programs or higher academic ranking. In the following examples, Amy, Brenda and Jeff explain that for their families, mode choice is secondary to school choice I should probably tell you we do not go to our neighborhood school. We go to a school that is a little more than two miles away ... at the magnet school for the highly gifted program. They do provide bussing for the highly gifted program ... but I figured out it would be just as easy for me to drive him than to be at the bus stop a half an hour early. (Amy, Interview, #CR9) Well I drive my kids to school because we always pick the best school no matter how far away from home it is. (Brenda Interview, #SA 7) I never believe that I should automatically send my kid to the local school out of convenience. I am willing to get my child to the best 149

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school with an education that best fits their personality. This may mean having to drive them to school. (Jeff Interview, #CR11) School Requirements In addition to open enrollment at the district level, individual schools enact policies that restrict travel mode choices. In the following examples, staff members and parents describe their schools' biking regulations. I think they kind of discourage it because there is no safe place to put them ... The first thing, I do have them say that there is no ... they ask do you have a bike rack ... that's fine but just know there are no bike racks to lock things up. (Secretary, Force STSC 9-17-07) I read it in a Thursday folder that 3rd grade is when they want kids to start riding their bikes. But for me, I have a second grader and a kindergartner and a fourth grader. So the fourth grader rides his bike and the other two want to. (Carrie, Slavens STSC 9-19-07) I don't have many bikers at all because we discourage it at this point because we haven't had the training nor do I have the space for the bikes where I can keep them secure ... I don 't have space to put the bikes. I have no ... I don't have a case to lock them up in. You know. If we get that ... and I will continue to discourage it because I only have one bike rack out there, you know. (Principal, Sabin STSC, 9-11-07) Additional policies apply for children in pre-school classes and for those who attend after-school programs. In the following example, Monica describes a sign-in requirement that prevents parents from quickly dropping the kids off at school, but also prevents the children from traveling without their parents. You know a lot of the schools have got a head start ... preschool in many schools, and those schools require that the parents get out of 150

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their car and bring the kids in and sign the children in because it's part ofthe state law. (Monica, Coa/ition,l-23-08) In contrast with district regulations, however, individual school policies can change abruptly with staff turnover. Out of the thirteen schools that I studied for this part of my research, only two had leaders who regularly rode bicycles to work and encouraged the students to do the same. Since that time, one of the two left the district. In the following examples, two parents summarize the impact of leadership change on school culture. Professionals from the City of Denver echo their sentiments in the second two examples. There was a teacher who used to ride his bike and walk but he moved to another school. (Melissa, Slavens STSC 9-19-07) Once you change a principal the whole school changes as well. (Jackie, Coalition-sub, 11-15-07) The truth is that, about the lack of principal involvement at the schools. Sometimes it is just the, just the stayability of it, the sustainability of it ... because you could have a really solid principal, but the next year you get a new principal, so that transition of leadership ... (Abigail, Coalition, 1-23-08) You may have a principal one year that's ... well... you know this isn 't working, the busses are dropping off here and the parents are dropping off here and we've got to change it all, and so we'll get our engineers. They change all of the signs and they do everything then they get a new principal comes in the next year and says no this is not working, and they want to change it back again. It creates a lot of work and you know you are doing it over and over and over again ... (Crissy, Coalition, 1-23-08) 151

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academic materials. Parents made travel decisions to accommodate classes and extracurricular programs that occasionally require students to carry heavy backpacks, instruments, projects or refreshments to and from school. Those instances typically resulted in parents electing to drive who would normally choose more active modes of travel. In the following example, Andrea explains how her son's backpack influences the travel behaviors of the entire family because they make those decisions together. Kami similarly chooses to drive her son on occasions when he has extra materials to carry. His backpack weighs a hundred pounds! If anything were to allow them to change their commute to school this would have to change (Andrea, Interview, #SM7) Som e times if I had to be at school or if he had a project or poster to carry I would drive him. (Kami Cory PTSA, 4-4-08) special events. Parents also made travel decisions to accommodate school requirements or special events intended to benefit the children's health. Those instances typically meant that parents would walk who normally drive. In the following examples Lyssa and Sharon describe active travel events that encourage them and their children to walk on occasion. They have a walk to school day here where everybody is encouraged to walk to school And they do, you know big ... they have water bottles on the playground for when you get there and they re not you know you don t get in big trouble if you're late because y ou know people aren 't really sure of how long it s going to take them So we walk on walk to school day. (Lyssa, Interview, #STO) 152

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Her school has a program sponsored by the gym called the kilometer club done by the coaches. But they only do that when the weather's good, and I'm able to do that most mornings when my schedule allows me to do that (Sharon, Coalition-sub, 11-15-07) So what happened was they got prizes for walking on Wednesdays and then they started telling their parents they had to walk and there was all this peer pressure so ... they started walking every day because they just liked it. (Rachel, Coalition-sub., 11-15-07) We have incentives to the classrooms that walk the most so they'll get like a prize pizza party type thing. (Rebecca, Coalition-sub., 11-15-07) Some parents are more inclined to walk or bike their children to school than others. In part, the difference depends on household routines disruptions in those routines, children s maturity, the availability oftrusted companions, and parents' prioritization of health and wellness instruction. However, their inclination to walk or bike is only relevant if the environmental context allows pedestrian activity. Theme 3: Context Planning research about school travel emphasizes contextual barriers that prevent active travel. For example, school travel research often cites distance between home and school as one of the most influential factors associated with automobile travel for the school trip (Bradshaw, 1995; Collins & Kearns, 2001; DiGuiseppi, 1998; Krizek, 2003; McDonald, 2007; Prevention, 2005; Schlossberg Greene et al., 2006). However, exploratory studies find that even families who live close to schools 153

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choose to drive to overcome traffic hazards or social dangers from unfamiliar adults, transient populations and groups of older children (Collins & Kearns, 2001; DiGuiseppi, 1998; Hillman, Adams et al., 1990; Joshi & MacLean, 1995; Prevention, 2005). Studies that describe transportation options often focus on access to private vehicles, and show that families that can drive are likely to do so (Bemetti, Longo et al., 2008; Bradshaw, 1995; Cervero & Radisch, 1996) Traffic Dangers Several context-related themes that emerged in my research parallel those from previous studies. For example, some parents described traffic dangers including hazardous crossings and busy streets. In the following examples, Amy, Rebecca, Andrea and Lyssa describe general road hazards that make it feel unsafe for them to walk to school. In contrast to the examples mentioned previously, these parents do not associate the obstacles with their children's maturity or ability to handle themselves. I thought about this before and there 's really not a lot of safe routes We live a block and a half from a light rail station and from a highway entrance and it's really busy in the morning and there are no ... there's not really a safe route to get there that he could that I would feel comfortable letting him go on his own. (Amy, Interview, #CR9) I'm not sure we have any really great ways to get to our school. There 's a lot of really great walking until you get about a block from school and then it gets pretty dangerous. (Rebecca, Steck PTSA, 1212-07) 154

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We do have to cross some kind of busy streets. We have to cross Colorado Boulevard, which is a pretty major six-lane highway. (Lyssa, Interview, #STO) It is mostly about one of them getting hit by a car There are so many cars around ... (Andrea, Interview, #SM7) In the following example, Janet describes a specific hazardous crossing as more of an inconvenience than an obstacle that entirely prevents active travel. Although Janet chooses an active travel mode and faces the challenging intersection, she says that it deters other families from walking or biking to school. We're on the other side of University {Avenue} ... You have to make sure that all of the cars come to a stop because some whip through. And then it's such a quick light. They are running across with their bikes and you have to make sure they stop, which takes a few seconds, and then the hand starts flashing. I think a lot of people on that side are concerned about that. I know that other people won't walk. (Janet, Interview #SLJ) In contrast, Alison describes her family's choice to move to a new residence closer to the school rather than risk her life crossing a major intersection multiple times each day or having to drive In a two week period, I was crossing the road six times a day between ECE and regular school hours and somebody ran the light at Fifth and Colorado while we were on one side or the other and on Colorado Boulevard six times in a two week period. And then we were like we are moving. Because the option is that I start driving to school because it is really the only safe way to get to school. (Alison, Steck PTSA, 12-12-07) 155

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In response to traffic safety concerns, school staff and parents often recommend infrastructure improvements such as bike lanes and locked bike parking, traffic lights, signs, and other traffic calming devices that could increase pedestrian safety. However, their efforts are not always rewarded. In the following examples, a school principal and a PTSA parent describe problems in the neighborhood, and express frustration in the slow pace of infrastructure improvements. You know, I think of the wide sidewalks ... because as soon as you go across Dallas over there, it's all those little dinky sidewalks. And it's a three-way stop there. Why couldn't there be a three-way stop on the busier street? (Principal, Slavens STSC, 9-19-07) If there are those places where crossings, where signs are needed, do we wait to mention that or can we request that now? There are immediate concerns at the school ... We have already done that [called DPS}. I need to follow up on that one. Nothing has been done about the recommendation that Nicole did about the signs ... I think what you are doing is great, but if the other side doesn't do it then there really aren't going to be any safe routes, so I don't know what you are going to be able to present. This has been an ongoing problem with us. We talk about it every single year over and over again and have had no cooperation whatsoever. (Alison, Steck PTSA, 12-12-07) In many cases, like the one the Alison described at Steck Elementary, poor driving and distractions in the cars in addition to poor roads, signage and other infrastructure contribute to dangerous traffic conditions around schools. In the following example, Andrea, Tricia, Jackie and Pauline describe traffic dangers, but focus blame on the drivers rather than infrastructure. Everybody goes fast ... faster than the speed limit. (Andrea, Interview, #SM7) 156

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People just are on their phones just not paying attention and you know you are taking your life in your hands crossing Colorado Boulevard (Tricia, Steck PTSA, 12-12-07) Seriously, we have to make it illegal for cell phones to be spoken on in school zones ... it is the stupidest thing in the world I have ever seen and it's all over the place. (Jackie, Coalition-sub, 1 0-24-07) 1 know in front of my children's school there is a, there is a crosswalk. It's not a signalized crosswalk and the teachers will go out and they put their safety vests on and stop sign and I'm always surprised every time I come up there how many people do not have any intention of stopping for the parents and the students and the teachers who try to cross the crosswalk there. (Pauline, Coalition, 1 0-24-07) In the following examples, school staff, police and Coalition members describe recurring violations in which parents create traffic dangers during drop off and pick up. Pulling into these spots like where the recycling bin in is, and this the loading dock. They pull in and back out into the street. If they can 't pull into a parking spot they don 't really worry too much about the rest of us. This is where they park and come flying across the parking lot. You know. We ask that people don't drop their kids in the parking lot and they pull right up there and stop in the middle ... and drop their kids off on the drivers' side. (Principal, Slavens STSC, 9-19-07) Oh goodness, well you guys ... well today, have fun. You can watch these crazy parents do u-turns in the middle of the street. (Principal, Sabin STSC10-22-07) I mean there are a lot of things that can be done ... more volunteers as far as school goes, putting the information out there to the parents. 157

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Parents sometimes are the worst problem ... I hate to say it, but you go to any school you know there 's parents that just stop in the middle of the roadway dropping their kids off, or just parking where they are not supposed to be. (Police Officer Tony, Slavens STSC, 9-19-07) We ask them year after year after year, ask parents not to double park, not to let their kids cross the street, every year in the middle of the year we send home a reminder in Spanish and in English, so ... (Assistant Principal, Slavens STSC, 9-19-07) In the following example, Lisa describes in real time a traffic violation she observed as she conducted a walking audit of the school. Look at this guy crossing behind the bus and now they are crossing in the street to the cars ... just taking their kids ... illegally jaywalking across the street to get to the cars. (Lisa, Force STSC, 9-17-07) Social Dangers Denver parents described social dangers associated with specific locations including residential and commercial locations that foster criminal or otherwise undesirable activities, and expressed concern that other children and unfamiliar adults might cause trouble if they allowed their children to travel alone. In the following example, Barbara recounts concerns that a group of students shared with her about their neighborhood. I'm glad you guys are talking about the crime issue, but we have worked with a bunch of young kids and I asked them to brainstorm on you know why they don't walk to school, what makes it unsafe to them. Everything was related to people, not sidewalks. Some was related to traffic, but it was the liquor store, people hanging out at the liquor store, the graffiti signs that sense that there are unsafe people around. 158

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Because the police department wou/dn 't have time to be busting people for loitering outside of a liquor store, that's not going to, you 're not going to find that in the crime statistics. (Barbara, Coalition, 1-23-08) In the following examples, Alicia and Kerry express concern about the social environment their children are exposed to on the bus and at the bus stop. The kids on the bus and how they behave, the loud music, the bus driver's behavior towards the kids and their attention to the road while driving ... the amount of time the kids stay on the bus ... the walk to the bus stop and the people my son is exposed to at the bus stop ... (Alicia, Interview, #SA4) You know, and this is going to sound terrible... There are two bus stops very close to the house. And ... I may answer No, there aren 't really kids on our street. I don't know the other kids in his class are at these bus stops. One of the bus stops right ... in Lowry there's like public housing ... and I don't have any issue with that but one of the bus stops is like, that's the primary bus stop, and the other bus stop is literally like a couple hundred yards from my house. And my, part of my fear is just that maybe it's just more issues than he needs to be exposed to (Kerry, Interview, #LR6) Similar to Kerry, Mary and Mike describe neighborhoods that suffer from transience and a lack of familiarity between neighbors. I just doni feel comfortable. I mean, 32nd is ... you know, we live on 32nd so it'd be going down 32nd People getting off of .. well, people ride the bus along there all the way from Applewood to Downtown ... I doni want to say its transient, but sort of I mean you get people there all the time you doni even know. (Mary, Interview, #EDO) You no longer know your neighbors like people once did I remember that my parents had the same neighbors the entire time my brother and I were growing up; they knew the neighbors and we did also as 159

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children. Now, I don't think I feel comfortable talking to the neighbors just because they are new and seem quite busy. In days past they'd come over and say "hi." I wouldn't have my daughter go take them their mail, that's how little I know them. (Mike, Interview, #CR6) In contrast, Marie describes a neighborhood that has a strong sense of community, but feels unsafe due to a single residence with persistent criminal activity. K: So do you think this is a safe neighborhood? M: Nope. K: Oh, no? What is unsafe about it? M: Down on the corner of 281h and Ivy there is a drug house We '/I clean up the street, the neighbors know each other. They watch out after each other, but that one house down there ... (Marie, Interview, #HA9) Parents also expressed concern about dangerous adults, including neighbors and strangers that may pass through the area. In the following examples, Ramona, Jackie, Rory and Albert describe similar concerns about social dangers. Too many kidnappers and child molesters. (Ramona, Interview, #SA5) I really worry about walking school busses. I mean we have people, obviously we have people who are sexual predators. (Jackie, Coalition-sub, 11-1 5-07) We live in Denver. It's just not a very safe place to let your eight-and six-year-old kids be alone or even with someone you know. (Rory, Interview, #CR1) 160

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No, the world we live in. It ain't safe nowhere ... That's right. You can live in a 100,000 or million dollar homes and kids are kidnapped on the street. It don't matter. (Albert, Interview, #VA3) Some parents also described positive neighborhood characteristics, including a strong sense of community, a feeling of safety and comfort, and neighbors who know each other and watch out for each other. In the following examples, Anne and Alberta describe their neighborhoods as both safe and protected by neighbors who know each other well enough to help in a crisis. I think it's a safe school neighborhood and we live in a safe neighborhood and we chose to live in an area that had a good elementary school in it and we were close to it so we wouldn 't have an issue with where to go to school and how far it would be from our house. That was part of our decision making process when buying our house. (Anne, Interview, #ST8) S: Do you ever worry about strangers? P: Yeah, I mean every mother has to, but in this neighborhood I'm not too worried ... like if one of them yelled for help, someone would be around to protect them. (Alberta, Interview, #SL3) Transportation Options One might expect that families who live in safer neighborhoods would be more likely to walk or bike to school. However, in many cases, the opposite was true. Similar to other studies, Denver parents described their travel behaviors for school trips in terms of available transportation options. In the following example, a 161

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secretary at Force elementary relates parents' requests for their children to bike to school because they live too far to walk but do not have bus access. Over the years I don 't see it as a lot but every new year a parent will ask, you know, can my son or daughter ride their bike and dada da da da, because they live at the farthest point and there is no bus (Secretary Force STSC 9-1 7 -07) Some parents' options were limited because they chose to enroll their children in schools outside of their neighborhoods. For example, Brenda and Debbie take their children to a school that is out of reach of bussing, and too far to walk or ride. 1 would say that the distance is the primary reason I drive the kids to school. It's about two to two and a half miles. That s just a little too far to walk. Because we chose the school that is not in walking distance we are responsible for transportation. (Brenda Interview, #SA7) My other child, there really isn t any other way of getting to school there s no bus that he can take, so he pretty much has to be driven to school because it's too far to walk... Well I suppose if there were more carpooling available ... in our area or convenient bussing .. then we probably would, I mean we would consider riding the school bus. It would save some gas basically It wouldn 't really save any time for her It would for me. So that might be an option in the future. (Debbie, Interview # CRIB) Other parents described complex carpooling arrangements based on intimate knowledge of other families habits and schedules, and expressed a high degree of dependence on those options. In the following examples, Nora describes advantages as well as strategic challenges associated with carpooling, while Debbie speculates about convenient alternatives to driving. 162

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So there are carpools for soccer and carpools for ballet and carpools for lacrosse ... so everything that my kids do has a carpool. You need a calendar ... and figure out which kids you have in your car, but I think, you know in this day and age, if you have more than one kid and they tend to be active, then really the only way you can manage it is through carpools. And you still do a ton of driving but you are essentially doing less than you would if you were responsible for only your family .... I thinkyou agree to carpool with families that you know because you understand their habits .... Carpools can be a very intimate affair. You all have to have the same ... be on time ... It definitely develops community in the fact that you have this added thing that you have in common .... It definitely enhances that community between the adults and the kids. (Nora, Interview, #STJJ) Well I suppose if there were more carpooling available ... in our area or convenient bussing ... then we probabl y would, I mean we would consider riding the school bus. It would save some gas basically. It wouldn't really save any time for her It would for me. So that might be an option in the future. (Debbie, Interview #CRIB) Gas Prices Denver parents expressed concern about transportation costs, and speculated that active travel might help them financially. In spring of 2008 gas prices across the U.S. increased to almost four dollars per gallon which may have influenced this line of thinking more than would normally occur In the following examples Yasmine and Jeff consider options that would decrease the number of miles that they drive. In answer to the question, "how would active travel for the trip to school affect your lifestyle," Yasmine calculates the time and gas she would save. Her response suggests that she imagines her child traveling by himself if he were to walk or bike. Because Jeff and his family currently live over 15 miles from the school, his response focused 163

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on a more substantial decrease in driving, although active travel would also become a possibility if they moved closer to the school neighborhood. It would change the amount of miles I would put on my car. It would change the amount of gas I would have to buy for my car and it would free up approximately thirty minutes of time I usually spend driving. (Yasmine, Interview, #PH3) We are considering moving here as well .... We are encouraged by the gas prices and the number of miles we drive for work. (Jeff, Interview, #SL7) Evidence from this study suggests that some contextual factors are byproducts of parents' choices about how, when, and where their children go to school in addition to being barriers that prevent active travel. Although parents living great distances from their children's schools are not likely to walk, those living far from schools invariably chose that obstacle because other benefits associated with their choices outweighed active travel considerations. Although families generally do not choose to have dangerous traffic patterns or criminal activity in their neighborhoods, those conditions do not present barriers to walking when alternative transportation is unavailable. Variations in parents' responses to contextual conditions indicate that eliminating barriers will not necessarily achieve an increase in active travel. Mapping Attitudes Using Opportunity-Propensity Measures The following two-dimensional graph illustrates several attitude types related to combinations of parents' opportunities (x-axis) and propensities (y-axis) to use 164

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active travel modes for school trips (see figure 5-l ). The data points in this illustration represent attitudes that individual parents in this study expressed, although their exact placement in the graph is figurative. Figure 5-l: ATTITUDE TYPES BY PROPENSITY AND OPPORTUNITY Identify safe routes OPPORTUNITY Schedule events Leave PROPENSITY Reward continuity Increase awareness and reward behavior The top right quadrant of the graph represents families that have both the opportunity and the propensity or desire to walk or bike to school. I included parents in this group who described occasional disruptions to their routine that required them to drive. Active travel programs can plan to increase their numbers of active trips and should target this group by rewarding continuity in active travel behavior. The top left quadrant of the graph represents families that are inclined towards active travel, but perceive that their opportunities to walk or bike are limited. I 165

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included parents in this group whose path of travel to school crosses a busy road or intersection, but who associate active travel with personal benefits. Increasing their numbers of active trips may require program facilitators to identify existing safe routes between home and school, increase safety along certain routes, or otherwise improve perceptions of pedestrian safety. The bottom right quadrant of the graph represents families that have the opportunity to walk or bike to school but lack the inclination or motivation to do so. I included parents in this group who live within 10 blocks of the school and whose paths of travel do not cross a busy road or intersection, but have not considered active travel, perhaps because driving is part of a routine that they perform without thought. Active travel programs can use promotional materials to increase awareness of benefits associated with active travel, and can reward students who walk to school. The bottom left quadrant of the graph represents families that have neither the opportunity nor the inclination to walk or bike to school. Although it may be most cost effective to exclude this group from intervention efforts, they may still respond to occasional walking or biking events. Ideally, active travel programs should focus efforts on those families whose inclination and/or opportunity to use active travel hang in the balance. By introducing the activity on an occasional basis, the program can increase that group's level of comfort with the activity and increase the probability that they will repeat it on their own. 166

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Summary Similar to other planning research regarding parents' travel behaviors for school trips, my qualitative study revealed environmental, socio-demographic, and psychological factors associated with travel mode choices. More specifically, three themes emerged as I analyzed transcripts of SR2S planning meetings, impromptu focus groups, and interviews with parents (see table 4-15). Those themes-Life-pace, Nurture, and Context-also superficially reflect the findings of extant research. However, researchers often interpret those factors as barriers to active travel. My analysis indicates otherwise-that the respondents' experiences of each issue vary considerably. This suggests that a single approach to active travel intervention, particularly one focused on eliminating environmental obstacles and enabling active travel, would influence only a small segment of the policy's intended target audience. Over the past several decades, researchers in planning and related disciplines have developed theoretical models to understand why people make certain choices. I discuss the evolution of those models in Chapter Two. Although the models generally stem from and/or support the rational choice theories of microeconomics (i.e. cost benefit analysis), some models describe the influences on choice in more detail. For example, McMillan's (2005) model of children's travel behavior describes an indirect influence ofurban form on parents' travel mode choices and Chapin's (1974) general model of human activity differentiates and elaborates 'opportunity-related' and 'propensity-related' influences on activity patterns (see figures 2-1 and 2-3). 167

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Findings from this qualitative study suggest that propensity moderates the influence of opportunity, rather than operating independently from it, as Chapin's (1974) model suggests. That relationship is consistent with Michelson's (1977) discussion of antecedent conditions, which influence propensity to engage in social behaviors that environmental conditions support. It is also consistent with McMillan's (2005) model of children's travel behavior that depicts parents' mode choices as a function of mediating and moderating factors (see diagram 2-1). For example, a parent whose propensity to use active modes of travel is high (i.e. a health-conscious family) might perceive a environmental condition such as a major highway crossing as an inconvenience rather than an absolute barrier and still choose to walk to school. Combinations of propensity and opportunity in varying degrees result in an array of attitude types that targeted intervention can more effectively address. The small number of interview respondents in this qualitative study was well suited to an intuitive interpretation and classification of attitudes, and a figurative representation of those attitudes in graph form. However, more accurate attitudinal measures are necessary to guide intervention for a larger population. In Chapter Six, I outline the quantitative methods that I used to study Denver parents' attitudes about the school commute. I describe findings from that study in Chapter Seven. 168

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CHAPTER6: QUANTITATIVE METHODS Introduction Fewer parents walk their children to school than in past decades (Prevention, 2005). In Chapter One I explained why that change in travel behavior is problematic, and how planners justify intervention. Assuming that planners should intervene, it is necessary to determine how they can encourage parents to walk their children to school more often. Chapter Two examined McMillan's (2005) model of children's school travel and contrasted it with other choice models in planning (Boamet & Sarmiento, 1998; Michelson, 1976; Schuler, 1979). I argued that McMillan's (2005) model covers key dimensions of travel choice, including environmental, socio-demographic and psychological factors. However, the model only vaguely references a relationship between environmental characteristics and personal factors that may influence mode choice. By focusing on opportunity-related, environmental factors as primary determinants of travel mode, the model suggests that parents generally want to walk their children to school, but are prevented by external conditions. Other behavioral models recognize propensity-related factors (Chapin, 1974; Michelson, 1976), but similarly emphasize opportunity as the primary determinant of activity choice. 169

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One objective of my research was to examine the relationship between opportunity and propensity as they relate to parents' mode choice for school trips. In Chapter Three, I reviewed research about school travel that identifies and examines factors that influence parents' travel mode choices for school trips. I noted that researchers often reduce findings to a few of the most significant factors associated with driving, and that they describe the environmental factors, including social and physical environmental characteristics, as obstacles or barriers that discourage active travel. Even studies that examine travel mode choices for specific demographic groups focus on obstacles that prevent active travel (McDonald, 2008a). Because that approach assumes that parents of school-age children prefer to walk but are thwarted by various external conditions, it implies attitudinal homogeneity. This assumption could limit the reach of intervention by focusing undue attention on environmental opportunity. My research examines parents' attitudes about the school commute to find out how they vary, so that planners can encourage more people to walk more often. As I described in Chapter Four, the research design included two phases that spanned three semesters (see table 4-1). I selected 12 elementary schools as research sites based on their participation in SR2S non-infrastructure programs during the 2007-2008 school year (see Chapter Four for a description of the research sites and programs). In the first phase of my research, I identified issues that parents associate with the school commute not just factors that prevent walking or biking. In Chapters 170

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Four and Five, I presented the qualitative phase of my research that addressed that question. Findings from my study concurred with those of other exploratory studies, but also indicated that parents respond inconsistently and even contradictorily to certain issues relevant to the school commute. However, my study did not fully examine how parents' attitudes vary in terms of opportunity and propensity to use active travel. That is, therefore, the main objective of my second phase of research. In this chapter, I describe the quantitative methods that I used in my second phase of research. The chapter is organized to present the framework of the study and then to describe my data collection and analysis methods. I begin by briefly describing my research design and the research sites for the pilot and main sorting exercises. Then, I present my data collection instruments, including original and revised questionnaire and Q-sort exercises. I explain how I distributed the materials and how that influenced response rates. Finally, I describe my methods for analyzing the data and explain how cluster analysis of the sorting exercise data produced types of attitudes about the school commute in terms of opportunity and propensity measures. I present results from the main sorting exercise in Chapter Seven. Second Phase Research Design In this second phase of my research, I applied Q-methodology in a non experimental research design to identify and compare site-specific attitude types about active school travel. Q-methodology combines psychometric and operational 171

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principles with correlational and factor-analytical techniques to study human subjectivity that is, the points of view of research subjects (Brown, 1980, 1993; Brunner, Fitch et al., 1987; McKeown & Thomas 1988; Stephenson, 1953). Although Q-method uses quantitative methods to analyze data, the nature of the study is essentia11y qualitative because it examines and compares respondents' complete attitudes rather than estimating population parameters such as the proportion of respondents influenced by one or more demographic and/or environmental factors as they pick a travel mode for the commute to school. For the second phase of my research, I administered a pilot sorting exercise in spring 2008, revised the research tools over the summer, then administered a main sorting exercise in fa]] 2008. As part of these sorting exercises, I asked respondents to complete a Q-sort activity as we11 as a short questionnaire about household characteristics and travel behaviors. In the remainder of this chapter, I use the term sorting exercise in reference to the combined activities because they were administered together. I use the term Q-sort in reference to the sorting tool specifica11y. I analyzed some portions of the full data set from the main sorting exercise and then used purposeful, exclusionary sampling to select and analyze a small number of cases at each school. 172

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Research Settings Parents from eleven public elementary schools in Denver Colorado participated in this second phase of research. I first conducted a pilot sorting exercise at four schools in May 2008. Two schools closed permanently at the end of the school year, and I administered a revised, main sorting exercise at the remaining seven schools in September and October 2008 (see table 6-1 ). In the next section, I briefly describe the study sites that I used for my pilot and main sorting exercises, and how site selection supported my research objectives. See Chapter Four for a more thorough description of the research setting and study sites. Pilot Sorting Exercise Locations I conducted a pilot sorting exercise with parents from four of the twelve Denver public elementary schools that participated in a SR2S non-infrastructure grant during the 2007-2008 school year (see table 6-1, pilot sites are highlighted in grey). The research sites include one school from each of the three program grants-Force from Program 1; Steck from Program 2; and Lowry from Program 3 -and Munroe, an alternate school that had previously completed a SR2S non-infrastructure program. I included Munroe elementary, which has a predominantly Latino student enrollment, as an alternate to replace two schools Hallett and Smith that served disadvantaged populations and that had been slated for closure at the end of the 2007-2008 school year. I eliminated those two schools from the sorting exercise because news of the 173

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closure and anticipation of moving to new schools promised to influence parents' attitudes about the commute in ways that would compromise research objectives Table 6-1: 2008 school enrollment by race/ethnicity and free/reduced lunch program Teams School %Black %Latino %White %FIR #Enroll Denver Public Edison 3.0 47.3 46.9 42.5 461 Schools .. __ { I 'Sf,,' .. ,_,,., . '-,.<:!; ., .. ,. ( t' \. < '. ,.) 77,4: Force., .. 0.7 .. ... 89.4 7.5 .. ... 585 Denver Osteopathic Sabin 3.5 62.1 26.5 44.3 634 Foundation Slavens 2.2 5.7 89.3 1.5 456 Denver Environ. Cory 3.5 12.7 79.1 8.4 369 Health Transport. Bromwell 5.2 3.7 80.7 7.4 326 ' i Solutions I And Steck ; 7.5 .. 9.6 76.T 1Q:6: 292 Stapleton TMA Philips 83.4 13.0 3.5 84.0 169 Denver I , .. i';l; ,. ,. Lo\vry .. ' '''. ,s2J t 40.9 Health 28.8 .16.6 1'". ...... 458 . ,. . DPS Rides Valdez 1.1 95.4 2.3 87.3 434 University ; '.f' / l.O. ,_.;._ .>:.:2Ut .. /. 74; 8 : Average 12.7 40.9 42.4 43.6 432.5 Source: Piton Foundation School Facts, Data Year 2008, http://www.piton.org/ The resulting selection of schools for the pilot sorting exercise was diverse in enrollment size and demographic composition. It included two very large schools that serve disadvantaged populations-Force and Munroe; one small school that serves an affluent and predominantly White population-Steck; and one average sized school that is racially and economically diverse-Lowry. I selected this variety of schools 174

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for the pilot sorting exercise in order to test data collection instruments and processes with parents from various socio-economic backgrounds in school settings influenced by several SR2S programs, and so that I could revise the tools for the main study. Main Sorting Exercise Locations I conducted the main sorting exercise with parents from the remaining seven of twelve Denver public elementary schools that participated in SR2S non infrastructure grants during the 2007-2008 school year. The research sites included schools from each of the three program grants Edison, Sabin, and Slavens with Program One; Cory, Bromwell, and Philips with Program Two; and Valdez with Program Three. The resulting purposeful selection of schools for the main study approximately represents the larger body of DPS elementary schools in terms of basic socio-demographic indicators and neighborhood contexts. The sample is comprised of two schools that serve disadvantaged populations Philips and Valdez one very small and one above average in enrollment; three schools that serve affluent and predominantly White populations Slavens, Cory and Bromwell one above average and two relatively small in size; and two schools that are racially and financially diverse-Edison and Sabinone above average and the other very large in size. The locations of the research sites are also diverse, including settings with a range of housing densities and land use mixes. One of the objectives of this research was to 175

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explore how parents' attitudes about active school travel vary. By including a diverse sample of Denver's elementary schools as research sites, it was possible to compare types of attitudes within sites with those of other sites. Data Collection In this quantitative study, I collected data from a much larger sample of parents than in the qualitative study that I presented in Chapters Four and Five. To accommodate the large number of potential respondents, I distributed research packets to parents at participating schools through the students' take home folders and collected them in the schools' respective administrative offices. The indirect contact with parents posed a significant challenge that I addressed through the design and distribution of materials. In the next section, I describe the packets of materials that I used for the pilot and main studies and explain how the various instruments served my research objectives. Research Packets For the pilot sorting exercise, I presented research packets to parents in plain white legal-sized envelopes that contained several items (see figure 6-1). The main item was an 11 x 17 sheet of paper that I printed on both sides and folded in half to create a pamphlet. The front of the pamphlet contained an introduction to the project and raffle and a picture of the grand prize, an electric bicycle. The back of the 176

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pamphlet contained a short questionnaire requesting information about the respondent's household. Inside the pamphlet were a large Q sort diagram, instructions for completing the Q-sort, and four questions eliciting feedback about the exercise. The packet also contained stickers printed with statements about the school commute and unique three-letter codes for the Q sort exercise, and a raffle ticket to encourage participation. I numerically coded raffle tickets and other materials so that I could file identifying information separate from data, ensuring respondents' anonymity. I prepared separate packets in English and Spanish with the help ofhired translators, and verified translations with the help of bilingual school staff. 177

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Figure 6-1: PILOT SORTING EXERCISE PACKET-ENGLISH (NOT TO SCALE) :t.:t'.!.i' .. .. I'O.MoH .,. f;,tv.-1-'.1 vnt toe l\) l'P"'''"" ... ":.l.t'<.>C.I f...l'':' ... :O II r>r .. ,1 q:ll >rt'ooer-IC< I"'!"" ,t< ,.,....,,:.!"" ,.. :-. 1o '"''' -,.,,,U 'YII>Ud lo1o; ''' '",,..-,."'I t"tl -of!f>'f v.--.,:.ol ... m ... ... ,,,.,-.j,..,. MOti-1 Oisa-gre<.> -4 .INSTRUCIIO.NS -I t..l'lldllitl t:. .,. Ht,_,-..l sr= tw .. {J!.lLI >OW,_, .., ,,,,.,. .. ;,.,, .,._,.,., ,_,.. Uo.,. l,.,.-._ -' .,... ... "''''""' .. f.$<-1 ""'.;tiL:Ii h t;,l .... ,.,, ltU fl'-<'"'"1. "'"'' ll'f"'l ..-1;: !t"ul . ,..., .,.$1 ... :>'';o lhr: ..-.;u ;n_l'if..,. 'lllli 'n;;l.c t-j r11 .. ''''' ;I-<: !. l ,. Contc>l ll<'k<'l rctum wnh completed acttvity. I Name Phone: l Sl----<:"'.v,:-c.-TJ!!\ 't"..'f.l" ,,.,fc-.-.,.-, -:r" lc lvw t\0 "'"''""::u-. :,-, '""' :1Ph'f1)' ;J!'-<.1 no'ftC "M'CI, :;t:" 4 ll": t ... :J'"''J f"\i""' tA.h I;"' II .. ::,,,-__ ,. .. -,. fl., t ., fl,.; .. J IJ' ;.nt-O!> ....,,! e' wa>t '" "'7no;1 --:IOJ\. lr '''"1.1-.-ln" t,,. r.-&to"o{l'o! .. _,., ,.,,--,,;._.,Of!'(<'",._ .. J:A<)" l '4"'t:-'O: lc: ,.,,, .. ... "-" _1-<'>"'-,......,-! : ,. .. ,,' .:..! _, 1'-.......... !' -' p.t, ....... ,. .--..... ,,o; ....... "''t .. "'''.1'(.' ... -... ...-""''' ,..,.,wt :),tvu .. ,.. ...,,.,tlt,; .. I." .-.-_,,,., ... .,.,,._, ,.,.,llo<,..! ,. ;<.u ">;t ''' )r fU .lt_._.,,,, \l'o!-. ,,..,,,.. !l,e ., >to I. r;.--4-,.,, z, "''''" Jt 't.:l.'-': )-Jt. '_;,:, ,; ''"' !-or lt:t ...,.,.,.'".' .. NoOpmton Mos.t Agree 178 I ACTIVITY FEEDBACK: f1 y >I .. ,.,, !ttll ""Y '" II t h '!! 'J ,.,_ 1\Jc!: nre ph:,'SICailj' ar6 ely !!er m SO:::h::OOL :Ot:iVI j

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Revised Research Packets-for Main Sorting Exercise Responding in part to feedback and results from the pilot sorting exercise, I made changes to the research packets to improve the Q sort exercise and to encourage more parents to respond in the main sorting exercise (see figure 6-2). I presented the main sorting exercise packets in legal-sized envelopes printed with the title of my research, a raffle ticket, and due dates. The new presentation drew attention to the materials and identified them with the raffle so that they would not get lost on respondents' kitchen tables. On the raffle ticket, I requested the name and phone number of the person completing the packet and the teachers and grades of children enrolled at the school. That information helped me to eliminate duplicate entries and to determine response rates based on school enrollment. 179

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Figure 6-2: MAIN SORTING EXERCISE PACKETENGLISH (NOT TO SCALE) I :.!. "<"lter:..,r.br:n W "o:w..., ;te! ........ .,J,'rl .. !r."'--1 ':...:J.IJ .,..n ...... ,.n .... 8-........ ., ___ ... ... -_.-;; '-: .Jt>i .,.,-."--r) 'r"'- "' ,,,..,, . -, k ,. ..... ,,,._,. Most 04sagree _, INSTRUCTIONS I z .. "hlt"' '""'"' ;p ... .... --:<.<.>< "''"""'"' ,,..,,.,..,.,. un. )'"' Ltdl.,.., 1t ,,u'l ho.-d.,. '!"! .,..,v..,.luo.o<-th., "''" ,. .. ,,.,. ''"-'' <..t"'-""'' -1 n ..... "'" .... ....... t_..,,.,.,.,,..,tio l>, If, ..... "''' tt., Jia;>K ..-t-e-.e \lo;lH'<" :o .. t thr u..,. "'"""' -Y'"''" ,..-.._"' r..,..,-,,.,"!1"" !:-, .... .. ... ,.,,. ,u ,. ., .. -.u-. l'-" .. ,_ . ,.,.,., .. ; NoOoinion WHAT DRIVES PARENTS? Promot;ng o School Commute _____ l To 1hc groar.cl pnu dr-"9 (aP\4 pUo partyCO!Iteil). l'e!U!"P' I"StC"'FII!Itdoc;tiyoty GI'CI hckc"',. ')101.1'" chrlds OCTOBER 1. ZOOS To The. cklssroom plUG I'O'"'Y co.ntet:t oNy. rctum tt.cm to your tholes teochu NOVEMBR I. 2008 See de toils inside. L____ ____________________________________ __ 180 <:.,,.. n"'"'-"' lo:.< \-,;,o.r t,:o"'l>h' I''>'"" "'"..,''"c-"' wo:J f'C11rr> the t.,-,.,., .-, ''" z.,. ___ l'' .,.,,_ r,,. ..., . t... .-.t -,,,," 41 .:. .. ,..,,,, (_,.., .. :r.-..oc L;u-..... rccv .:"1'"-"" .;: .,-rc rt ., >:l::r'l. ,.,,., ... ..__, . 'lo-or >t'J > I'! I IV--... "") r ,. "::o:t<..k :t-,., ,.t..ol,.r .. r,. Hr 1-''a.:o, .. ,..._.._, __ .. ,J.., 1-'i.t :-, ,., .. ,;:;,-,-r.,_J L., .. b<:t",.i'"l lr,..-r lit" f uld,.tl.l fl .. .. ... Most Agree fl"""'' .,,.. ,,,,.,.",.,..; .. ,l ft"''' ,., .,,. .. U"'l!'" ,0,1-v' ,..._,,_, rl. "''! '" wJ'I 'r.r tl ... l>h;.>V.W: H..,!lh" 011 10 ..... lu qrnn.-J "'Q" dra-n9 H.c!U(> t'!' rof D17.ll P"l'1)' ;osv; Kic!; who arB p-hysically arle !l'J bi
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The main item in the packet was again printed on an 11 x 17 sheet and folded as a pamphlet. The front of the pamphlet contained a revised, shorter introduction to the project and raffle that emphasized issues that had been problematic during the pilot sorting exercise. For example, I reminded parents to return only one packet per household and explained where to find replacement materials. The front of the pamphlet still illustrated the raffle's grand prize to catch the family's attention when they opened the envelope. The back of the pamphlet, contained a revised questionnaire requesting information about the respondent's household. Inside the pamphlet and under the sorting diagram, I removed the feedback questions and used the space to expand instructions for completing the sorting exercise and to remind parents when the completed packets were due In response to feedback from the pilot study and to prevent unnecessary frustration, the new instructions offered alternatives to using the stickers, such as taping or gluing the stickers to the diagram or writing the three-letter codes in the spaces. Finally, I redesigned the bundle of stickers so that the statements were easier for respondents to read and so that the three-letter codes did not get covered or cut off in production or during the sort. In addition to the electric bicycle that I offered as the grand prize, the new raffle tickets announced Target gift cards as second prizes and pizza parties as awards to classrooms that returned the most Q-sorts. The Children Youth and Environments Center for Research and Design and Little Caesar's Pizza donated the additional prizes. I listed all of the prizes in the project statement and on posters that hung in the 181

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schools' administrative offices from the beginning of the fall semester until the beginning of November (see figure 6-3). I associated the research with a raffle and other rewards to address a possible self-selection bias that favored the more "involved" parents. Figure 6-3: POSTER-ENGLISH (NOT TO SCALE) ENTER TO WIN I Schwinn Electric Stingray Bicycle ADOITIOOAL.ffilZES Target Gift Cards Classroom Pizza Party DEADLINE October 1 2008 see Details Inside Packet WE NEED YOUR OPINION TO IMPROVE THE SCHOOL COMMUTE In addition to revising the design of the packets, I also improved data I collection processes by making revisions to data collection tools that improved the substance of the research. In the next two sections, I describe the data collection instruments, including the sorting exercise and the questionnaire and their changes 182

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Q-sort exercise A sorting exercise, known as a Q-sort, served as the main data collection instrument for this quantitative study. Similar to other psychometric tools, the Q-sort exercise provides an opportunity to identify variations in subjects' attitudes about a given issue (Brown, 1980, 1993). It asks respondents to rate statements that represent a range of attitudes, from "I agree with these statements the most" to "I disagree with these statements the most". In contrast to other tools, the Q-sort allows respondents to reflectively rank statements in relation to each other, which enables the researcher to evaluate their perspective as a whole (Addams & Proops, 2000; Brown, 1980, 1993; Vogel & Lowham, 2007). Respondents represented their perspectives by organizing the statements in a forced, quasi-normal (similar to bell curve) distribution single-centered around a mean score of zero (see figure 6-4). By enforcing the distribution, the Q-sort compelled respondents to consider statements in relation to each other, rather than rating them independently. Although I encouraged respondents to conform to the distribution, I gave them license to deviate from it as necessary to properly represent their perspectives. For example, a respondent could attach more or fewer statements to a certain rating column than the number of spaces that the diagram allowed, and it would not disqualify the Q-sort. As long as the respondent included all of the statements somewhere on the diagram, I could include the respondent's perspective in my analysis. 183

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Figure 6-4: SAMPLE Q-SORT DISTRIBUTION DIAGRAMNOT TO SCALE -4 -3 -2 -1 0 +1 +2 +3 +4 Instructions: I Feedback: defining the concourse-Q set for pilot sorting exercise. The first step in designing my Q-sort exercise was to select a Q set, which is a set of statements selected from a larger conversation on the issue of interest, often referred to as the concourse. The number of statements needed to perform a Q-sort exercise depends on the purpose and resource constraints in the study. For example, McKeown and Thomas (1988) recommend using 60-90 statements to ensure that points ofview are represented adequately. In contrast, Addams and Proops (2000) claim that typically 30-50 statements suffice for a comprehensive but manageable sorting exercise. Brunner (1987) suggests limiting the Q set to 32 statements "to minimize the time required for busy respondents to sort it". Ideally, the Q set should be large enough to represent the range of policy angles commonly discussed in language familiar to respondents and should be small enough to be manageable for a short sorting activity (Addams & Proops, 2000; Brown, 1980). In addition to the pragmatic consideration 184

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of respondents' time, the smaller Q set is more manageable for computer processing. Drawing from the journey to school concourse, I selected 36 statements each for my pilot and main sorting exercises. In this section, I describe the structure of my Q sets and how their revisions addressed problems encountered during the pilot sorting exercise. Defining a concourse regarding active travel for the commute to elementary school posed a unique challenge because the topic was more subtle than controversial. Van Eeten (2000) describes Q methodology as being uniquely suited to the analysis of highly controversial, intractable issues. Because schools are unlikely to impose commuting policies that threaten or even markedly inconvenience parents, feelings on the subject do not achieve levels of polarizing contention. In the context of a polarized debate, the definition of the concourse can reflect the continuum of political thought. However, less contentious issues can similarly benefit from the study of subjectivity by identifying nuances in respondents' attitudes that relate to their behavior. In May 2008, I conducted a pilot Q-sort using an inductive, structured, quasi naturalistic Q set that I had derived from a preliminary review of planning literature about active travel. The Q set was inductive because the themes that I selected emerged from patterns I had discerned in the literature, rather than representing an a priori theoretical consideration (McKeown & Thomas, 1988). The Q set was structured inasmuch as I attempted to represent the full range of policy angles 185

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commonly discussed in the literature, including environmental activism, safety, health and wellness, independent mobility and time. The Q set was quasi-naturalistic because it drew perspectives from sources outside of the study population. I prepared the Q set using a thematic 6x3x2 factorial design that included six policy angles, three geographic scales, and issues versus values (see figure 6-5). By selecting statements that fit the factorial design, I ensured that the statements would not overlap in purpose, but would approximate a stratified sample of the discursive topics. I initially tested the pilot Q set by conducting an informal focus group with graduate students while they completed the Q sort exercise for a class project. Analysis of the pilot Q-sort data revealed two key concerns that I addressed for the main Q-sort. First, the thematic factorial design that I used to select the range of statements reflected strongly in the results, suggesting that the process inadvertently imposed external theoretical constructs rather than grounding theory in the empirical data. Since the main purpose of this study was to characterize and compare types of attitudes based on empirical evidence, a grounded theoretical approach would be more appropriate than hypothesis testing. Second, approximately one quarter of the statements elicited very weak responses and ultimately did not contribute to the pilot attitude types, suggesting that the statements may have been inappropriate for the local audience. 186

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L8l t1t "r' "' .:r:o,tt :'Ill! .\11:;-:or<.\qd (I 'tli ;_.; \ (()' hlrt!TJIM Jot I" \tu.-.td wtt rww;"Ptiit:ou mo j no ..;pi( \lli ii'll' .!1: ; Jri\\ 1!.,.1 r.1 ;\u :a. \ _r.-l(f !" \ 1J(l i ltu!\\ I ll:>lt'' rl -'1-1;-''I <11 pno>;:;' '1 ''1 II ; ToiO -----ir:tll ro ':'1\l.'flll:>tb;:mr ;o .,u.1s r: ;J.'.'11 !Pl('ll\1.) : Hit-; ''flt: in" ,">iii' li.-\1Jt\ .'-q j!ftij,ij iPlj>pij: ) ; \iJr.til':il.t i.f loi. 'l' ii:i riff i t[:' i p1111rr 17' ... r..'t'. II." III ''"'ll'l IS'V'\ I rJ t'' 1: 1 poi ; . 1 iti: . ..... \."11;1 ........ .>pt:!.'P liP I.J,1rP.!llf" \,It'.; .;.ii ,,. p ..... i.i iiJi' .. ;;t.i.'ip1 \iit iftil'tn:>,1itr.ii .::.:.-;'i' li.i.,h: r + 1,ri-.: .... -:r.r p:onw:.-i1.1 :11 .""-' .r, 11.1 tf1J!IP l.lo> 1p11111 , PliJ ; f'iri! \i, ; :;:-,6,1 i"t'ire ;; ,,'fl'' ;i>,q-... L Jii.'XI< IF''' \lllti 'I PJI'II'.\1 p:..1lp'> (!J II.Hilftlf' :'lllll!l! \\ .\H r Hir .\Tl r l' l -':':1' j"JJ 'fll"'i :: .... J :'\1; 1"1 ,'-li .. 1'"711JI.c-::-. 1 r1 r ,Jiiiiil.: .... 10:1 'll" '' ,;i :.:r. \::-: ; : i i li( .. jlt'l :,\1i a.-; 'iiti :\.1 r 1ir.a ;:"H, r ; .;rfi15itcr..:, .... ,,.:.\. -,,(;t' "i"i-;1-r'l! a ... :fuffji! \\. r :11. 1 1'1"-IJ 1 l!llt>IJI!I.I I! >I 'i! '_IPJ('I'\1.) .\Hoi _,,,, . 1 .. .... u : li:\i'l''ri4.) tn.J -ntt::>o.j'lt:'ttf :;rn,1t. p :. Dlfl! -\r.1< po>flfl1'..;1'-t3L:->IJ lilo!>lll ,:,,, . iff' ; ";:ti'i" .. . '>fi: iit!:i ii:.'ii'ti'lf.)--1i:fs :m;.; ... ur iirq r. :>tfl f!Uno>n: :>tfp!I[ ; \ rs p.-."ttwqq.-i:ih>\\ I i I11S .t. 1.'1-iYS iii: .r,.'if iir..1 i"''!:iii i>l rii:Oui'1. W \\ .\Wi ., "l'F' 'IL'J\ll'tll 1.,\pi> pur. )t,-.IJp o'ir;l :ill! \ uc:ru 'lll\-' l [f11 ... . -v.j ii i;,;:;j,. _;if ;>i Jl \ :' : \rfj s --yyy 'JI.I.>lll ;;Ill P:")tUJ 1>1 llr.."'l ;r.tj 1\ .op .(;j ,1;1lll'l j lir.]m.;(l Iii 'I 'fl ,\,n. 1 iii -iiiiiiiii r ;, :1 ..... ...... -... --l3S 0 l011d

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revised Q set-for main sorting exercise. In response to the aforementioned concerns about the pilot Q set, I used data from my qualitative study (described in Chapters Four and Five) to refine the Q set for the main Q-sort exercise. In the first stage of content analysis of my qualitative data, I organized text from interview transcripts into thematic clusters, approximating the open coding process of grounded theory (see Chapter Four). I selected statements from the initial thematic clusters to use in the main Q set. My selection resulted in a quasi-inductive, naturalistic, semi-structured, Q set of 36 statements reflecting local perspectives about the issue of active travel for the trip to school (see figure 6-6). Although the revised Q set represented themes that emerged from the interview transcripts, it was quasi-inductive because I purposefully selected similar proportions of statements representing opportunity-related and propensity-related facets of the issue so that I could test Chapin's (1974) conceptual framework. The Q set was naturalistic because each statement was taken directly from the language of respondents. The set was only semi-structured because it represented a hierarchy of discursive themes, but did not fall into a strictly factorial design. I tested the revised Q set by informally interviewing a group of colleagues while they completed the sorting exercise. I made minor adjustments based on their feedback. 188

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Figure 6-6: MAIN Q SET .. _ . !J.'JJI.. . ltl!l '1_f .a I' rs : plar> ;il;:ir JI"Llll:td llt..:lr lnps II ;;dh"h >l. : rrw ; v.orkmg tim..: to w.alk tu ; _1'1:\.1. _sa,_o:_ tinh: h} wrth .. -rr.anJs. 1'.-\CI: { '01'\\l: :'\I i.:'\t. L ...... :J)r_i_\ i:} \ ':':f.B'l}!IJ ... .... .J Pet' Park mg. ;.t the sd11MI b a p.t< n U .'5JJ . ___ J .. b .. a. bL1n.:j __ _,jiJ.J . P.-\( 'I:-St R \'Eli.L\:'\( I. t -------.... --.____ ,_ _ ---.. --; \Jn the s;rt:.:r_ ii'Jht:1 in :!rVLlJ'S. I'SI Thcr .. -i.lt\:!l._t in I'S_ ( If ydkd f,,r h d p 111 '-'ur .. on..: W'::!c.tiJ pn>h:d I'S, \ .... } _ 10_ It !{J. UU:. 1_:\I)L l' t.0J>l: I 01 1 . :mo:o 1: i( fO h:o s, h11o.d 1 ;;.-..:. :--.n It's fu n f11: kid s w -Ll 111 sdJO:x)l w11h their i ;--..!!.. .. ___ -;_aJll.'o..' .. J(.IIW.Y !'II:\ _ . L __ .1\;J...: ..;r_ td 1'03 .. ,l_\\t: .. !\ lJHLliL-Ll! : .-\IJ t l .. __ __ _ .... ___ . .. ___ : _I J ; L lit ts '-o:t} imrut1jn; 1 _f;tmi I y ; .:--..! 1_,\ j lh.' po1n cr(I,;J. t:hriJr..:n s li\ ..::; : We tll.b .. lak a.s p_,,,,ihk \.09P . __ J __ t11 hh. ; ; wlw arc ph_y,-i;:i.lll:_. ;:tdi\L' hcn.:r m ; :>; ____ ;_ . \ : .0PS. Par .. \\llll.. ,,; th.:1r when liHl:'li\U:N l -. ..... _. --; ('J:! ( ?f:. _!t:lj-. . . _. :. .. 1\) l_('J:J .... _,,_ur : q :_\\_: \\),'.11lJ.r _is .J!ld hjku1: tv r-IR:\f l'lC m mrr . ....... .. ... .. c........ ...... ... . . ....... .... e . . ... . .... .............. ( I 1-1. l Tn.rc ;u.: till maj\H wee n 1Kif and ; (' J_l? Ll)).:r 1 l!':J.!l_ i I!:..\ I Dl{l.:\D . (J_)I'_ _ CDS ('I)( CDK. !it:-:. _l'.UJ. l))o..:r.: .+.:a.l 1.: td,_,_ [:-:,}j\l_cll\ the I E !ho:y if a it will :.!'-' L >-l .h:!l ,1: \ Jti:.laliHd KiJ..;. pr,'ll!\.'lfL'Il fn1m o:h.:r ).;ids JnJ 189

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Questionnaire In addition to completing the Q-sort exercises, respondents answered short questionnaires with multiple-choice and fill-in-the-blank questions to provide information about their households and to complement the sorting exercises. Following the pilot study, I made changes to the questionnaire to address problems of ambiguity and to facilitate data analysis. Below, I describe the questions for each study and changes that I made to support research objectives (see figure 6-7). distance. Because distance has been shown to influence mode choice, the pilot questionnaire asked how far in blocks the family lives from the school. This question also assisted with sampling for cluster analysis. Many respondents in the qualitative study described their distance from the school in blocks rather than miles or other measures. However, some respondents had difficulty recalling the distance in those terms. To address that problem, I included an additional question in the main study questionnaire, asking respondents to name the streets in the intersection nearest to their home. That information provided sufficient information to check approximate distance and to map respondents without compromising their safety or anonymity. 190

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Figure 6-7: REVISED QUESTIONNAIRE --v---------- -------- - -, _._._._ ------ - --- ----Questionnaire: C1rclo. fllltn or the appropnate msponsl:! i I 1 Blocks fwm schooL 0-3 4-6 79 10+ j 2 Closest intersuctton to horna : and I --------------! 3. Dwelhng type: Other: _____ 14. AduUs ifl OU1er : 11ouse Apartrrnm t Mother Father -------1 5 Stay at home adults : Other: ____ I 6 1 Gets Children to schtYJl. Ott1er: _____ Muttler Fa1twr i 7. Workslvoluntaurs at schooL MQtner Fa!t1er Omor: _____ i 8. Pnrn.ary at heme Enyltst; Spa rush OtMr: _____ __ Wtuta Htsparm : j 9. Black. OliHir --10 Grelde l,. .. e l s of all children : Buys _____ Girls -----11. Afte r -school programs? Befure-schuol pn:;grams? 13 Household incornl:! : 14 Number of cars: Often Scme1tllHJs Often Hardly Hardl y ever less than 12 000 36.000 tu 72.Gt"\J 12 .0l}:J to 36.0-:JO 12.0t.W or hrghar 15. How do y-Ju gel your children lo sdHYJl? Mark. all apply: carpoul wrth other rarr11lws. _They the bus. 'WfJ walk or btke i n g-ood weather. I dnve them both ways. _They go with srblings or _We walk one way dnva one way. _'We drop them uff on wa1 to .vork. Other-: _____ 16. Wha1 Influences hov' you go9t your dlild ren to sehoul? 1 :r. Wh.m yuu to walk or your dHidren 10 school? 191

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household characteristics. Household characteristics have also been shown to influence mode choice for school trips. The pilot questionnaire asked several demographic questions to characterize household members. Specifically, it asked how many adults are in the household whether there is a full-time parent at home, the ages and genders of children in the household, the racial, ethnic and language backgrounds of household members and approximate combined household income. The revised questionnaire asked similar demographic questions, but requested grade levels of children rather than ages, and used a multiple-choice format for race/ethnicity and language. It also asked additional questions to estimate the family's economic status: dwelling type and number of cars. These questions provided data for calculating response rates and allowed me to gauge how well the data represented the population at each school. schedules. To learn about the family's activity schedules, the pilot study asked about students' participation in extracurricular activities, and about parents' volunteer or paid employment at the school. An open-answer format of the question about extracurricular activities resulted in data that were too detailed to be useful, so the main study requested only the frequency of participation in beforeand after-school programs. Both questionnaires asked who takes responsible for getting the kids to school and used a multiple-choice, multiple-response format to ask how they get there and other characteristics of the trips. 192

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travel mode preferences. The revised questionnaire added two open-answer questions taken from the interview protocol of the qualitative study -to allow parents to explain their travel mode choices in their own terms, and to encourage them to speculate about conditions that might encourage them to walk or ride bikes to their children's schools. Because I collected data for this study using a self completion format with a large number of respondents, these questions substituted for the short interviews typically conducted during a sorting exercise. Recruitment Procedures As I described in Chapter Four, volunteering and working with the four Safe Routes to School programs helped me to gain access to key informants at each school, including school staff and parents. I recruited interview subjects for the qualitative study by contacting parents during drop off and pick up times outside of the schools. I also conducted short, impromptu focus groups with parents and school staff during PTSA and SR2S planning meetings. However, DPS regulations regarding research in schools denied me access to lists of enrolled students that would include contact information for distributing questionnaires and sorting exercises to families. As a result, distributing materials and following up with parents presented a unique challenge for this part of my research, and I significantly revised my approach after the pilot Q-sort. 193

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Pilot Q-sort During the pilot Q-sort, I approached data collection passively and subsequently achieved poor response rates. Specifically, only 63 parents returned entries representing 100 students or 5.3% of total enrollment at the four schools (see table 6-2). At the beginning of May 2008, I distributed the research packets to parents of every student in the four participating schools through the students' take home folders and invited parents to complete and return them to their children's teachers or to the administrative office within a week of receiving them. By completing and returning the packets, parents indicated their consent to participate in the study, and were entered into a raffle to win an electric bicycle. This method did not provide the opportunity to follow up with non-responsive parents, or to have direct, verbal interaction with the respondents during the sorting activity. Table 6-2: Pilot study response rates "0 ....,"0 Q) c:: .... "' c:: "'"0 Q) c:: "' .... Q) .... Q) a Q) t:: c "' == Q) Q) Q)-0 "0 .... "0 0 0 .... "' :l 0. ;:l .... .... 0. I .... Q) .... c:: c Q) 0 r/:!0:: trJW CJ.lO:: # # # % ..... Force 18 29 585 5.0 "0 Steck 11 19 292 6.5 B r/) .... Lowry 18 30 458 6.6 Munroe 16 22 564 3.9 0::: Total 63 100 1899 5.3 194

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Main Q-sort For the main Q-sort, I approached data collection actively, improving the packet design, offering additional contest prizes, and using a number of follow up methods to improve response rates. The revised process still lacked follow up with non-responsive parents, but the active approach for distributing materials and collecting data, and the revisions to the packets resulted in much higher response rates. Specifically, 650 parents returned entries representing 1,110 students or 38.8% of total enrollment at the seven schools (see table 6-3). Table 6-3: Main study response rates "0 .,_.-o Q) = ...... Vl = Vl"' Q) = Vl ...... Q) ...... Q) E v t = Vl ="' Q) Q) vQ) 0 "0 .... "0 0 0 .... Vl ::I c. ::I .... .... c. I ...... Q) ...... = = Q) Cl r:/)Q:::; r:/)U.) UJQ:::; # # # % Edison 195 334 518 64.5 Sabin 117 201 565 35.6 -6' Slavens 96 171 466 36.7 ::I Cory 91 142 392 36.2 ...... (/) = Bromwell 54 96 323 29.7 Philips 45 82 210 39.0 Valdez 52 84 385 21.8 Total 650 1110 2859 38.8 Procedures for distributing the packets differed at each school, but included delivering them individually to parents during back to school nights and during pick up times, sending them home to parents in the students' take home folders, and leaving additional copies in the administrative offices for parents to pick up when 195

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they signed in. Administrative staff at the schools printed short descriptions and reminders about the project in their newsletters (print and online) and announced the deadlines each week during their morning announcements. Timing and other factors may have also played a part. For example, school staff explained that in the last weeks of the spring semester (when the pilot Q-sort occurred), parents receive a larger number of announcements in their children's take home folders than at the beginning of the year (when the main Q-sort occurred), which may have caused them to opt out of the pilot research. Response Rates and Demographic Representation I calculated response rates based on the numbers of students represented by returned Q-sort packets divided by the total number of students enrolled at each school (see tables 6-2 and 6-3). Although the data represent parents' attitudes, information about the numbers of families at each school was not available. Because I distributed Q-sort packets through student folders, many families received multiple copies. I allowed each family to submit only one Q-sort packet, and used the list of students' primary classrooms that they reported on the entries to calculate the number of students that they represented. I used information from the questionnaires to eliminate duplicates and otherwise invalid entries. For example, a Q-sort would be invalid if the respondent failed to include any of the Q set statements on the diagram, 196

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left significant portions of the questionnaire blank, or answered questions with inappropriate or illegible symbols that could be misinterpreted. Response rates were consistently low at all four pilot schools, ranging from 3.9% to 6.6%, with an average rate of 5.3% of enrolled students. In contrast, the average response rate for the main Q-sort was 38.8%-over seven times higher than the pilot-with individual schools ranging from 21.8% to 64.5%, despite relative consistency in data collection procedures. Table 6-4 presents the demographic composition of respondents based on the percentages of children of each racial/ethnic group who were represented by the respondents (R) and compares those numbers with the racial compositions of each school's enrollment (E). As indicated in the columns labeled R-E for each category, the racial/ethnic composition of the entries is not equal to the composition of the schools' student enrollments but approximates the proportions in most cases, and is closer for the main Q-sort. I highlighted the instances in which the difference between the entries and schools exceeded an absolute value of I 0%. The variance indicates a sampling bias that may also be explained by the respondents' family sizes, and by 1.9% to 6.6% of entries from each school that did not disclose racial/ethnic information. Overall, the composition of respondents included significant demographic groups from each of the research sites, although proportions were not representative. 197

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Table 6-4: Response rates by race/ethnicity %White %Latino %Black %Other Sch. Ent. E-S Sch Ent. E-S Sch. Ent. E-S Sch Ent. E-S (Pilot) Force 7.5 13.8 6.3 89.4 86.2 -3.2 0.7 0.0 -0.7 0 0 0 0 0.0 Steck 76 7 89.5 12.8 9.6 10. 5 0.9 7.5 0.0 -7.5 0 0 0 0 0.0 Lowry 52.4 66 7 14;3 16.6 3.3 -13.3 28.8 13.3 -lS.S 0 0 6.7 6.7 Munroe 21.5 4 6 -17.0 71.9 95.5 23.6 1.5 0.0 -1.5 0 0 0 0 0 0 Total 33. 1 42. 0 8.9 54.4 49. 0 -5.4 8.8 4.0 -4.8 3.7 2.0 -1.7 (Main) Edison 46 9 59.3 1 '2:.. 47.3 30 8 ..... 3 0 0.9 -2.1 2 8 6 6 3 8 Sabin 26.5 22.4 -4.1 62.1 61.7 -0.4 3.5 4.0 0.5 7.9 10.0 2 1 Slavens 89.3 89.5 0.2 5.7 1.8 -3.9 2 2 1.2 -1.0 2.8 4.1 1.3 Cory 79 1 84. 5 5.4 12.7 6 3 -6.4 3.5 2.8 -0.7 4.7 4.9 0.2 Brom. 80 7 88. 5 7 8 3.7 0.0 -3.7 5.2 0.0 -5.2 10.4 10.4 0 0 Philips 3 5 12.2 8 7 13.0 19. 5 6.5 83.4 62.2 0 1 3.7 3 6 Valdez 2.3 19.0 16:7 95.4 71.4 -u:o 1.1 0.0 -1.1 1.2 7 1 5.9 Total 48.8 56. 5 7 7 37.7 28.4 -9 4 8.9 6 1 -2.8 4 5 6 8 2.2 Sampling for Cluster Analysis Q methodology is oriented towards intensive behavioral analysis of individual or small groups of subjects, but can also be used for extensive studies that aim to identify a range of viewpoints present in a population (Brown, 1993; McKeown & Thomas, 1988). In that respect, the Q-sort technique differs from a traditional survey because it does not require a large randomized sample of respondents (Addams & Proops, 2000). Addams and Proops describe the necessary scope of sampling for the extensive Q study: 198

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"As a consequence of the expectation of finite diversity, the number of participants does not have to be large. What is required is that these should be deliberately selected to reflect the widest range of potential types of opinions in order to identify all factors that exist in relation to the topic ofresearch" (Addams & Proops, 2000, p. 21). In this context, the term 'factors' refers to viewpoints identified through cluster analysis of respondents' perspectives. Brown clarifies that even with an extensive study the selection of respondents should include "typically no more than 40, to assure the comprehensiveness of the factors and the reliability of the factor arrays" (Brown, 1980, p. 92). He explains that the numbers of respondents loaded on any one factor is inconsequential, and that it only requires 2-3 to establish a common factor. The purpose of Q technique is to establish the taxonomy of views, rather than to weigh them against each other in occurrence. The purpose of cluster analysis in this research is to identify prominent types of attitudes held by the population most likely to increase their rates of active travel for the school commute. Using the schools' take-home folder systems, I sent the sorting activity to a much larger population than I hoped to include in the study Based on the overall response, I selected up to 40 respondents to be included in each cluster analysis. In the next section, I present the sampling strategies that I used for the pilot and main studies and the resulting sample characteristics. Although I present the research sites by program as I have throughout the dissertation. 199

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pilot study Q sample. Although a large sample of respondents is not recommended for cluster analysis, the low response rates in the pilot Q-sort presented a problem because they did not allow me to select the widest range of potential types of opinions at each site. In some respects, characteristics of the samples were homogeneous. For example, all but two of the respondents from Steck were affluent and White, and all but one of the respondents from Lowry lived more than ten blocks from the school. While the homogeneous sample at Steck reasonably represented the population at the school, the sample at Lowry did not. Respondents from Force and Munroe represented the schools more closely. Both samples were nearly all Latino, and included appropriate ranges of self-reported household income levels and distances from the schools. To address the low response rates in the pilot Q-sort, and to achieve the widest possible range of potential types of opinions, I combined four schools' respondent pools rather than performing cluster analysis at each school. The combined sample allowed me to examine variations in attitudes about the school commute and to determine whether those variations coincided with certain demographic characteristics (see table 6-5). 200

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Table 6-5: Pilot Q sample demographic characteristics ------{/l {/l 0 0 --0 0 '-' '-' '-' '-' (.) (.) (.) (.) Q)0 Q)0 z Q) 0 0 0 e-o e-o ..... .g-.g-ca Q) .s {/l.D {/l.D ..... -\0 -\0 .5 v .5 1\ 0 ....l co 0 Cl v Cl 1\ f-; Steck 10 0 0 0 5 5 0 10 10 Lowry 7 0 3 0 1 9 2 8 10 Force 1 9 0 0 7 3 6 4 10 Munroe 1 9 0 0 6 4 9 1 10 Total(#) 19 18 3 0 19 21 17 23 40 Total(%) 47.5 45.0 7.5 0.0 47.5 52.5 42.5 57.5 The resulting sample is representative of the larger study population in terms of race and income. Percentages of White and Latino respondents are consistent with school enrollments, but the percentage of Black respondents is lower than the average (see table 6-4). Proportions of low and high self-reported household income levels are nearly even, with a slightly higher percentage of households reporting higher incomes. Distances from the school are also nearly even, with about half of the respondents living within six blocks of the school and about half of the respondents living farther than six blocks. main study Q sample. Response rates for the main Q-sort varied, but were sufficient to enable cluster analysis on attitude types within each school instead of combining them. Rather than selecting respondents who represented some 201

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combination of attributes of the school's total population, I selected a sample based on a series filters. Since the initial number of responses differed between sites, I subjected some schools to additional filters so that the number would fall within the desired parameters of 20-40 cases. I applied the exclusions sequentially as described below (see table 6-6) Table 6-6: Main study Q sample filters School No Filter #I Filter #2 Filter #3 Filter #4 Filter #5 Final Filter Q-sort Distance Survey Student Child in Count Incomplete more than Incomplete takes the ECE (n) 10 blocks bus Edison 195 -34 -86 -26 -1 -9 39 Sabin 117 -16 -52 -6 -6 X 37 Slavens 96 -9 -27 -13 -4 -7 36 Cory 91 -10 -51 -2 X X 28 Bromwell 54 -1 -26 -2 X X 25 Philips 45 -7 X -5 X X 33 Valdez 52 -7 -18 -5 X X 22 ./ I ,65o I '/1 '' I I . I ,, . ./' I Total -84 -18 -60 -11 -16 220 The first filter was based on completion of the sorting exercise Sorting exercises that were missing any of the statements from the Q set could not be included in the cluster analysis because the exclusion or replacement of missing statements alters that case s perspective It cannot be assumed that missing statements would fit into blank spaces on the form. In some cases, respondents were excluded because statements that they taped or glued to the form became detached during transfer. Resource limitations prohibited follow up contacts with those 202

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respondents to verify the sorts, so their data were used for descriptive statistics, but not for cluster analysis. The second filter was based on perceived distance from home to school as measured in blocks. A number of studies show a negative correlation between distance and walking (Krizek, 2003; McDonald, 2007). Open enrollment policies have made it possible for students to attend schools that are not in their immediate neighborhoods, and sometimes many miles from home. Addressing this obstacle would require policy change at the district level or would require parents to relocate to live nearer to the schools. Since these changes fall beyond the reasonable scope of active travel intervention, these families are excluded from the analysis. I generally excluded respondents who marked 1 0+ blocks from the cluster but I made an exception to that rule for the Philips site because so many respondents would have been excluded that cluster analysis would not have been possible The third filter was based on completion of the questionnaire. Although data from the questionnaire do not prevent analysis of attitude types, completed questionnaires made it possible to analyze the best specimens from each cluster in terms of other socio-demographic variables. For that reason I excluded respondents who did not answer important parts of the questionnaire from the cluster analysis but included them in other statistical analyses. The fourth filter was based on travel modes used in the journey to school. Students traveling by bus presumably live farther from schools However, for some of 203

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the research sites, the DPS school district assigns bus routes for students whose journey to school includes major road crossings. While the bus-riding students could be encouraged to walk to school despite the major intersection, the infrastructure improvements that would be necessary to ensure their safety are typically not attainable given budgetary constraints. Also, students riding busses normally walk at least a portion of their trip, and do not contribute to traffic problems at the school. Since active travel interventions aim to encourage only a marginal increase in trips made by walking or biking, it is reasonable that bus-riders would not be targeted For that reason, at sites with larger respondent pools, including Edison, Sabin and Slavens, I excluded respondents who marked that their children go by bus. It would also be reasonable to exclude respondents who marked "0" for the number of household vehicles or who only marked that their children walk to school since theoretically, their rates of active travel could not be increased. However, the data supplied by the completed questionnaires were not reliable for assuming those conditions. The final filter is based on the grade level of students at the school. Children's age is frequently noted in literature as well as interviews as a factor that limits walkability. ECE and Kindergarten, in particular pose concern because in addition to the children's relative size and ability, those programs are sometimes offered as half day sessions, which mean additional trips to and from the school for parents who have children in multiple grades. At sites with larger respondent pools, including 204

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Edison and Slavens, I excluded respondents who marked that they have children who attend ECE. characteristics of resulting main Q sample. The resulting Q sample includes demographic characteristics of the larger study population in terms of race and income although the numbers are not proportionally representative (see table 6-7). Percentages of White and Latino respondents are consistent with school enrollments, but the percentage of Black respondents is lower than the average (see table 6-4). Proportions of low and high self-reported household income levels are nearly even, with a slightly higher percentage of households reporting higher incomes. Distances from the school are also nearly even, with about half of the respondents living within six blocks of the school and about half of the respondents living farther than six blocks. 205

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Table 6-7: Main study Q sample characteristics til til tll,.:.d a....:.d 0 ...... til (.) 0 (.) Kl 0 0 e. tllO .... 0 0) .= ,.-.. tllo oo -.. =#: ,.-.. e -(.) =#: =#: ,.-.. --o \0 tii ,.-.. '-" '-" '-" =#: (.) )( (.) )( c:: 0 '-" "Vi v; SfA e{A '-" c:: ,.:.d ..... ;;.. ca .g (.) 8 8 c ..c:: ..... ..... a.s a.s c::..c:: Q 0 ....l c:Il 0 .......... -..... fEdison 29 8 0 2 18 21 8 31 32 39 Sabin 9 18 3 6 28 9 19 16 31 37 Slavens 33 0 1 2 26 10 2 34 26 36 Cory 22 2 3 1 20 8 4 24 21 28 Bromwell 22 0 0 3 17 8 4 21 19 25 Philips 4 7 19 2 9 24 25 7 24 33 Valdez 5 17 0 0 19 3 14 8 13 22 Total(#) 124 52 26 16 137 83 76 141 166 220 Total(%) 56.4 23.6 11.8 7.3 62.3 37.7 34.5 64.1 75.5 100.0 Edison A total of 195 families at Edison returned the sorting exercise (by November 1st), representing 334 students or 64.5% of the school population. Race/Ethnic groups are reasonably well represented based on statistical estimates provided by the Piton Foundation. I selected a sample of 39 cases from among the 195 respondents to be included in the cluster analysis based on Filters 1-5. Sabin A total of 117 families at Sabin returned the sorting exercise, representing 166 students or 29.4% of the school population. Race/Ethnic groups are reasonably well represented based on statistical estimates provided by the Piton Foundation. I selected 206

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a sample of 37 cases from among the 117 respondents to be included in the cluster analysis based on Filters 1-4. Slavens A total of 96 families at Slavens returned the sorting exercise, representing 109 students or 23.4% of the school population. Race/Ethnic groups are reasonably well represented based on statistical estimates provided by the Piton Foundation. I selected a sample of 36 cases from among the 96 respondents to be included in the cluster analysis based on Filters 1-5. Cory A total of 91 families at Cory returned the sorting exercise, representing 13 7 students or 34.9% of the school population. Race/Ethnic groups are reasonably well represented based on statistical estimates provided by the Piton Foundation. I selected a sample of 28 cases from among the 91 respondents to be included in the cluster analysis based on Filters 1-3. Bromwell A total of 54 families at Bromwell returned the sorting exercise, representing 86 students or 26.6% of the school population. Race/Ethnic groups are reasonably well represented based on statistical estimates provided by the Piton Foundation. I selected a sample of 25 cases from among the 54 respondents to be included in the cluster analysis, based on Filters 1-3. 207

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Philips A total of 45 families at Philips returned the sorting exercise, representing 63 students or 30.0% of the school population. Race/Ethnic groups are reasonably well represented based on statistical estimates provided by the Piton Foundation. I selected a sample of 33 cases from among the 45 respondents to be included in the cluster analysis, based on Filters 1 and 3. Valdez A total of 52 families at Valdez returned the sorting exercise, representing 65 students or 16.9% of the school population. Race/Ethnic groups are reasonably well represented based on statistical estimates provided by the Piton Foundation. I selected a sample of 21 cases from among the 52 respondents to be included in the cluster analysis based on Filters 1-3. Data Analysis and Interpretation I used SPSS software and cluster analysis to examine selected Q-sort cases. For the pilot study, I analyzed a total of 40 cases from the four selected research sites. For the main study, I analyzed between 20 and 40 cases for each research site, based on the purposeful, exclusionary sampling described above. Data analysis involved four key steps that I describe in this section: determining the similarity between cases, identifying core perspectives, substantively characterizing each core perspective, and determining which cases best represent each core perspective. 208

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Similarity Between Cases -the Agglomeration Schedule Cluster analysis using the Pearson correlation coefficient produces an agglomeration schedule and dendrogram that describe the similarity of z-scores between complete respondent cases and helps to determine clusters also commonly named core perspectives or factors. I performed cluster analysis using Between Groups, Nearest Neighbor and Furthest Neighbor algorithms and determined that Furthest Neighbor (also called Complete Linkage) revealed stronger, more identifiable clusters The maximum coefficient in the pilot study agglomeration schedule is r2 39=.788 (see figure 6-8). As indicated in the table, cases 2 and 39 were the most similar without being identical, so they combined (maintaining the label 2) in the first stage of clustering The process continued as the cases with the closest z-scores were grouped with those that were previousl y combined until all 40 cases were considered. Figure 6-8 : PILOT STUDY AGGLOMERATION SCHEDULE (STAGES 1-10) Cluslc! CC>Itli>l lh.v j . : r . ,;!_:. _:._ : 1 .: l 11.:_ . 1 1 S I 0 -., ... {I 7-lt ......... __ ., ,,.--;OO,O.O W '''" no !'-! \ ')( : l i ,:'il } (I ; I 5 J : --... 209 :\1'17-":''!r;' . . _I :1 1 1 ( I f.l i l i l 1 1 f .l . !:'. . .. .1 _ ' I_ I .... I "l _ ii _"::., ' ,!lo '1-;"

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Maximum correlations in the main study agglomeration schedules ranged from .719 to 1.00 (see Appendix D). For two study sites, Philips and Valdez, the first stage of clustering occurred with a coefficient of 1.00, indicating that two respondents at each site shared identical perspectives in their sorting exercises. In both cases, the answers to the questionnaires were not identical, but were similar enough to suggest that they may have been duplicates. Another indication of duplication error is the distance between the first and second stages of clustering. The second stage coefficients for the two sites were relatively low (.675 for Philips and .662 for Valdez). Staging typically occurs gradually, with a reasonably consistent decrease in the coefficients. With the exceptions of Philips and Valdez, Slavens had the highest coefficient for the first stage of clustering at .813, with an expected rate of decrease in later stages. The high coefficient at Slavens indicates strong similarity between clusters 9 and 16 without suggesting duplication. Core Perspectives The second step in cluster analysis was to interpret the number of core perspectives and member cases (respondents) that belong to each one. For this step, it was important to balance two objectives. Because the goal of the research is to tailor intervention to groups of parents who share perspectives about the commute, the first objective was to identify two or more distinct perspectives held by parents at each 210

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research site. I used the dendrograms to roughly identify the core perspectives for each site, interpreting the branching structure to find clusters of cases. Brown ( 1980) explains that the number of member cases included in each cluster is not significant, except to ensure that the perspective is shared. At one extreme, there might be only one perspective that includes all cases. However, that extreme would not recognize nuances of opinion and would discourage tailored intervention. At the opposite extreme, the number of clusters could equal half of the number of cases and stil1 allow each to be shared. However, that extreme could parse perspectives too finely making tailored intervention extremely resource intensive. Although this analysis is intended to identify relevant perspectives about the school commute, it is neither expected nor desirable to identify every possible permutation of priorities. The second objective is to ensure that the clusters describe cases (respondents) with reasonably similar viewpoints about active travel for the school commute. The dendrogram illustrates the degree of similarity between cases and how they combine to form clusters. The lower the number along the x-axis when cases combine, the stronger the core perspective of the cluster will be. In some instances, identifying a few clusters with larger numbers of member cases resulted in weaker clusters that combined at lower degrees of similarity (read as higher distances at combination). The alternative was to identify a large number of smaller clusters whose member 211

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cases combined at higher degrees of similarity. Each dendrogram typically offers several possible interpretations. Using the dendrogram from the pilot study cluster analysis, I identified five core perspectives with four to twelve member cases each. Each of the clusters combined at distances between 10 and 15 along the x-axis and are highlighted by dashed lines (see figure 6-9). As indicated in the diagram, three outlying cases (33, 32 and 22) combined at high distances, suggesting that they did not belong in any of the five clusters and that they did not form a sixth cluster. Using the dendrograms from the main study cluster analysis, I identified between two and five core perspectives per research site (see Appendix E: Main Study Dendrograms). As would be expected, several instances in which I identified fewer core perspectives with larger numbers of member cases resulted in weaker relationships because the clusters combined at higher distances (see table 6-8). For example, the two clusters that I initially identified for Philips had large numbers of member cases and very high distances at combination. The alternative interpretation had six clusters, a majority having between two and four member cases. Because the smaller number of clusters resulted in core perspectives with few similarities between cases, I opted to use the alternative grouping for my analysis. In other cases, I retained the larger clusters because the core perspectives were still strong. 212

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Figure 6-9: PILOT STUDY DENDROGRAM Cluster A I I I 1 1 I I I , I I r Cluster B 'j I .. I I ; I Cluster C ; I I. I I I I I I T Cluster D I i 1 I Cluster E Outliers I I u .. .... 1 213 i r [ l

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Table 6-8: Core perspectives by site and combination distance Site Cluster Member Cases Maximum Combination Distance P-A 8 15 P-B 7 13 Pilot P-C 4 14 P D 6 10 P-E 12 II ED-A 9 9 ED-B 8 12 Edison ED-C 7 14 ED-D 5 12 ED-E 8 15 SA-A 10 18 Sabin SA-B 10 15 SA-C 12 21 SL-A 14 17 Slavens SL-B 13 17 SL-C 9 21 CO-A 8 12 Cory CO-B 10 14 CO-C 7 17 BRA 7 II Bromwell BR-B 5 10 BR-C 4 10 BR-D 6 15 PH-A 18 20 Philips PH-B 15 24 PHA-A 4 9 PHA-B 13 14 Philips PHA-C 4 16 Alt. PHA-D 3 8 PHA-E 5 II PHA-F 3 13 VA-A 5 15 Valdez VA-B 5 16 VA-C 12 20 214

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Characterizing Core Perspectives -Ideal Types The third step in cluster analysis is to determine which statements best characterize the core perspectives, defining an ideal type for each. This process provides two types of information. First, it describes each cluster in terms of dominant attitudes. Second, it provides sufficient information to compare clusters and combinations of clusters within and between research sites, and to identify overarching patterns of perception and behavior. (see Appendices F and G) To define the ideal types, I calculated the mean scores for each Q set statement among member cases of each cluster (see figure 6-1 0). I selected statements whose absolute value means are equal to or greater than 2.0 (highlighted in bold print). Therefore, the selection of statements elicited strong responses of similar sign (+or-) from the entire cluster during the sort and represent the perspective of the group. In figure 6-10, the highest absolute mean scores occurred with statements SPV (2.50) and PLV (-3.88). I eliminated statements with absolute value means less than 2.0 from the ideal types either because they elicited weak responses from the entire group or because they elicited a combination of strong, but opposite-signed responses from individuals that canceled each other out in the mean score. For example, in the pilot study, statement SL V -Traffic congestion around the schools is a big problem -had neutral mean scores between 0.00 and 1.00 for each ofthe core perspectives although it received the full range of scores ( -4 to +4) from individual respondents. The issue 215

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captured by the statement may be important to some parents, but the low mean score suggests that it does not contribute to the perspectives that define the clusters of parents. Figure 6-10: IDEAL TYPE FOR PILOT CLUSTER A M A ost .gree M tD' OS Isagree Code Statement and Mean Scores Code Statement and Mean Scores It is important to protect the kids Schools should focus on students' from unsafe people. grades, not their health. SPY (2.50, 3.14, 2.50, 3.33, 1.67) PLY (-3.88, -1.14, -1.50, -0.67, -3.17) I can't trust other parents to be Kids who are physically active are responsible getting my kids to likely to do better in school. school. OBY (2.50, 0. 71' 0.50, 1.83, 2.50) TLI ( -2.38, 1.43, -0.50, -2.83, -1.92) I have never really considered Walking children to school would walking or biking my kids to be time well spent. school. TLY (2.25, 1.29, 2.00, 2.33, 1.58) OPY ( -2.38, -2.14, 1.00, -2.17, -2.50) Children should be supervised Our neighborhood has plenty of when they are outside. opportunities for outdoor recreation. MBI (2.25, 2.71, 2.00, -0.17, 0.25) OLI (-2.00, -I. 71' -0.50, 1.83, 0.67) To draw meaning from the ideal types, I did a comparative analysis that involved three steps. First, I interpreted each cluster's perspective based on the collection of statements that were included in its ideal type. In some cases, it was necessary to interpret the statements collectively rather than in isolation because the context provided insight into the group's perspective. For example, some clusters included in their profile statement NHS: We try to sleep in as late as possible. That statement could be interpreted as a concern about time management or about a health 216

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practice. If the profiles included other statements relating to time management, such as PTR or PTS, I would interpret NHS to also relate to time management. Mean scores for each statement provided additional insight into the emphasis that the group placed on individual issues as well as larger conceptual themes. For example, the absolute mean score for statement NHA in the profile for Cory's Cluster Cis 2.70. Although it ties with PSA for the highest mean score in that cluster, it is still relatively low, which suggests that the cluster is only weakly connected. In contrast, the absolute mean score for statement CTE in the profile for Edison's Cluster Dis 4.00. The very high absolute mean score means that the cluster agrees unanimously that the issue is a high priority. The negative sign means that they disagree with the statement itself To measure each cluster's opportunity and propensity to use active travel, I averaged the mean scores for statements that were included in their cluster profiles and that related to each concept. Of the 36 Q set statements, 17 described opportunity-related issues and 15 described propensity-related issues I discarded the remaining 4 statements because they did not clearly fit into one classification or the other. For several statements, I reversed the signs of the mean scores so that agreement consistently referred to an opportunity or propensity to walk or cycle. Similar to the mean scores, the resulting values had a possible range of -4.00 to +4.00, based on the sorting chart. The resulting averaged values fell between -2.70 217

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and +3.00 for opportunity-related statements and between -2.50 and +3.03 for propensity-related statements. Second, I identified similarities between clusters by correlating them with each other. Values had a possible range of 0.00 to 1.00, with higher values indicating greater similarity. A 1.00 value indicates that the clusters are identical, and only occurs for each cluster as it is correlated to itself. Very low values indicate that the clusters do not share issues of concern, or that their perceptions of shared issues are opposite. Very high scores indicate that several issues are shared between clusters, and that the clusters may be differentiated by nuances of attitude, or by one or more differences. Third, I compared the clusters' ideal types by identifying statements that appeared in only one cluster and which statements appeared in multiple clusters. Statements that appeared in multiple clusters indicated issues that are a high priority at the research site or across all of the research sites. However, it was also important to note which clusters did not include those statements, since the absence indicates a distinctly different perspective. In Chapter Seven, I discuss ideal types for the main Q sort in more detail. Identifying Representative Cases-Best Specimens The final step in cluster analysis was to determine which member cases in each cluster best represented the group. Member cases whose Q-sort distributions' z218

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scores are closest to the cluster's mean z-scores are highlighted as the Best Specimens ofthe cluster (see figure 6-11 and Appendix G: Main Best Specimen tables). I included cases whose scores were higher than 700 as Best Specimens and highlighted them in grey. The number and quality of Best Specimens varied depending on the degree of similarity between case members (see table 6-9). For example, I identified five Best Specimens in pilot Cluster A that had z-scores ranging from .728 to .874. In contrast, I identified 14 Best Specimens in Cluster E that had z-scores ranging from .712 to .827. A larger number of Best Specimens served as an additional indication of similarity between cases. Identifying best specimens makes it possible to conduct short follow-up interviews to clarify interpretations of each cluster's shared points of view, or to discuss intervention strategies before implementing them on a broader scale. However, I did not conduct follow up interviews in this project. Because my Q-sort activity included a short questionnaire about the respondents' lifestyles and demographics, the cases selected as best specimens shed light on the travel preferences and other characteristics of the clusters, and facilitated comparisons between research sites. 219

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Table 6-9: Pilot study best specimens Cluster A ClusterB ClusterC ClusterD ClusterE S1 13 .451 .376 694 .422 .373 S227 ' '';374 606 .694 .644 l eo;: ,. S065 .499 .357 .503 .653 1 .:<: .791 S106 .309 .217 351 1 ?IJ' .441 .:1. S219 .487 .593 <.< & $ 6 : ':. > ;734 Col S071 79$' .... .410 .245 264 .581 rJ) : SI36 .683 459 394 .339 .421 S148 .570 .311 .432 .594 ... . : .781 S299 633 564 .314 .677 S129 .499 .288 388 I>:. .\ $ Z 8 ; .538 L544 .380 240 .418 .549 L563 .592 .498 .422 636 L073 .646 .367 .424 .417 .467 L521 .372 029 .280 508 :.'.\ i.e . ;112 ..... L393 .582 .755 ... .554 .430 .520 0 L375 .442 .404 .816 .484 .568 ....J ,.:.,:::-:, L419 ;128'' .520 .405 .483 616 L046 .483 .352 .389 .579 .. ; <1't9 L341 605 .590 .494 .542 >,;. / 1.6. 6 Ll63 .545 .565 .567 .476 't'> ':733 M495 .748' .420 .365 643 .496 M372 084 .063 165 089 037 M280 294 .791 .349 .264 .392 MOI3 .390 : . :.150 364 .417 .364 0 Ml90 .543 .563 .530 .507 .; ... = "' L 'J'62. = M277 .433 292 .347 .42 I :; M152 545 .475 .509 .440 <144' ' M279 573 687 394 .267 .447 M418 .412 .441 J .: .:.;{as, 271 387 l i r .: > M186 635 .665 .474 .201 .543 F378 .569 .344 .141 .115 252 F578 .332 376 .112 .319 .392 F580 .214 292 .252 183 .132 F397 .579 595 .459 .. \;Y.:i%;1f3 ; F516 .467 rr, ;'7;76 .320 .240 .373 Col ... 0 F385 .560 396 .168 .217 .310 'F236 .493 .. ... ;141. .324 .358 .352 F155 .509 .: '.761 .373 .338 .598 F099 ; /. .. '<:$04:. .543 .671 505 670 F422 .460 .496 .391 .321 .; : .1o. s 220

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Summary In this chapter, I presented Q-methodology and the quantitative research methods that I used in the second phase of my dissertation research. The purpose of this quantitative study was twofold. First, it was to examine a larger sample of parents' attitudes about the school commute and to develop a typology that can directly guide active travel intervention. Second, it was to investigate the use of the Q-sort technique in a variety of research contexts and to determine how differences in study populations influence research processes and findings. In this second sense, this research serves as a methodological test to determine whether Q-technique can be effectively used to achieve meaningful, preliminary assessments of parents' attitudes at schools intending to increase rates of active travel through programmed intervention. Using the Q-sort technique, I examined attitudes of parents from eleven Denver public elementary schools that participated in three Safe Routes to School non-infrastructure programs during the 2007-2008 school year. The selection of schools ranges broadly in size and physical context as well as socio-economic status and demographic characteristics of student enrollment. I prepared packets to send out to all parents at the schools that included the sorting exercise materials and a short questionnaire about the respondents' households. Based on results from the pilot study, I revised the packaging, design and substantive content of the research materials to improve response rates for the main 221

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study. Response rates did indeed improve considerably from the pilot to the main study, with the average response rate increasing from 5.3% to 38.8%. The higher response rates allowed me to purposefully select between 20 and 40 respondents for cluster analysis. To create the sorting activity, I selected 36 statements from among 670 comments that parents made regarding the school commute during interviews in my first phase of research. I sampled the statements to reflect each of 36 initial themes that emerged during content analysis of interview transcripts (see Chapters Four and Five). The resulting Q set is considered naturalistic because it reflects the language of respondents and is drawn from interviews. The design of the Q set may be considered unstructured because it reflects the perspectives of respondents; however, the set is also structured to examine an externally defined theory-one that predicts parents' travel mode choices based on their opportunity and propensity to walk children to school. Respondents rank-ordered the statements to fit a quasi-normal distribution with "most agree" to one side and "most disagree" to the other. To analyze the sorting exercises, I used SPSS software and ran a hierarchical clustering algorithm that compared respondents' complete perspectives and sorted them into attitude-based, thematic clusters. I describe those clusters in greater detail in Chapter Seven. Data analysis involved four key steps. First, I used cluster analysis and Pearson's coefficient to determine the similarity between respondent perspectives. Second, I 222

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used the resulting agglomeration schedules and dendrograms to identify core perspectives, or clusters of cases. Third I used the mean scores (respondent rankings) for each statement to substantively characterize each core perspective. I also used the mean scores to graph each case in terms of opportunity and propensity. Finally I correlated cases with core perspectives to determine which respondents best represent each core perspective. The attitude-based typologies that emerged through my analysis provide the framework for the presentation of findings in Chapter Seven. 223

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CHAPTER 7: QUANTITATIVE FINDINGS Introduction In support of active travel intervention, planning researchers examine why parents choose to drive rather than walking their children to school. In general, the research identifies factors associated with travel choices and then estimates population parameters, such as the proportion of parents who agree that dangerous traffic conditions discourage them from walking their kids to school, or the proportion of parents who agree that they do not have time to walk back and forth to school. As I explained in Chapter Three, planning research often reduces findings to the most significant factors influencing travel mode, which implies attitudinal homogeneity by disregarding smaller groups of subjects who do not share the dominant experience, and by disregarding secondary factors that also influence choices. Inasmuch as the factors identified as most significant also tend to be environmental obstacles, extant research implies that parents would generally prefer walking to school if they were not prevented by conditions outside of their control. Socio-demographic studies similarly emphasize environmental factors, but in doing so they demonstrate that certain types of parents respond differently to comparable 224

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external conditions. While that research aims to explain why behaviors differ, it does little to investigate how they differ. The purpose of my research was to examine parents' attitudes about active travel so that planners can tailor intervention to specific target groups and, thereby, encourage a larger proportion of students to walk to school. Q-methodology is well suited to that objective because it magnifies nuances in subjects' predispositions rather than diminishing them through generalization. My research design included two phases: an exploratory qualitative study followed by a quantitative study using a sorting exercise with cluster analysis. In Chapter Four, I outlined the qualitative methods that I used in the first phase of my research, which identified the range of issues that Denver parents associate with mode choices for their trips to elementary school. In Chapter Five, I presented the findings from that qualitative study, organized around the three major themes that emerged from the data. In addition to identifying several key issues that Denver parents associate with their school travel choices, findings from the qualitative study provided a concourse of ideas from which I selected statements for the Q-sort exercise that I conducted in the second phase of research. In Chapter Six, I outlined the quantitative methods that I used in the second phase of research. I used Q-technique to learn relevant predispositions to active travel, and to identify and distinguish types of people based on their attitudes. The 225

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analysis that I described in Chapter Six resulted in 31 core perspectives about active school travel with 3-6 perspectives at each school. This chapter presents those core perspectives, first graphed to illustrate varying degrees of opportunity and propensity expressed by the clusters of parents. The four quadrants of the graph illustrate an attitude-based typology regarding school travel and provide a potential policy framework for active travel intervention. I discuss each quadrant individually and compare the substantive composition of several clusters that fall within each of their bounds. My findings suggest that while opportunity and propensity positively correlate, they do not predict travel behavior characteristics. To direct active travel intervention, it is necessary to examine and compare attitude profiles of individual clusters. Following characterization of each quadrant of the opportunity-propensity graph, I correlate clusters at each school to show varying degrees of similarity between perspectives, and compare them across the seven schools to examine overarching trends. These results indicate an opportunity to more effectively direct behavioral intervention by purposefully recognizing social diversity. I discuss implications of my findings in Chapter Eight. Characteristics of Cluster Analysis Cases As I described in Chapter Six, 650 parents from 7 Denver Elementary Schools responded to my main survey-style Q-sort activity. From among them, I purposefully 226

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selected 220 cases (22-39 from each school) representing salient demographic characteristics for inclusion in the cluster analysis (see Chapter Six). Although I purposefully selected cases that were most likely to embrace active travel, based on their proximity to the school and other characteristics (see Chapter Six for details), 46% of the 220 parents reported that they drive both directions and 27% reported that they drive one direction. However, 55% of those parents reported that they walk in nice weather. These statistics indicate that there are shades of travel behavior beyond the walk/drive binary often depicted in research. and that intervention can leverage positive current travel behaviors in order to increase future numbers of walking trips. Range of Perspectives Similar to results from other types of quantitative research, the content of core perspectives obtained through cluster analysis of Q-sorting is limited to those issues that are included in the research tool. For example, if a survey or Q sort asks respondents to provide their perspectives on issues A through F, the results cannot introduce issue Gas a possibility. For that reason, in Chapters Three and Four, I argued that qualitative methods are more appropriate than quantitative methods for identifying the range of factors associated with travel mode choices, in part because they can use an open-ended approach to probe for new dimensions ofthe problem. If the underlying objective of the research is to identify and eliminate obstacles that 227

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prevent or discourage active travel, it is imperative that the full range of factors/obstacles be included. Traditional quantitative research is appropriate for estimating population parameters, such as the proportion of parents who live a certain distance from the school and who typically drive their children back and forth (McDonald 2007) or the proportion of households that earn a certain level of income and who typically walk their children (Boamet and Sarmiento 1998). Findings from those studies are often mistakenly treated as causal relationships instead of simple correlations (Handy 1996). Predictive models often include those findings as factors influencing behavior, despite the proportion of respondents who behaved differently. That type of generalization is detrimental to policy that aims to influence behavior. Q-methodology differs from traditional quantitative research, in part, because it identifies predispositions upon which behaviors are predicated, rather than estimating parameters or seeking causal explanations. The selection of statements included in the Q set limits the substantive content of core perspectives. However, it is the subset of statements that cluster members include in each perspective rather than the percentage of parents agreeing with individual statements that provides insight into attitudes about active school travel. Analysis of this Q-sort resulted in 31 discrete clusters of parents characterized by their shared perspectives of active school travel. In this section, I present a typology ofthose perspectives based on the opportunity-propensity dichotomy of 228

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Chapin's (1974) General Model of Human Activity. As I explained in Chapter Six, I defined x and y coordinates for each cluster by averaging the mean scores of statements included in its profile that indicated opportunity and propensity respectively (see figure 7-1). Figure 7-1: ATTITUDE TYPOLOGY FOR SCHOOL TRAVEL .vv VA-A J.VV S; __.,..,.,--'VV l:SK-1'\ ED .L.vv .. v.vv 00 -2 00 -1 .00 0 00 1 00 2 00 3 00 4 DO ....._ c P-B -J..VV PH---'.VV ED-I -J.UU Opportunity Averaged Mean Scores for Related Statements 229

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Opportunity and Propensity (Top Right Quadrant) As indicated by Figure 7-1, the majority of attitude-based clusters identified in this study (18 of31) have positive mean scores for both the opportunity and the propensity to use active travel modes for school trips, albeit varying degrees of each. Found in the top right quadrant, Cluster BR-B has the highest overall level of propensity with a mean score of 3.03. Note that three clusters in this quadrant, SL-C, ED-A and SL-B, exceed Cluster BR-B's level of opportunity, and that Cluster VA-A in the top left quadrant has the second highest mean score for propensity despite its relatively low opportunity score. Although their behaviors are comparable, the opportunity score for Cluster SA-B is significantly lower than the score for Cluster SL-B (0 .72 versus 2.39). About half of the parents in Clusters SA-Band SL-B report driving both ways to school, but 6 of 10 parents in Cluster SA-B and 11 of 13 parents in Cluster SL-B also walk on nice days. These findings indicate that opportunity and propensity do not entirely determine travel behavior characteristics. Even parents whose clusters appear in the top right quadrant of the diagram do not consistently walk children to and from school (see table 7-1). Proportions of parents in those clusters who reported walking on nice days range from 0 to 1 00 percent. At the low extreme, 0 of2 parents in Cluster ED-D reported walking on nice days. However, they also did not report driving one or both ways. Instead, they 230

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marked that they drop their children off on the way to work, but did not specify travel mode. Table 7-1: Travel characteristics of clusters in top right quadrant "0 Q...!>l! Q) Q) "' c c "' 0 ... > > "' "' Q) "0 til ... 0 "' til c ;::: >. ... Q) -0 -0 Q) Q) Q) Q) :s C/) c C).() Q) :s 0 0 Q) "' c (,) til (,) 1;),()(,) "' 0 .,CI) til Q) c (/) CCI) r: 0 c ;::: :E "' Q) "' u Q) c ue-u -Q) ... u Q) :s 0 c Q) ... tl. -u ....... > ....... ....... ..!>
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present themselves as the strongest walkers, details of their responses indicate that their travel mode choices are more complex than the simple walk/drive binary that often appears in the literature. Due to variations in travel behavior within the quadrant, it is necessary to examine characteristics of the clusters to determine how intervention can increase proportions of walking trips. For example, for Cluster ED-A, the averaged mean scores for statements relating to parents' opportunity to walk (opportunity score) equals +2.56 and the averaged mean scores for statements relating to parents' propensity to walk (propensity score) equals + 1.81. Therefore, that cluster appears in the top-right quadrant, and may be broadly characterized as parents who are moderately inclined to use active travel modes, and for whom contextual conditions strongly favor that choice. Specifically, the profile of Cluster ED-A expresses preference for an active lifestyle (NHA, NHE and NHS), motivations for walking to school (NOA and PCI), contextual conditions that support active travel (CEW, CEI and CDP), trust in children and neighbors to act safely (NIS and PSA), and a positive image oftheir children's physical fitness (NHD) (see figure 7-2). 232

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Figure 7-2: CLUSTER ED-A IDEAL TYPES Most Agree Most Disagree Statement and Mean Scores Statement and Mean Scores (A, B, C, D, E, F) (A, B, C, D, E, F) < We want physical activity to be a part of :I: our children's lives. z (3.33, 3.12, 2.57, 2.00, 1.40, 2 75) < I can't trust other adults to get my kids to r/) school. 0.. (-2.89, -2.50, -1.43, -1.50, -2.40, -0.62) U.l Physical fitness is very important to our :I: family. z (3.33, 3.00, 2.86, 1.00, -0.20 1.50) There really aren't many safe routes to our Ui school. u (-2.44, -1.50, -2.14, 0.00, 0 60, -2.62) Children can be safe on the street ifthey r/) learn the right skills z (2.78, 0 75, 0.57, 1.00, 0.80, 0.25) 0 We worry about our kids becoming obese. :I: z (-2.22, -1.25, 0.29, 1.50 -2.40, -2.12) Colorado weather is ideal for walking and U.l biking to school. u (2.78, 2.12, 2.00, 0 50, 1.80, 0 75) r/) We try to sleep in as late as possible :I: z (-2.22, -0.38, -0.71, -3. 50 0.80, -2 62) < Kids who are physically active do better 0 in school. z (2.22 3.50, 2.71 1.00, 0.20, 1.38) 0.. There are strangers out there waiting to take 0 your kids. u ( -2.22, -1.00, -0 86, 0.50 2 20, -0 50) Walking to school is more enjoyable than 0 driving. 0.. (2.00, 1.75, 1.29, 0 00, 1.20, 2.25) For Cluster BR-A, the opportunity mean score equals+ 1.57 and the propensity mean score equals+ 1.95. For Cluster SA-B, the opportunity mean score equals +0.72 and the propensity mean score equals +2.75. As a result, both clusters also appear in the top right quadrant, and might be broadly characterized in much the same way as Cluster ED-A. However, each of the cluster profiles emphasizes a unique combination of issues that can help to guide intervention. For example, whereas Cluster BR-A emphasizes motivations for walking to school (PCI, PCB, NIF, NOA), Cluster SA-B emphasizes safety (PSG, CTE, PSC, CEI, CTR) (See figures 7-3 and 7-4, also see Appendix G). 233

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Figure 7-3: CLUSTER BR-A IDEAL TYPES Most Agree Most Disagree Statement and Mean Scores Statement and Mean Scores (A, B, C, D, E) (A, B, C, D, E) Walking to school is more enjoyable 0 than driving c. (3.43, 2.60, 1.33, 2. 75, 1.83) There are no busy roads in our Eneighborhood u (-3.00, -1.40, -0.67, -3.50, -2 50) 0 The location ofthe school influences 0 where we live. z (2.86, 1.40, 0.67, 3.25, 0.00) There aren't many kids in our neighborhood r:/'J c. (-2.86, -1.60, -1.33, -0.75, -2.67) Parents bond with their kids on the trip u to school. < I can't trust other adults to get my kids to r:/'J school. c. (2.86, 1.60, 0.67, 1.00, 1.00) c. (-2.71, -2.40, -1.33, -0.50 -3.17) Colorado weather is ideal for walking I.Ll and biking to school. u (2.86, 1.40, 1.33 -1.75, 1.83) There really aren't many safe routes to our iii school. u ( -2.71, -1.00, -2.67, 0.50 1.67) It's fun for kids to go to school with !::: their friends. z (2.71, 3.00, -0.67, 0.25, -0.83) < By third grade, kids can get to school okay on their own. z (-2.14, -0.40, -0.67, -3.50, -2.17) < We want physical activity to be a part of ::c our children's lives. Backpacks are too heavy for kids to carry to 0 school. z (2.43, 3.40, 2 33, 2.00, 2.67) z (-2.00, -1.80, -0.67, -1.00, -2.00) < Kids who are physically active do better 0 in school. z (2.43, 3 00, 3.33, 2 00, 1.17) 0 Children would be safe if they traveled r:/'J in groups. c. (2.00, 1.20, -0.33, -0.25, 0.67) 234

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Figure 7-4: CLUSTER SA-B IDEAL TYPES Most Agree Most Disagree Statement and Mean Scores Statement and Mean Scores (A, 8, C, D) (A, 8, C, D) c.:> Children would be safe if they traveled in <:rJ groups. 0.. (0.40, 2.70, 1.33, 0.00) <:rJ We try to sleep in as late as possible. :I: z (-3.10, -3.40, -2.58, -3.00) IJ.l Too many drivers disobey traffic rules !and signals. u (2.70, 2.30, 0.50, 1.60) There really aren't many safe routes to our -school. w u (0.10, -3.10, -1.33, -2.80) If children yelled for help in our area, u someone would protect them. <:rJ 0.. (-0.40, 2.10, -0.42, 2.80) cG There are no busy roads in our !neighborhood. u ( -2.60, -2.00, -1.67' -2.00) < We want physical activity to be a part of ::r: our children's lives. z (1.70, 2.10, 3.08, 1.80) Propensity Only (Top Left Quadrant) The second largest group of attitude-based clusters identified in this study (9 of 31) expressed some degree of propensity to walk children to school, but perceive a lack of opportunity, despite varying distances between home and school. As mentioned above, Cluster VA-A, found in the top left quadrant, has the second highest overall level of propensity with a mean score of 2.80. However, it behaves as an outlier because it has a relatively low opportunity score, even compared to other clusters in the same quadrant. Linear regression of the 31 clusters results in a positive slope of0.39r meaning that propensity generally increases with opportunity, although the r2 of 0.23 indicates a weak correlation. Although the opportunity scores for the top left quadrant are negative, meaning that parents perceive barriers to active travel, that perception does not entirely prevent active travel. For example, Cluster BR-E had an opportunity mean 235

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score of -0.38, but only 1 of6 parents reported walking on nice days. In contrast, 10 of 14 parents in Cluster SL-A and 4 of 5 parents in VA-B reported walking on nice days, despite similar opportunity scores of -0.11 and -0.30 respectively. Moderately low and very low opportunity mean scores similarly failed to predict travel behavior. For example, whereas 2 of 13 parents in Cluster PH-B reported walking in nice weather, 3 of 4 parents in Cluster PH-C reported walking. Similarly, 0 of 4 parents in Cluster BR-D, and 2 of 10 parents in Cluster SA-A reported walking on nice days. However, with comparably low opportunity mean scores, 2 of 5 parents in Cluster VA-A and 4 of 12 parents in Cluster VA-C reported walking (see table 7-2). These findings indicate that evaluating and subsequently addressing levels of opportunity for active travel is insufficient for predicting and influencing travel behavior. Table 7-2: Travel characteristics of clusters in top left quadrant "0 c....><: C1) C1) "' = = "' 0 .... > > .... 0 't: til ;:::: >-. "' "' C1) "0 "' "' "' C1) -0 C1) C1) Cl)-C1) ::s CIJ = Oil C1) ::s Cl Cl "" ... -0 ,CJJ C1) "' = (,) "' (,) 01)(..) "' 0 ;. .... "" fCIJ I:CIJ i:: "" 0 "" = "' C1) "' u ueu -C1) .... u C1) ..2 0 = C1) = C1) .... .... o.o:l > .... "" .... ...><: .... ...><: u 0 c.."' 0 C1)
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To guide intervention, it is necessary to examine attitudes and how they relate to the opportunity and propensity to walk. For example, for Cluster SA-A, the opportunity score equals -2.70 and the propensity score equals +0.30 Therefore, that cluster appears in the top-left quadrant, and may be broadly characterized as parents who are only modestly inclined to use active travel modes, and for whom contextual conditions strongly discourage that choice. Specifically, the profile of Cluster SA-A expresses concern about heavy traffic and poor driving (CTR, CTD and CTE), stranger danger (CDP) and children's ability to negotiate the journey alone (NIA) (see figure 7-5). Figure 7-5: CLUSTER SA-A IDEAL TYPES Most Agree Most Disagree Statement and Mean Scores Statement and Mean Scores (A, B, C D) (A, B, C, D) 0 Drivers are too distracted by their phones fand kids u (3.10, 1.30, 1.17, 0.80) r:/) We try to sleep in as late as possible. ::t ;z (-3.10, -3.40, -2.58, -3.00) U.l Too many drivers disobey traffic rules fand signals. u (2. 70, 2.30, 0.50, 1.60) c::: There are no busy roads in our fneighborhood u ( -2.60, -2.00, -1.67' -2.00) 0.. There are strangers out there waiting to 0 take your kids. u (2.40, -0.1 0, 0.58, 0.40) By third grade, kids can get to school okay
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social conditions (CTR, CTD, CTE and CTP) Cluster BR-D balances its concern for contextual hazards (CTR, CEP, CTC, CTE and CDS) with conditions and motivations supporting active travel (PTW, NOD, PCI NHE NHA and NOA) (See figure 7-6). Figure 7-6: CLUSTER BR-D IDEAL TYPES Most Agree Most Disagree Statement and Mean Scores Statement and Mean Scores (A, B C D, E) (A, B, C D, E) 0 The location of the school influences 0 where we live. z 2.86, 1 40, 0.67, 3.25 0 00) By third grade kids can get to school okay < on their own. z 2.14 -0.40 -0.67, -3.50, -2. 17) Walking to school is more enjoyable u than driving Q. (3.43, 2.60, 1.33, 2.75, 1.83) eX There are no busy roads in our fneighborhood u ( -3. 00, -1.40 -0.67' -3.50, -2 50) Ll.l Physical fitness is very important to :t our family. z 1.57, 3.80, 1.00, 2.75, 2.17) Working parents can't take time to walk to fschool. Q. (-1.71 -1.00, 1.00, -2.00, 1.50) u There are dangerous crossings in our fneighborhood u 0.14, 1.40, -0.67, 2 25, 3.67) 0 We worry about our kids becoming obese. :t z (-1.29, -1.00, -1.33, -2.00, -2.17) < We want physical activity to be a part :t of our children's lives. z (2.43, 3.40 2 .33, 2.00, 2.67) Q. Air pollution is NOT a problem at our kids' Ll.l school. u -0.29, -0.40, -0. 67, -2.00, -1.67) < Kids who are physically active do 0 better in school. z (2.43, 3 00, 3 .33, 2.00, 1.17) Ll.l Too many drivers disobey traffic rules fand signals u (0 29 0 20, -0.33 2.00, 1.83) en People no longer know their neighbors 0 like they once did. u (-0 29, -2.20, 0 67 2.00, -0 33) 238

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Opportunity Only (Bottom Right Quadrant) Only one attitude-based cluster identified in this study lacks the propensity to walk despite having a positive opportunity score. For Cluster CO-B, the averaged mean scores for statements relating to parents' opportunity to walk equals +0.78 and the averaged mean scores for statements relating to parents' propensity to walk equals -0.72 (see table 7-3). Note that nearly half of the 31 clusters have lower opportunity mean scores, but significantly higher propensity mean scores. Although this cluster reads as an outlier, it is useful to examine its profile to determine which propensityrelated issues intervention might address to encourage active travel. Table 7-3: Travel characteristics of single cluster in bottom right quadrant ... E "' ::s u CO-B "' C) u ..... 0 'It 3 0.78 -0 .72 2.3 "' C)-"' 0 0 ue-..... ou 'It 0/3 c "' ::s en c u ..... o1/3 0/3 1/3 0/3 2/3 Despite low propensity scores, I of 3 parents in Cluster CO-:B reported walking on nice days. That characteristic of the cluster suggests that tailored intervention can encourage additional walking trips. Cluster CO-B's mixed travel 0/3 behavior is consistent with the ideal type profile, which expresses several motivations for using active travel modes (NHE, CEW and NHA), but counters them with modest 239

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concern about safety (CTR and NIA) and strong views regarding nurture (NHD, CDK, NOS, NHS) that justify automobile travel (see figure 7-7). Figure 7-7: CLUSTER CO-B IDEAL TYPES Most Agree Most Disagree Statement and Mean Scores Statement and Mean Scores (A, B, C, D) (A, B, C, D) tJ.l Physical fitness is very important to our = family. z (2.25, 3.33, 2.50, 3.29) 0 = We worry about our kids becoming obese. z (-1.12, -3.33, -1.40, -1.14) Colorado weather is ideal for walking tJ.l and biking to school. u (0.25, 2.33, 2.50, 2.71) r::G There are no busy roads in our Eneighborhood. u (-3 25, -3.33, -2.1 0, -2.14) < We want physical activity to be a part of = our children's lives. z (3.62, 2.00, 2.70, 2 86) :::.G Kids need protection from other kids and 0 youth. u (-0.25, -3.33, -1.50, 0.14) r:/J We try to sleep in as late as possible. = z ( -2.38, 2.00, -1.00, -0.57) r:/J Kids will walk to school if there are special 0 events. z (-0.62, -2.33, 0.00, 0.29) < By third grade, kids can get to school okay on their own. z (-2.13, -2.00, 0.30, -2.71) 0 The location of the school influences where 0 we live z (1.00, -2.00, 0.90, 0.14) Neither Opportunity Nor Propensity (Bottom Left Quadrant) Three attitude-based clusters identified in this study fall into the lower left quadrant, meaning that they lack both the perceived opportunity and the propensity to walk their children to school. However, within the quadrant, Cluster PH-A has a significantly higher opportunity score than Clusters ED-E and PH-F (see table 7-4). The higher score, in this case, can be attributed to statements that suggest opportunities for safe travel (PSG, PSU and CEI), in addition to concern for safety (CDP, NIT, CTE, CTR and NIA). Again, it is necessary to examine characteristics of 240

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the clusters' ideal type profiles to determine which issues resonate most strongly with parents, and to tailor intervention appropriately to encourage active travel. Table 7-4: Travel characteristics of clusters in bottom left quadrant ED-E PH-A PH-F c 0 c.. c.. 0 -2.48 -0.13 -2.44 -2.50 2.2 -0.92 2.3 -1.34 2.7 20 40 60 2.4 0 0 25 1.3 0 33 33 1.3 80 50 0 1.6 2.3 2.0 For Cluster PH-F, the opportunity score equals -2.44 and the propensity score equals -1.34. Therefore, that cluster appears in the lower-left quadrant, and may be broadly characterized as the opposite of Cluster ED-A, which appears in the top right quadrant. Although PH-F similarly expresses a preference for physical activity by including statements NHA and NHE in its profile, that preference is challenged by cluster members' collective perceptions of safety (CTD, CDK and CTR), and time constraints (PTW, PTM and PCE) (see figure 7-8). 241 3.6 1.8 2.5

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Figure 7-8: CLUSTER PH-F IDEAL TYPES Most Agree Most Disagree Statement and Mean Scores Statement and Mean Scores (A, B, C, D, E, F) (A, B, C, D, E, F) 0 Drivers are too distracted with phones, fkids and other things. u (0.00, 1.69, 3.00, -1.67, -0.80, 3.00) U.l Driving to school is very convenient. u 0.. (-3.00, 1.00, 1.25, -1.00, -0.40, 3.00) Working parents can't take time to walk to fschool. 0.. (-1.75, -0.46, 1.25, 2.67, 0.20, 2.67) 0 We worry about our kids becoming = obese z (-3.50, -0.77, -0.50, -3.00, 2.20, -2.67) Parents save time by combining school ftrips with errands. 0.. (-1.50, -0.23, -1.25, 0.67, 0.20, 2.33) 0.. Air pollution is NOT a problem at our U.l kids' school. u (-0.25, -1.38, 0.50, 2.00, -2.20, -2.33) < We want physical activity to be a part of = our children's lives. z (0.50, 2.54, 1.25, 3 00, 1.60, 2.33) There are no busy roads in our fneighborhood. u (-2.75, -3.00, -0.75, -1.67, -2.60, -2.67) ::.G Kids need protection from other kids and 0 youth. u _(1.50, 1.15, 0.75, 1.33, -0.20, 2.33) Physical fitness is very important to our = family. z (0 50, I .54, 2.25, 0 33, 2.60, 2.00) Similarities Between Core Perspectives Core perspectives at each school often shared one or more characteristics. Using Pearson's correlation coefficients, I measured similarity between perspectives (hereafter clusters) at each school. Correlation coefficients can range from 0.00 to 1.00 and are proportional to the degree of similarity between clusters. Since each cluster is identical to itself, its correlation equals 1.00. Cluster correlation coefficients for this study ranged from a low of0.03 for Clusters ED-D and ED-F to a high of 0.85 for Clusters SL-A and SL-B (see figure 7-9). The distribution of correlations for all seven sites was slightly skewed to the left, but otherwise normal with a mean of 242

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0.44 and a standard deviation of 0.22. That means that overall, the clusters tended to be distinct but shared some attributes. Figure 7-9: MAIN STUDY CLUSTER CORRELATIONS USING PEARSON'S COEFFICIENT Cluster ED-A ED-B ED-C ED-D ED-E ED-F ED-A 1.00 0.78 0.66 0.26 0.20 0 .64 ED-B 1.00 0 .72 0.12 0.50 0.69 ED-C 1.00 0.14 0.18 0.47 ED-D 1.00 0 .15 0 .03 ED-E 1.00 0 47 Cluster SA-A SA-B SA-C SA-D SA-A 1.00 0.56 0.49 0 44 SA-B 1.00 0.68 0 64 SA-C 1.00 0.59 Cluster SL-A SL-B SL-C SL-A 1.00 0.85 0.74 SL-B 1.00 0.60 CO-A CO-B CO-C CO-D CO-A 1.00 0.42 0.67 0.76 CO-B 1.00 0.39 0.49 CO-C 1.00 0.82 Cluster BR-A BR-B BR-C BR-D BR-E BR-A 1.00 0.73 0.54 0.58 0.56 BR-B 1.00 0.53 0.48 0.68 BR-C 1.00 0.39 0 .34 BR-D 1.00 0.56 Cluster PH-A PH-B PH-C PH-D PH-E PH-F PH-A 1.00 0.58 0.14 0 .25 0.22 0.18 PH-B 1.00 0.29 0.23 0.54 0.49 PH-C 1.00 0.15 0 22 0.27 PH-D 1.00 0.19 0 .23 PH-E 1.00 0.23 Cluster VA-A VA-B VA-C VA-A 1.00 0 .15 0.22 VA-B 1.00 0.56 VA-C 1.00 243

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Low Correlation Distributions of coefficients at each site varied considerably. The distribution of coefficients at Edison was bimodal with a low-average mean of 0.40 and several very low scores. Coefficients for clusters at Philips and Valdez were very low with means of 0.28 and 0.31 respectively. The very low scores at those schools indicate that several core perspectives were not only distinct, but in opposition on certain issues. For example, at Edison, Clusters ED-D and ED-F had a correlation coefficient of0.03, the lowest overall score. Although those clusters shared viewpoints on two statements regarding physical activity (statements NHA and NHS), they had opposing mean scores for statements regarding the behavior of drivers (statement CTE) and the convenience of driving to school (statement PCE). Mean scores for numerous other statements were opposite in sign, but not valued as highly for one or the other of the clusters (statements PTS, and PTM, NIA, NHD, CEG, CEI and CDK). At Philips, Clusters PH-A and PH-C had the lowest correlation coefficient (0.14). Although those clusters shared a viewpoint regarding children's ability to negotiate the trip to school by themselves by third grade (statement NIA), they had opposing mean scores for statements regarding neighborhood demographics (statement PSU). Mean scores for several other statements were opposite in sign, but not valued as highly for one or the other of the clusters (statements PCE, PCI, and CDS). 244

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At Valdez, Clusters VA-A and VA-B had the lowest correlation coefficient (0.15). The two clusters share a viewpoint about families being too rushed to walk children to school (statement PTR), but otherwise diverge in their opinions. Mean scores for numerous statements were opposite in sign, but not valued as highly for one or the other ofthe clusters (statements PTS, PSA, NIA, CTD, CTE, CDP, CDS and CDK). These low correlation coefficients, and contrasting core perspectives indicate that parents respond differently to similar environmental conditions -a truth already told in planning studies that focus on demographic characteristics as I mentioned in Chapter Three. However, the attributes of the core perspectives describe how they differ, and do so in terms of relevant predispositions to active school travel. Moderate Correlation Coefficients for Cory, Sabin and Bromwell were moderate with means of 0.59, 0 57 and 0.54 The average scores at those schools indicate that while the core perspectives share viewpoints on several issues, they are still quite distinct overall. For example, at Sabin, Clusters SA-A and SA-C had a moderate correlation coefficient of 0.49. These clusters shared a viewpoint regarding the need to sleep in (statement NHS), and otherwise diverged in their opinions. These clusters similarly differentiated themselves with numerous strongly held viewpoints that the other 245

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cluster did not share (statements PCI, NIA, NHA, NHE, NOA, CTD, CTR, CTE and CTE). In contrast, at Cory, Clusters CO-B and CO-D had the same, moderate correlation coefficient of0.49. These clusters shared viewpoints on four issues, including the importance of physical fitness (statements NHA and NHE), the opportunity presented by Colorado's weather (statement CEW), the proximity of major roadways (statement CTR), and children's ability to negotiate the trip by themselves by third grade (statement NIA). Despite these several commonalities, the clusters still differentiated themselves with numerous strongly held viewpoints (statements PCI, PCB, PSU, PSA, NHD, NHS, NOS, and CDK). Similarly, at Bromwell, Clusters BR-B and BR-D had a moderate correlation coefficient of 0.48. These clusters shared viewpoints on three issues, including the relative enjoyment of walking versus driving (statement PCI), the importance of physical activity and fitness (statements NHA and NHE) and the academic benefit of physical activity (statement NOA). Otherwise, they diverged in their opinions, differentiating themselves with numerous strongly held (statements PTW, PCP, PSA, NIF, NIA, NHD, NHS, NOD, CEP, CTC, CTR, CTE, and CDP). These moderate correlation coefficients and corresponding core perspectives indicate that even parents who share certain values or concerns may be differentiated in their attitudes about active school travel based on specific facets of the issue. 246

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Therefore, it would be appropriate to design a multifaceted intervention that addresses the basis for their attitude differences. High Correlation Slavens had the highest coefficients, with a mean of0.73. High scores indicate that while the core perspectives may be distinguished by subtle differences of opinion, they share viewpoints on numerous issues. For example, at Slavens, Clusters SL-A and SL-B had a high correlation coefficient of 0.85. These clusters shared viewpoints on four issues, including the relative enjoyment of walking versus driving (statement PCI), neighborhood demographics (statement PSU), being able to trust other parents as chaperones (statement PSA), and the importance of physical activity and fitness (statements NHA and NHE). Despite these several commonalities, the clusters differentiated themselves with several strongly held viewpoints (statements NIF, NHD, NOA, CEI, CEW, CTR, and CDK). These high correlation coefficients and corresponding, nuanced core perspectives indicate that families at this school are largely homogeneous in their attitudes about active school travel. However, there are sufficient differences in the ways that they explain their travel preferences, and in their travel behaviors, that a multifaceted intervention could still increase rates of active travel. 247

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Overall Similarities Many of the core perspectives identified through this study shared one or more characteristics with clusters at other schools. Although it was not possible to correlate clusters between schools, I was able to compare core perspectives based on the statements that appear in their profiles (see Appendix G). Comparison of cluster profiles revealed commonalities in parents' preferences for physical activity, motivations for travel mode choices, and concerns about contextual conditions and companionship. In this section, I briefly discuss the implications of those commonalities. Preference for Physical Activity The most striking similarity across schools was parents' preference for physical activity. All but four of the clusters expressed a preference for physical activity by strongly agreeing with statement NHA: We want physical activity to be a part of our children's lives and/or statement NHE: Physical fitness is very important to our family. The highest scores were 3.40 for statement NHA and 3.80 for statement NHE, both found in Bromwell's Cluster BR-B profile. The remaining four clusters had low enough absolute mean scores for those statements that they did not appear in their cluster profiles. That result indicates that parents in those clusters perceived other issues included in the Q set to be higher priority than physical activity. 248

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Motivations for Travel Mode Choices Another similarity across schools was parents' perceptions of benefits associated with their preferred travel mode. All but one of the 31 clusters included a statement in their core perspectives that explained travel mode preference in terms of academics, nurture, and/or health benefits. For example, just over one third (39%) ofthe clusters strongly agreed with statement NOA: Kids who are physically active do better in school. Although the remaining clusters did not include that statement in their core perspectives, they generally did not disagree with the sentiment. The general consensus on that issue reflects in the top heavy distribution of clusters on the opportunity-propensity graph. Over half (54%) of the clusters included in their core perspectives the nurture related issues of enjoyment and bonding contained in statements PCI: Walking to school is more enjoyable than driving, and statement PCB: Parents bond with their kids on the trip to school. In all but one case, the clusters that included these statements strongly agreed with them. However, it is important to note that a group of parents at one school shares an alternative perspective-that driving is more enjoyable than walking. In contrast, three quarters (74%) of the clusters included in their core perspectives the health-related issues of obesity and sleep contained in statements NHD: We worry about our kids becoming obese, and statement NHS: We try to sleep 249

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in as late as possible4 The absolute value of their mean scores for these issues indicates that they consider them to be high priorities. However, in all but two cases, the clusters strongly disagreed with the statements, indicating that they do not choose their mode of travel in response to the threat of obesity or the desire to sleep late in the mornings. That perspective justifies car travel, particularly if intervention emphasizes weight loss Over half (52%) of the clusters included more than one type of benefit in their core perspectives. For example, about one quarter (23%) of the clusters included nurtureand health-related issues in their perspectives, one tenth (I 0%) included academic and nurture-related issues and one sixth (16%) included all three. Ofthe clusters that included only one type of benefit in their core perspectives, approximately one third (30%) focused on either academic or nurture-related issues, while over two thirds (69%) focused on health Although it is striking how many clusters focused a portion of their profile on one or more benefits associated with active travel, the differences are especially significant because they indicate the issues around which intervention might be focused for sub-groups of parents. 4 Statement NHS could also be interpreted as a time-related issue. However since other statements relating to time were generally excluded from core perspectives, it is reasonable to use the alternative health-related interpretation. 250

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Social Conditions and Companionship A third similarity across schools was parents' interest in the social conditions in their neighborhoods, and their children's need for companionship on the way to school. All but one cluster included at least one statement in their core perspectives relating to these issues, although they generally emphasized a need for companionship over concerns about stranger danger. For example, just under half(45%) ofthe clusters included in their core perspective issues about strangers contained in statement COP: There are strangers out there waiting to take your kids, statement CDS: People no longer know their neighbors like they once did, and statement CDK: Kids need protection from other kids and youth. However, almost an equal number of clusters disagree as agree with the statements, and most clusters were ambivalent about the statements individually. In contrast, over half (52%) of the clusters included in their core perspective statement NIA: By third grade, kids can get to school okay on their own. In all but one case, parents disagreed with that statement, suggesting that companionship is a high priority for their younger children's trip to school. Of those clusters that did not strongly disagree with statement NIA, half strongly agreed either with statement PSC: If children yelled for help in our area, someone would protect them, or with statement NIS: Children can be safe on the street if they learn the right skills. Over half (55%) of the clusters included alternatives to parents escorting their children to school, expressed through statement PSA: I can't trust other adults to get 251

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my kids to school, and statement PSG: Children would be safe if they traveled in groups Only infrequently did clusters that expressed concern about children's ability to manage the trip alone also include a statement explaining the need for companionship in terms of social conditions. For example, only one quarter expressed concern regarding potential assailants (statements CDP and CDK). Instead, those clusters tended to focus on hazardous road and traffic conditions. Contextual Conditions A fourth similarity across schools was parents' concerns about contextual conditions. All but four of the clusters included at least one statement in their core perspectives that described conditions that could either encourage or prevent them from walking or biking their children to school. For example, one quarter (26%) included in their cluster profile statement CEW: Colorado weather is ideal for walking and biking to school. More frequently, the clusters focused on traffic conditions in their neighborhoods. For example, three quarters (81%) of the clusters included in their core perspectives the traffic-safety issues contained in statements CTR: There are no busy roads in our neighborhood, statement CTC: There are dangerous crossings in our neighborhood, statement CTE: Too many drivers disobey traffic rules and signals, and/or statement CTD: Drivers are too distracted by their phones and kids. In all but 252

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two cases, the clusters concurred that road and/or traffic conditions in their neighborhoods were hazardous. In contrast with the overwhelming consensus that busy streets exist in the neighborhood, far fewer clusters included the other statements that more specifically described hazards It is important to note that two groups of parents from opposite ends of the city shared an alternative perspective about drivers' obedience to traffic rules and signals, and did not share the other clusters' concern for busy roads and crossings. One cluster disagreed with statement CTE so strongly that it received a mean score of -4.00, which means that every parent in that cluster rated the issue as highest priority. However nearly half ( 44%) of the clusters that expressed contextual concerns (42% overall) disagreed with statement CEI: There really aren t many safe routes to our school. This apparent contradiction suggests that hazardous conditions do not entirely prevent parents from using active travel modes if they have the desire or need to do so. General Disagreements Time Conspicuously lacking from two thirds of the clusters (71%) were statements expressing concern for parents' time spent traveling back and forth from school. The third of the clusters that did include one or more of those statements were divided in their attitude For example only one cluster agrees and two disagree with statement PTR:families are in too much of a rush to walk to school. Similarly, only two clusters 253

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agree and two disagree with statement PTS: Families plan their schedules around their trips to school. Three clusters agree and two disagree with statement PTW: Working parents can't take time to walk to school And finally one cluster agrees and one disagrees with statement PTM: Parents save time by combining school trips with errands. The general ambivalence that parents express about this issue contradicts the study by Jones Dix et al. (Jones, Dix et al. 1983), which identifies household scheduling concerns as among the key factors influencing mode choice for school trips. That contradiction underscores the need to identify attitude-based subgroups of the active travel program's target population. Summary Active travel programs aim to increase the proportion of school trips that children and their parents make by walking or cycling. Planning research helps to guide those interventions by identifying factors that discourage active travel so that the path might be cleared for that behavior to flourish. Much of the extant research narrows findings to a small number of the most significant factors or barriers, such as travel distance, traffic danger (Jones Dix et al. 1983) or stranger danger (Joshi and MacLean 1995) so that policy can focus on conditions that seem to have the greatest impact on travel mode choices. However, demographic studies indicate that people respond differently to similar contextual conditions (McMillan 2006a; Bemetti Longo et al. 2008; 254

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McDonald 2008a). That research often examines the ways that various groups of people-defined by standard socio-economic indicators like race/ethnicity, gender or household income choose to travel, given one or more contextual conditions. Results from those studies do little to explain how personal characteristics influence travel mode choices, instead reinforcing the emphasis on a small number of external factors. This study used Q-technique to examine parents attitudes about active travel so that planners can tailor intervention to target groups defined by relevant predispositions as opposed to standard social indicators. I described my research methods for the two-phase project in Chapters Four and Six. Cluster analysis of the Q-sort resulted in 31 core perspectives, or clusters of parents, characterized by their unique, shared perspectives of the school commute. By averaging the mean scores of relevant statements included in the clusters profiles I illustrated a general typology of attitudes based on the opportunity-propensity dichotomy from Chapin's (1974) General Model of Human Activity Patterns. A majority of the core perspectives appeared in the top-right quadrant of the graph, meaning that they held at least some level of interest in using active forms of travel for school trips and that the opportunity to do so was available to them. The second largest group of clusters appeared in the top-left quadrant, meaning that while they were somewhat interested in active travel, they perceived a lack of opportunity to do 255

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so. Only four clusters appeared in the lower two quadrants, meaning that they preferred to drive. It is important to note that positive attitudes about active travel did not indicate that the members consistently used active travel for school trips. Examination of clusters within each quadrant revealed considerable variation in travel behavior, suggesting that the opportunity-propensity measure is insufficient as a predictor of mode choice. Instead, it is necessary to also examine characteristics of the individual cluster profiles to guide intervention Core perspectives often shared one or more characteristics. Correlations between clusters at each school ranged from very low scores that indicated opposite views on certain issues, to very high scores that indicated nearly (but not quite) homogeneous attitudes on many issues. I compared profiles of the 31 clusters and found several striking similarities, including preferences for physical activity, perceptions of benefits associated with active school travel, and recognition of social and environmental conditions that make active travel possible. Concerns about household scheduling and other time-related issues were regarded with ambivalence, suggesting that these concerns affect only certain subgroups of parents. I discuss the policy implications of these findings in Chapter Eight. 256

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CHAPTERS: ANSWERING THE QUESTION 'WHAT DRIVES PARENTS?' Introduction I began my dissertation with the ambitious overarching goal of bridging a gap between traditional planning research regarding travel behavior and intervention that aims to influence travel mode choices My research accomplished key objectives relating to that goal including identifying and comparing predispositions that underlie parents' mode choices for school trips. After starting my research, I realized that it would also be necessary to examine the relationship between parents' perceived opportunity and expressed propensity to engage in active travel. Although I made progress in that direction, further consideration of the relationship is warranted if planning research is to encourage people to walk more frequently. I begin this chapter by presenting the three main accomplishments of my dissertation, which relate to practice, research and the relationship between the two. I conclude by discussing limitations and corresponding directions for further research. Main Contributions This dissertation's main contributions stem from my approach for examining an active travel intervention's diverse target population. In the first three chapters, 257

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I argued two key points. First, extant planning research about active travel emphasizes environmental factors and focuses on parents' hindered opportunity to walk, assuming that they would if they could. Second, previous research acknowledges but also diminishes variance in travel behavior as it identifies most important factors associated with travel demand and mode. Those points led me to use mixed methods to investigate parents' experiences and perceptions of the commute to elementary school, and to consider their propensity in addition to their opportunity to walk. The qualitative portion of my research that I presented in Chapters Four and Five found considerable disparity in parents' experiences and perceptions of travel related issues, which not only acknowledged variance in travel behavior, but also indicated variations in parents' opportunity and propensity to walk children to school. More importantly, that portion of my research provided the raw materials that were necessary for me to further examine attitudinal diversity about active travel in the context of Denver's public elementary schools. In Chapters Six and Seven, I presented the quantitative portion of my research, in which I found that opportunity and propensity were positively, albeit weakly correlated. However, I also found that travel characteristics are not a function of opportunity and propensity, as Chapin's (1974) human activity model suggested. Rather, attitude groups found in each quadrant of the opportunity-propensity graph include shades of travel behavior between typically walk and typically drive. These 258

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findings have implications for both practice and research. Perhaps more importantly, they help to explain why predictive models do not provide sufficient guidance for intervention. I discuss each in turn below. Implications for Practice Findings from my research suggest that based on the type of attitude (as per the four quadrants on the opportunity-propensity graph), there are a variety of ways that intervention can leverage positive behavior to incrementally increase numbers of walking trips. Because parents' travel behaviors include shades of preference that is, even those who typically choose one mode sometimes choose another intervention can build on their current numbers of walking trips by addressing issues that resonate strongly with the attitude-based clusters. However, addressing those issues requires selecting appropriate intervention tools. Selecting Intervention Tools The first task in selecting intervention tools is to identify meaningful attitude groupings in order to provide the most effective application of resources. That is to say, the program facilitator needs to know his or her audience before designing the intervention. As I explained in Chapter Seven, it is possible for the number of perspectives obtained through Q-sorting to equal the number of respondents, since each person's perspective is based on his or her unique experience. Despite that individuality, it is possible to find relevant commonalities among groups of 259

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respondents without generalizing to the entire population. My research revealed 31 attitude-based clusters at the seven schools, with 3-6 distinct perspectives at each school. The next task is to select the most appropriate combination of key issues to address at each school. Using the Q-sort method, perspectives about active travel are limited to permutations of issues defined by the research instrument. In that respect the process of selecting intervention tools reflects Simon's (1957) theory of bounded rationality and Lindblom's ( 1959) theory of incrementalism by identifying the "best options from among a limited subset of infinite alternatives My content analysis of open-ended interviews with parents revealed a hierarchy of key issues that could be addressed through intervention. However in each of the schools the SR2S non infrastructure programs that I observed overlooked many relevant issues potentially missing opportunities to increase numbers of walking trips. It is not necessary to apply every intervention tool at every school. As I mentioned in Chapter One, Safe Routes to School legislation recommends applying what it calls the 5 E's", a combination of intervention categories that address opportunity-related and propensity-related issues through education, encouragement engineering, enforcement and evaluation (Safe Routes to School National Partnership, 2009). The 5 E's approach is comprehensive. However, each "E" category can address a variety of issues so it is still necessary to select tools that are relevant at each specific school. For example as I mentioned in Chapter Seven, 81% 260

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of the attitude groups expressed concern for dangerous traffic conditions in their neighborhoods, but half of them recognized that there are safe routes between home and school. While it may be universally appropriate to teach children how to cross a street safely, to encourage walking at some schools it may be more effective also to teach families which routes are safer for pedestrian travel (and why) and to reinforce those lessons with walking excursions or by establishing walking bus routes. If intervention tools based on the 5 E' s were appropriately selected and applied at each school, Safe Routes to School could influence a wide range of parents to change their travel behavior. However, the process of funding the programs that I observed in my research negatively impacted implementation by preventing facilitators from strategically selecting and applying intervention tools. Program Implementation Implementation of the Safe Routes to School programs that I observed fell short ofthe comprehensive 5 E's approach outlined in legislation. The shortcomings resulted from a very broad distribution of limited funding and from poor timing of grant funding to local program facilitators. First, as I mentioned in Chapter One, federal legislation earmarked 612$ million for Safe Routes to School programs nationwide over fiscal year 2005-2009. The amount of money allocated to each state depended on its proportion of enrolled students. Beginning with that proportion, dividing by five years, among several cities 261

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and between infrastructure and non-infrastructure programs, the amount of money available for intervention was very limited and could not be applied to every school in the state. As a result, the Colorado did not allow schools to receive multiple grants. Given the one-time opportunity to receive SR2S funding, schools should not have treated the grants as direct intervention, but should have used the money to establish in-house programs and to leverage resources. However if the 2005 SAFETEA-LU legislation provided had limited funding to SR2S as a pilot study and that future legislation will be more generous, the state should have strategically selected schools as exemplars, funded them adequately to implement comprehensive intervention and not agonized over the inequitable distribution of resources. Second the timing of Colorado s SR2S grant funding was an impediment to strategic intervention For the 2007-2008 SR2S grant period state of Colorado DOT required facilitators to propose intervention before they had meaningful contact with the schools or their target populations. Although certain program elements were prerequisite, the scope of each intervention related more to the agendas of facilitating organizations than to the travel behaviors or attitudes of parents The state awarded grants based on proposed active travel programs without concern for achieving targeted comprehensive intervention. Additionally, due to the late release of program funding, these facilitators were pressed to concurrently conduct surveys of parents and in-class interventions with students in order to finish before the end of the fall semester. By the time that 262

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facilitators and/or their contracted analysts sifted through survey data, their programs were nearly completed. There was no expectation for facilitators to use that local research to guide intervention That disconnect is indicative of a shortcoming in active travel research that I address in the next section. Implications for Research As I explained in Chapter Three, traditional transportation planning research has not been sufficient as a guide for active travel intervention. In addition to emphasizing opportunity related factors, its methods and findings are inaccessible to program facilitators, who need site-specific information to determine which interventions are most appropriate to influence their target populations. A conceptual model of travel behavior can be useful for researchers and practitioners if it explicitly describes the role and nature of intervention. Findings from my research led me to merge and refine McMillan's (2005) Chapin's (1974) and Schuler's (1979) conceptual models of travel behavior (see Chapter Two for comparison). My revised model focuses attention on attitude types and includes intervention to address propensity-related factors. I present the revised travel behavior model and explain key modifications below (see figure 8-1). 263

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Figure 8-1: REVISED CONCEPTUAL MODEL OF MODE CHOICE FOR ELEMENTARYSCHOOLTRAVEL External Sources of Change I : (e g Economic, Population Cultural) rr Availability and Quality of Facilities or Services Congeniality of Surroundings Personal Factors Cognition Valuation and Ranking of Travel Alternatives Motivations, Enjoyment & Thoughtways Predisposing Action Roles & Personal Characteristics Preconditioning Action Public. NGO and Private Sector Response Producing Change in Opportunities Investments & Regulation OPPORTUNITY to Engage in the Activitv PROPENSITY to Engage in the Activity Public. NGO and Private Sector Response Producing Change in Propensity Educational Programs Events and Incentives External Sources of Change (e.g. Technological, Economic) Pedestrian Presence Producing Change in Congeniality TRAVEL BEHAVIORS 264 + Satisfaction Levels Producing Change in Propensity External Sources of Change (e .g. Technological, Economic)

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Basic Structure My conceptual model focuses on opportunity-related and propensity-related factors, and their relationship to possible attitude types regarding active school travel. This relationship differs considerably from attitude-based choice models in social psychology (Dijst, Farag et al., 2008; Garling, Gillholm et al., 1998; Perugini & Conner, 2000; Walker, 2006) because the attitude types in my model are defined by parents' ranking of related issues, not by their affective response to active travel. The basic structure ofthe model combines elements of McMillan's (2005), Chapin's (1974) and Schuler's (1979) choice models (see Chapter Two). Similar to McMillan (2005) and Chapin (1974), my model differentiates opportunity-related environmental factors and propensity-related personal factors. Reflecting Schuler's ( 1979) model, the set of propensity-related, personal factors includes cognition, valuation and ranking of travel alternatives. In the revised model, I also indicate that environmental factors influence the socio-economic context and subsequently effect a change in personal factors. For example, environmental factors such as availability, quality and perception of facilities and services influence changes in the socio-economic context by encouraging residential mobility. This external influence is especially significant when assessing the impact of urban form changes on travel behavior, since people 265

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who prefer to walk may self-select into "pedestrian-oriented" neighborhoods, making it look like the change to built form impacted people's individual travel behaviors. Children's Agency Similar to McMillan's (2005) model, I acknowledge that parents largely determine their children's travel behavior. However, studies show that children are also active agents in decisions that affect them (Corsaro, 1997). For example, as I reported in Chapter Five, students used peer pressure to encourage parents to observe Walking Wednesdays at one of the schools, and then gradually embraced active travel on a more regular basis. To reflect children's influence, therefore, the revised model includes children's and parents' travel behavior, and indicates that the satisfaction of children and parents can influence motivations and decisions regarding future trips. Opportunity-Propensity Balance Similar to Chapin (1974) and McMillan (2005), I illustrate the role that planning intervention plays in altering conditions that determine attitudes. However, whereas those models emphasized environmental intervention by indicating urban form changes resulting in a change in mediating factors and public, NGO and private sector responses resulting in a change in opportunities, respectively, the refined model balances environmental and personal intervention by including public, NGO and private sector responses resulting in a change of propensity on the right-hand side This modification takes into account the educational programs, events and other 266

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incentives that active travel programs such as Safe Routes to School use to encourage walking (see Chapter One). Two Types of Intervention Contrary to Chapin's ( 197 4) model, both types of intervention that I included in the revised model respond to problems and/or opportunities associated with travel behavior, not the behavioral pattern itself. For example, as I explained in Chapter One, policy makers have expressed concern about increasing rates of childhood overweight and obesity and have developed active travel programs to encourage an increase in physical activity. However, the revised model suggests that increased pedestrian presence can directly influence environmental congeniality, for example, by making streets safer for pedestrians. In response to perceived problems and/or opportunities associated with travel behaviors, policy-makers implement investments and regulations to address the availability and quality of facilities and services. For example, cities increase the width of sidewalks in school neighborhoods and schools add lockable bike storage facilities near playgrounds. Those interventions address the opportunity to engage in active travel by decreasing a variety of costs, but can also catalyze a change of attitude and behavior. External sources of change also influence policy-makers to address environmental conditions. For example, prosperous economic conditions make it 267

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possible for policy-makers to fund initiatives. Technological advances such as flashing speed signals make it possible to regulate traffic more effectively. Damage from a heavy storm can also influence policy-makers to address environmental conditions. Responding to perceived problems and/or opportunities, policy-makers also implement educational programs, events and incentives to address cognition and valuation of travel alternatives, and to provide additional motivation for active travel. Programs that teach children how to walk or bike safely near traffic can improve perceptions of those travel alternatives ifthe students relay the experiences to their parents afterwards. Students also encourage their parents to let them walk to school to participate in special school events or because their peers are walking. A conceptual model can guide practitioners if it describes relationships between characteristics of target populations and possible intervention strategies Additionally, the model should accurately depict relationships between environmental and personal factors and travel behavior. Strengthening the Relationship Between Research and Practice As illustrated in the revised model, attitude types defined by levels of opportunity and propensity-comparable to McMillan's (2005) travel decisions before action is taken -are central to the discussion of travel behavior. However, my findings suggest that they do not predict travel characteristics This modification to 268

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previous models is significant because it addresses a persistent gap between travel behavior research and intervention. As I noted in Chapter Two, planning researchers argue that correlative studies poorly guide intervention because they lack causal explanations (Crane, 2000; Handy, 1996). For example, one cannot assume that because parents associate the choice to drive children to school with traffic danger, that they will walk if traffic danger is eliminated. This problem primarily occurs because the correlations focus on a few of the most significant factors as opposed to the complexity of the environment where the activity takes place. Handy ( 1996) recommended conceptual modeling to examine relationships among a more comprehensive array of variables in order to understand causality. In fact, traditional transportation planning research does use multivariate statistical modeling to predict travel behavior based on a selection of variables, and has reintroduced attitude theories in order to better capture behavioral tendencies (Garling, Gillholm et al., 1998). Those models are useful for estimating behavioral response to simulated environmental conditions at an aggregate level because they reflect general trends in very large populations. However, as I argued in Chapter Two, even conceptual models that examine relationships among environmental, socio-demographic and psychological variables still focus on environmental factors. In so doing, they assume binary responses to external stimuli, excusing behavioral outliers as the result of vague cultural or socio269

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demographic differences that ultimately compare to the control factors of the larger predictive models. That approach falls short for guiding behavioral intervention at a disaggregate level (i.e. a smaller population) for two reasons. First, by using measures of central tendency, the models overlook the diversity of the target population, which results in broad application of intervention tools that are not appropriate for some portion of the population. Second, even those respondents counted among the norm are likely to vary their behavior sometimes in response to varying external conditions. In this respect, the models overlook the variance of individuals, which is critical in behavioral intervention. Active travel intervention assumes that parents can and will change their mode choice given certain conditions. However, the same set conditions do not affect the entire target population equally. For example, as I described in Chapter Five, some parents see heavy traffic and dangerous intersections as inconveniences but not impenetrable obstacles Therefore it is necessary for the conceptual model to illustrate attitudinal diversity and the way that it can be addressed through intervention. Rather than indicating a causal relationship between opportunity propensity related attitudes and travel behavior, it suggests that those attitudes can and should influence the selection of intervention tools. My revised model indicates relationships between environmental and personal conditions, opportunity and propensity, parents' attitudes and possible types of 270

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intervention. However, additional research is needed to provide clear guidance to practitioners. In the next section, I discuss limitations of my dissertation research and possible directions for further research that will build on its contributions. Directions for Further Research The purpose of my dissertation was to bridge a gap between planning research about active travel and intervention that aims to increase numbers of walking trips to school. Findings provided insight into parents' predispositions about active travel, using an approach that promises to translate research into targeted intervention. There are also certain limitations to my study that suggest direction for further research. I conclude by identifying five such opportunities below. First, I argue that traditional planning research overemphasizes opportunity related, environmental factors that prohibit active travel and that it should also consider propensity-related factors that determine whether parents would be inclined to walk if they could. This normative argument is based on the assumption that if parents intend to walk they will, even if conditions are unfavorable. Social psychologists similarly suggest that goals and motivations influence travel choices more than attitudes about the travel mode per se (Dijst, Farag et al., 2008; Garling, Gillholm et al., 1998; Golob, 2003; Perugini & Conner, 2000; Walker, 2006). However, I found that the attitude types based on opportunity and propensity did not 271

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predict travel behavior. Further research is needed to understand the relationship between propensity and travel mode choices. Second, to understand the relationship between intended travel mode and actual travel behavior, it will be necessary to examine opportunity, propensity and travel characteristics more systematically than I was able to do in my dissertation. Based on my scatter plot of 31 attitude-based clusters, I found a weak correlation between opportunity and propensity. I also found that travel characteristics were not a function of opportunity and propensity. However, my estimation of travel behavior for each cluster was inexact due to the format of that portion of my questionnaire. Specifically, question #15 invited respondents to select one or more travel behaviors that apply from among a list of eight choices, but did not ask them to indicate how often each of the behaviors applies. For example, respondents who marked that they walk in good weather may do so once in a year or every time the sun shines. Future research should ask more specific questions about travel characteristics, making it possible to examine relationships between attitude types and specific travel behaviors. Third, although my revised conceptual model indicates that travel-related attitudinal diversity is a function of opportunity and propensity, practitioners would benefit from a typology of attitudes that includes various permutations of opportunity-related and propensity-related issues. My dissertation research found 31 clusters of parents, each defined by discrete sets of travel-related issues. In Chapter Seven I began to compare those attitudes, but fell short of establishing an attitudinal 272

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typology due to significant overlap. For example, as I explained in Chapter Seven, all but four of the clusters expressed preference for physical activity, and about a third mentioned specific motivations for their preference. However, over half of the clusters included more than one motivation for their preference (i.e. academics, nurture and health), making it difficult to categorize on that basis. This issue is worthy of additional consideration. Fourth, I argue that a behavioral approach to active travel research is warranted to more effectively guide intervention. As I mentioned previously, Qmethodology is well suited to that objective because it magnifies nuances in subjects' predispositions rather than diminishing them through generalization. Further, results from Q-sorting and cluster analysis will be more accessible to practitioners because they use plain language to describe similarities between people. However, the Q methodological approach differs considerably from the traditional modeling approach embraced by transportation planning. While findings from my study complement the findings of statistical research, it would be useful to integrate the predictive and behavioral studies. And finally, as I argued in Chapter One, it is important for planners to intervene in parents' mode choice for school trips because school trips constitute a significant proportion of non-work travel and because they impact the environment, affect children's health and determine independent mobility for children and caretakers. However, it is also necessary to encourage pedestrian travel on a larger 273

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scale and with a broader range of people. For example, Australia's TravelSmart program (www.travelsmart.gov.a!!i) takes a more comprehensive approach to travel mode intervention and uses social marketing to encourage people to use alternative transportation. This program would provide an appropriate case study at a larger scale. Even with larger scale programs, it is necessary to identify attitudinal diversity in people's travel choices and to tailor intervention appropriately. At any scale of travel intervention, it is necessary to find out what drives the driver. 274

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School Gender Bromwell BR2 Female BR6 Female BR8 Female Cory CR1 Female CRll Male CR13 Female CR18 Female CR19 Male CR3 Male CR6 Male CR8 Female CR9 Female Edison EDO Female ED1 Male ED10 Female ED2 Female ED3 Male ED4 Female ED5 Male ED6 Female ED8 Female Force FR3 Female Hallett HAO Male HA3 Female HA6 Male HA7 Female HAS Female HA9 Female APPENDIX A INTERVIEW SUBJECTS BY SCHOOL Mode Distance Kids Race busfdrive 1 hour 1 White walk/drive 5 min 2 White car/ carpool 3 miles 2 White 20 min. car drive 2 White car 10 miles 2 White bus/walk/ bike >1 mile 2 White carpool 15-20 min. 2 White car 2-3 miles 2 White walk/car 5 min. 3 White car >1 mile 1 White car/walk 1 mile 1 White car 2 miles 1 White car/walk 1/2 mile 2 White bike 10 min. ride 1 White car/bus 10-12 miles 1 Other car 7 min. drive 1 White walk/drive 2 blocks 4 Hispanic car/walk 5 min. 2 White walk/car <1 mile 1 White walk 10 min. walk 1 White walk 4 blocks 3 Hispanic car 1 mile 3 Hispanic car 15-20 min. >1 Black car 3 blocks 1 Hispanic car >5 miles 1 White car 2 miles >1 Black car 2 miles 2 Black walk 1 1/2 blocks 1 Black 275 Pseudo Anita Colleen Andrea Rory Jeff Cathy Debbie Chris Sean Michael Georgia Amy Mary Jeff Sayuri Annie Ernie Brooke Jim Melanie Patricia Modesto William Elizabeth Casey Gisene Yolanda Marie

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Lowry LRS Female walk across street 3 White Molly LR6 Male car 1.5 miles 2 White Kerry Philips PHO Female car 1.5 miles 4 Black Gail PH1 Male car 4-5 blocks 3 Black Monty PH2 Female car 4 blocks 1 Black Tori PH3 Female car 10 miles 2 Black Yasmine PH4 Female car 5 min. 2 Black Queen PHS Female car 15 min. 3 Hispanic Maria PH6 Female car 1/2 mile 3 White Jane PH7 Female car rv6 blocks 4 Black Teresa Sabin SA4 Female car 7-10 min. 1 Hispanic Alicia SAS Female car/walk 3 min. 5 Hispanic Ramona SA7 Female car 2 1/2 miles 7 White Brenda Slavens car/bike/ SL1 Female walk 4 blocks >2 White Janet SL2 Female car 3 miles >1 White Ashley carpool/ SL3 Female bike < 1 mile 2 White Alberta SL4 Female walk 6 houses 1 White Nancy carpool/ SLS Female walk 14 blocks 2 White Amy SL6 Female bus 15 min. ride 1 White Karen SL7 Male car 15 miles 2 White Jeff walk/bike/ SL8 Female carpool 1 mile 3 White Rachel SL9 Female carpool 1.7 miles 2 White Alexis Smith SM1 Male car 2-3 miles 1 Black Dave 5M3 Female car 5 blocks 1 Hispanic Alicia SM4 Female car/walk not sure 3 Hispanic Susanna SMS Female car 3 blocks 4 White Kat SM7 Female car/bike 1/2 mile 3 Hispanic Andrea Steck STO Female car/walk 1.6 miles 3 White Lyssa ST10 Female bus >1 mile 1 White Judy STll Female carpool >3 miles >2 White Nora ST12 Male carpool >3 miles 2 White Curt ST3 Female bus <10 min. >1 White Michele ST8 Female car/walk 4 blocks 1 White Anne 276

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Valdez VAl Female car 10-15 min. 2 Hispanic Juliana bus/car/ VA2 Female walk unknown 4 Hispanic Blanca VA3 Male car unknown 1 Black Albert 277

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Program Place City of Denver Coalition Denver Planning Edison Force Program 1 Sabin Slavens Bromwell Program 2 Cory Steck APPENDIXB LIST OF MEETINGS Meeting/ Event Date All groups 10/24/07 All groups 1/23/08 Subcommittee 10/17/07 Subcommittee 11/15/07 Subcommittee 1/11/08 Subcommittee 1/16/08 STS Committee 9/18/07 Bike Safety/Rodeo 10/5/07 Ped Safety 2/25/08 Ped Safety 2/26/08 STS Committee 4/1/08 STS Committee 9/17/07 Bike Safety/Rodeo 10/12/07 STS Committee 10/22/07 Ped Safety 1/17/08 STS Committee 9/11/07 Bike Safety/Rodeo 9/28/07 STS Committee 10/22/07 Ped Safety 2/7/08 Ped Safety 3/12/08 STS Committee 4/17/08 STS Committee 9/19/07 Bike Safety 10/19/07 Ped Safety 1_131/08 STS Committee 3/10/08 Rodeo-rain date 4/11/08 Principal 11/9/07 PTSA 11/13/07 Principal 11/19/07 PTSA 4/4/08 Principal PTSA 12/12/07 278 Focus no no no no no no no no no no no no no no

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Workshop 3/31/08 no Workshop 4/l/08 no Workshop 4/14/08 no Workshop 4/15/08 no Lowry Workshop 4/16/08 no Bike course 4/17/08 no Bike course 4/21/08 no Program 3 Bike course 4/22/08 no 5/5/08Walk and Wheel 5/9/08 no Workshop 4/8/08 no Workshop 4/9/08 no Valdez Bike course 4/10/08 no Bike course 4/11/08 no 5/5/08Walk and Wheel 5/9/08 no 279

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APPENDIXC INTERVIEW PROTOCOL Thank you for volunteering to participate in this research project. My name is_ and I am a student at the University of Colorado. This research project is entitled, "Driven to Succeed? Parents' Decision-Making Regarding the Journey to School." It is designed to learn various perspectives regarding children's daily commute to school. Your participation is voluntary. For your part in the research, I will ask you a series of questions about the commute to your child's school. This should take 30-45 minutes. You are free to stop the interview at any time, or to decline any question if you wish. Any information gathered as a result of your part in this research, including interview tapes and transcripts, will not have your name or any identifying information about you in it. Only the research team will have access to your complete file once the research is complete. I will not include your name or any other identifying information in my report about this research. In the remote chance that your confidentiality was breached, you would not experience any adverse effects. If you have any further questions about this research project, please contact the principal investigator, Kelly Zuniga, at 303-872-4136 or her faculty advisor, Professor Willem van Vliet at 303-492-5015. You may also contact the Human Subjects Research Committee Administrator, CU Denver Building, Suite 740; 303-724-1055, with any questions you may have about your rights as a research subject. 280

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Questions for Parents: 1) Decisions about trip to school: Who in your household makes decisions about your child's trip to school? What kinds of things influence decisions about your child's trip to school? 2) Companionship on trip to school: How frequently do you make the trip to school with your child? Do you allow your child to make the trip alone or with other people other than yourself? What are your primary reasons for escorting your child to school? 3) Mode of travel on trip to school: How do you and your child typically commute to school (walk, bike, drive, etc.)? How long does it take for you and your child to get from home to school? Do you typically go other places before or after the commute to school (work, errands, etc)? Can you describe the trips you made in the past three days (destination, distance, time, vehicle)? Day Time of Destination Distance Mode Companion Day 4) Changing commuting behavior: What conditions would make it possible for your child to walk or bike to school? How would active travel for the trip to school affect your lifestyle? On what conditions would you change the routine for your child's trip to school? 281

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APPENDIXD MAIN STUDY AGGLOMERATION SCHEDULES Edison-Agglomeration Schedule Cluster Combined Stage Cluster First Appears Stage Cluster I Cluster 2 Coefficients Cluster I Cluster 2 Next Stage I 9 28 .750 0 0 6 2 27 32 73I 0 0 II 3 6 10 .700 0 0 13 4 26 29 .694 0 0 I5 5 II I2 .675 0 0 I7 6 I 9 .669 0 I II 7 I4 25 .652 0 0 8 8 I4 3I .628 7 0 26 9 22 36 .6I8 0 0 I9 IO 4 21 .6I5 0 0 20 II I 27 .6I2 6 2 25 I2 7 8 .609 0 0 3I 13 6 I5 .594 3 0 27 I4 I6 19 .569 0 0 I9 I5 26 39 .562 4 0 23 I6 2 I8 .560 0 0 24 17 II 38 .525 5 0 2I 18 5 34 .513 0 0 34 19 16 22 .502 I4 9 3I 20 4 23 .494 10 0 30 21 11 20 .463 17 0 29 22 3 17 .461 0 0 30 23 26 33 .456 15 0 25 24 2 13 .440 16 0 27 25 1 26 .378 II 23 32 26 14 24 .370 8 0 29 27 2 6 .301 24 13 33 282

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28 35 37 .231 0 0 37 29 ll 14 .228 21 26 32 30 3 4 .226 22 20 36 31 7 16 .204 12 19 34 32 l ll .200 25 29 35 33 2 30 .169 27 0 35 34 5 7 .126 18 31 36 35 l 2 .044 32 33 37 36 3 5 -.084 30 34 38 37 1 35 206 35 28 38 38 1 3 -.344 37 36 0 Sabin-Agglomeration Schedule Cluster Combined Stage Cluster First Appears Stage Cluster 1 Cluster 2 Coefficients Cluster 1 Cluster 2 Next Stage 1 14 16 .719 0 0 18 2 4 35 .694 0 0 10 3 33 36 .650 0 0 8 4 8 21 .650 0 0 13 5 13 31 .602 0 0 14 6 7 24 .593 0 0 11 7 22 28 .577 0 0 12 8 23 33 .575 0 3 22 9 1 30 .544 0 0 24 10 4 26 .536 2 0 12 11 7 11 .512 6 0 17 12 4 22 .496 10 7 23 13 2 8 .492 0 4 29 14 13 25 .469 5 0 20 15 5 12 .456 0 0 31 16 19 27 .444 0 0 32 17 7 32 .421 11 0 30 18 10 14 .412 0 1 24 19 15 34 .400 0 0 26 20 13 29 .377 14 0 28 283

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21 3 9 .369 0 0 27 22 18 23 .368 0 8 25 23 4 17 .365 I2 0 28 24 I IO .336 9 I8 29 25 I8 37 .300 22 0 33 26 15 20 .300 I9 0 27 27 3 I5 .206 2I 26 31 28 4 13 .158 23 20 34 29 I 2 .155 24 I3 32 30 6 7 .137 0 17 36 31 3 5 .118 27 15 33 32 I 19 .037 29 16 34 33 3 I8 -.090 31 25 35 34 I 4 -.125 32 28 35 35 1 3 -.162 34 33 36 36 1 6 -.28I 35 30 0 Slavens Agglomeration Schedule Cluster Combined Stage Cluster First Appears Stage Cluster 1 Cluster 2 Coefficients Cluster I Cluster 2 Next Stage I 9 16 .813 0 0 5 2 11 27 .805 0 0 14 3 25 32 .776 0 0 8 4 6 36 .7I1 0 0 I4 5 9 21 .705 1 0 9 6 5 10 .694 0 0 15 7 2 30 .669 0 0 15 8 19 25 .665 0 3 21 9 9 15 .656 5 0 18 10 8 35 648 0 0 27 11 13 31 .635 0 0 22 12 3 17 .613 0 0 23 13 4 34 .594 0 0 18 14 6 11 .576 4 2 23 15 2 5 .550 7 6 26 284

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I6 I I8 .544 0 0 28 I7 I2 26 .537 0 0 33 I8 4 9 .533 I3 9 2I I9 I4 29 .525 0 0 24 20 23 28 .5I9 0 0 28 2I 4 I9 .48I 18 8 25 22 7 13 .463 0 1I 29 23 3 6 .4I5 12 14 26 24 14 33 .4I2 19 0 31 25 4 24 .369 21 0 30 26 2 3 .341 15 23 3I 27 8 20 .327 10 0 32 28 I 23 .28I I6 20 30 29 7 22 .262 22 0 32 30 I 4 .201 28 25 34 31 2 I4 .I94 26 24 34 32 7 8 .I44 29 27 33 33 7 I2 .044 32 I7 35 34 1 2 .006 30 31 35 35 1 7 -.1I7 34 33 0 Cory-Agglomeration Schedule Cluster Combined Stage Cluster First Appears Stage Cluster I Cluster 2 Coefficients Cluster 1 Cluster 2 Next Stage 1 12 21 .753 0 0 10 2 I4 16 .7I9 0 0 I6 3 9 IO .706 0 0 II 4 6 23 .706 0 0 I5 5 22 26 .694 0 0 I9 6 20 24 .688 0 0 14 7 19 25 .652 0 0 18 8 1 8 .649 0 0 I8 9 13 I8 .63I 0 0 II 10 I2 27 .598 I 0 2I II 9 I3 .576 3 9 I6 285

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I2 2 4 .575 0 0 I3 13 2 3 .556 I2 0 20 I4 7 20 .543 0 6 24 I5 6 28 .538 4 0 I9 I6 9 I4 .497 II 2 23 I7 5 II .475 0 0 22 I8 I I9 .449 8 7 24 I9 6 22 .400 I5 5 2I 20 2 I5 .349 13 0 23 2I 6 I2 .344 I9 IO 26 22 5 I7 .300 I7 0 26 23 2 9 .296 20 I6 25 24 I 7 .I96 I8 I4 25 25 I 2 .I 50 24 23 27 26 5 6 .069 22 2I 27 27 I 5 -.I09 25 26 0 BromwellAgglomeration Schedule Cluster Combined Stage Cluster First Appears Stage Cluster I Cluster 2 Coefficients Cluster I Cluster 2 Next Stage I 5 I7 .794 0 0 7 2 2 3 .788 0 0 4 3 6 I6 .7I4 0 0 6 4 2 13 .706 2 0 IO 5 IO 25 .68I 0 0 13 6 6 22 .659 3 0 I2 7 5 I5 .656 I 0 I2 8 I2 2I .63I 0 0 I5 9 I4 I8 .628 0 0 I4 IO 2 7 .600 4 0 I6 II 8 9 .544 0 0 14 I2 5 6 .487 7 6 I7 I3 1 IO .464 0 5 20 14 8 14 .450 11 9 23 I5 4 12 .444 0 8 20 286

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16 2 23 .434 10 0 19 17 5 19 .394 12 0 19 18 11 20 .325 0 0 22 19 2 5 .278 16 17 21 20 1 4 .262 13 15 24 21 2 24 .131 19 0 22 22 2 11 .031 21 18 23 23 2 8 -.044 22 14 24 24 1 2 -.136 20 23 0 Philips Agglomeration Schedule Cluster Combined Stage Cluster First Appears Stage Cluster 1 Cluster 2 Coefficients Cluster 1 Cluster 2 Next Stage 1 14 19 1.000 0 0 14 2 2 12 675 0 0 18 3 1 5 .671 0 0 7 4 3 13 .662 0 0 19 5 25 26 .625 0 0 23 6 9 16 .625 0 0 10 7 I 24 .589 3 0 20 8 15 32 572 0 0 27 9 8 22 .566 0 0 12 IO 9 30 .5I9 6 0 I5 II 4 20 .506 0 0 22 I2 8 29 502 9 0 20 13 6 IO .468 0 0 I6 I4 I4 I8 .437 I 0 18 15 9 31 .4I9 10 0 24 16 6 I7 .402 I3 0 30 17 7 23 .373 0 0 25 18 2 I4 .344 2 14 28 19 3 33 .331 4 0 22 20 1 8 .330 7 I2 24 2I 21 27 .294 0 0 27 22 3 4 .269 19 11 29 287

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23 11 25 .263 0 5 26 24 1 9 .236 20 15 26 25 7 28 194 17 0 29 26 1 11 .162 24 23 28 27 15 21 .075 8 21 31 28 1 2 .019 26 18 32 29 3 7 -.075 22 25 30 30 3 6 -.132 29 16 31 31 3 15 -.225 30 27 32 32 I 3 -.281 28 3I 0 ValdezAgglomeration Schedule Cluster Combined Stage Cluster First Appears Stage Cluster I Cluster 2 Coefficients Cluster I Cluster 2 Next Stage 1 5 16 1.000 0 0 13 2 3 12 662 0 0 8 3 2 13 614 0 0 4 4 2 7 .575 3 0 14 5 21 22 .525 0 0 16 6 10 14 .494 0 0 7 7 4 10 .469 0 6 I9 8 3 I7 .444 2 0 I6 9 I 9 .394 0 0 13 IO 18 20 .377 0 0 18 11 l-6 15 .375 0 0 14 8 11 .338 0 0 17 13 1 5 .331 9 1 I5 14 2 6 .294 4 11 17 I5 1 I9 .278 13 0 20 16 3 21 .225 8 5 20 I7 2 8 137 14 12 18 18 2 18 .087 17 10 19 19 2 4 .038 18 7 21 20 1 3 175 15 16 21 21 1 2 -.224 20 19 0 288

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APPENDIXE MAIN STUDY DENDROGRAMS Edison Dendrogram Rescaled Distance Cluster Combine C A S E 0 5 10 15 20 25 Label Num +---------+---------+---------+---------+---------+ 260081 9 260435 28 260006 1 260413 27 260697 32 I _j 260411 26 260450 29 260876 39 260714 33 _j 260127 11 260145 12 _j 260848 38 260247 20 I 260151 14 260404 25 260694 31 f-__j 260399 24 260053 6 260113 10 260157 15 _j I I 260022 2 260212 18 260149 13 I I I 260452 30 260776 35 260805 37 260036 4 260287 21 260298 23 J l I 260028 3 260200 17 J 260042 5 260731 34 I 260056 7 260069 8 I r--260290 22 260802 36 260160 16 260243 19 I I I I 289

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Sabin Dendrogram Rescaled Distance Cluster Combine CA S E 0 5 10 15 20 25 Label Num +---------+---------+---------+---------+---------+ 210200 14 210206 16 _) 210137 10 210009 1 210488 30 I I 210128 8 210273 21 _j 210028 2 210227 19 210455 27 I 210193 13 210493 31 t-I 210315 25 210480 29 210283 22 210463 28 I 210042 4 210684 35 210368 26 _) r-I-210224 17 210537 33 210685 36 210284 23 _j I l 210225 18 210745 37 I r--210066 5 210192 12 J 210038 3 210136 9 210205 15 210544 34 210249 20 I 210098 7 210303 24 J 210191 11 210532 32 I 210093 6 290

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Slavens Dendrogram Rescaled Distance Cluster Combine c A S E 0 5 10 15 20 25 Label Num +---------+---------+---------+---------+--------+ 220080 9 220137 16 _j 220170 21 220133 15 220038 4 220269 34 I t-I 220190 25 220261 32 220150 19 __] I I 220182 24 220007 1 220143 18 220179 23 220238 28 I r-I 220124 14 220246 29 I 220265 33 220072 5 220083 10 220008 2 220247 30 I I I I 220013 3 220138 17 I r--220084 11 220227 27 220075 6 220274 36 _j I I I 220088 12 220210 26 I 220079 8 220273 35 220166 20 J I I 220122 13 220250 31 220078 7 220178 22 I l I I I 291

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Cory Dendrogram Rescaled Distance Cluster Combine c A S E 0 5 1 0 15 20 25 Label Num +-------+---------+-------+---------+---------+ 240132 12 240431 21 240473 27 _j I I 240433 22 240472 26 _j ,___ 240053 6 240444 23 240487 28 _j I I 240031 5 240127 11 I 240192 17 240170 14 240183 16 _j 240092 9 240099 10 240147 13 240193 18 _j t-I 240003 2 240027 4 240020 3 I I 240179 15 240411 20 240459 24 240066 7 _j I J t-240361 19 240465 25 240001 1 240079 8 I I J I 292

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Bromwell Dendrogram Rescaled Distance Cluster Combine C A S E 0 5 10 15 20 25 Label Num +---------+---------+---------+---------+---------+ 230037 5 230167 17 230157 15 _j I I r--230045 6 230166 16 230183 22 I l I 230174 19 230017 2 230026 3 230132 13 230065 7 230187 23 _j I I I I I I 230193 24 230123 11 230179 20 r--I 230148 14 230169 18 230096 8 230099 9 I I J I 230101 10 230199 25 I 230001 1 230128 12 230182 21 230030 4 I I J 293

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Philips Dendrogram Rescaled Distance Cluster Combine C A S E 0 5 10 15 20 25 Label Nurn +---------+---------+---------+---------+---------+ 270214 14 270262 19 _j 270259 18 270021 2 270204 12 J J 270339 25 270343 26 270095 11 I J270074 9 270249 16 I 270368 30 270373 31 _I 270001 1 270046 5 270309 24 t---I I I t-270060 8 270292 22 270365 29 I I I 270241 15 270394 32 270290 21 I I I 270344 27 270050 6 270091 10 270253 17 f-. I I I 270030 4 270277 20 r-I 270029 3 270209 13 270900 33 I 270059 7 270307 23 270356 28 l l J 294

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Valdez Dendrogram Rescaled Distance Cluster Combine C A S E 0 5 10 15 20 25 Label Num +---------+---------+---------+---------+---------+ 250080 5 250264 16 250037 1 250165 9 _j I 250532 19 250573 21 250584 22 I 250063 3 250209 12 250271 17 I I I 250176 10 250227 14 250078 4 I I 250298 18 250537 20 I 250136 8 250200 11 tI 250041 2 250223 13 250114 7 t--I I I 250110 6 250244 15 I 295

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APPENDIXF PILOT STUDY IDEAL TYPES Pilot Ideal Type: Cluster A General Issues M A ost lgTee M tD. OS 1sagree Code Statement and Mean Score Code Statement and Mean Score It is important to protect the kids Schools should focus on students' from unsafe people. grades, not their health. SPV (2.50, 3.14, 2.50, 3.33, 1.67) PLV (-3.88, -1.14, -1.50, -0.67, -3.17) I can't trust other parents to be Kids who are physically active are responsible getting my kids to likely to do better in school. school. OBV (2.50, 0.71, 0.50, 1.83, 2.50) TLI ( -2.38, 1.43, -0.50, -2.83, -1.92) I have never really considered Walking children to school would walking or biking my kids to be time well spent. school. TLV (2.25, 1.29, 2.00, 2.33, 1.58) OPV (-2.38, -2.14, 1.00, -2.17, -2.50) Children should be supervised Our neighborhood has plenty of when they are outside. opportunities for outdoor recreation. MBI (2.25, 2.71' 2.00, -0.17' 0.25) OLI (-2.00, -1.71, -0.50, 1.83, 0.67) 296

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Pilot Ideal Type: Cluster B-Safety and Environment Focus M A ost .gree M o ost 1sagree Code Statement and Mean Score Code Statement and Mean Score It is important to protect the kids Pollution from cars is not much of a from unsafe people. problem where I live SPY (2.50, 3.14, 2.50, 3.33, 1.67) ELI (-1.25, -2.43, -2.25, -3.17, -1.83) There is too much pressure on I worry about my children getting children to participate in organized hurt in an accident. activities SPI ( 1.25, 2.86, 2.50, 1.50, 0.50} TBV (-0.50, -2.14, -2.50, 0.67, -1.83) Children should be supervised My children can decide how they when they are outside. want to get to school. MBI (2.25, 2.71, 2.00, -0.17, 0.25) MPV (-0.63, -2.14, -1. 50, -3.33, -2.25) I have never really considered It is important for me to do what I walking or biking my kids to can to protect the environment. school. EPV (1.63, 2.57, 0.75, 3.00, 1.75) OPV ( -2.3 8, -2.14, 1.00, -2.17, -2.50) The world is not a safe place for children these days. SBV (1.00, 2.57, -0.75, -0.83, -1.33) Walking and biking to school can help reduce auto emissions. EBV (1.38, 2.43 -1.00, 2.67, 1.92) High gas prices put financial pressure on our household. EPI (0.25, 2.14, 1.00, 0.33, 1.50) 297

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Pilot Ideal Type: Cluster C -Fitness and Safety Focus M tA OS M o ost Code Statement and Mean Score Code Statement and Mean Score Physical fitness is very important It is hard to wake up early in the to our family. morning. PPY (1.75, 1.43, 2.75, 1.83, 2.58) PPI (-0.75, 0.00, -3.50, 0.50, -0.75) There is too much pressure on I worry about my children getting children to participate in organized hurt in an accident. activities. SPI (1.25, 2.86, 2.50, 1.50, 0.50) TBY (-0.50, -2.14, -2.50, 0.67, -1.83) It is important to protect the kids If car traffic has to be cut, school from unsafe people. trips should be first to go. SPY (2.50, 3.14, 2.50, 3.33, 1.67) ELY (-1.38, -1.57, -3.17, -2.25) Children can be safe on the street Pollution from cars is not much of a if they learn the right skills. problem where I live. SBI ( -0.50, -1.86, 2.25, 1.50, 0.42) ELI ( -1.25, -2.43, -2.25, -0.83, -2.08) Walking children to school would Kids in our neighborhood mostly be time well spent. stay indoors after school. TLY (2.25, 1.29, 2.00, 2.33, 1.58) PLI (-1.00, -0.71, -2.25, -0.83, -2.08) Children should be supervised when they are outside. MBI (2.25, 2.71, 2.00, -0.17, 0.25) 298

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Ideal Types: Cluster D General Issues Most Agree M o ost tsagree Code Statement and Mean Score Code Statement and Mean Score It is important to protect the kids My children can decide how they from unsafe people. want to get to school. SPV (2.50, 3 .14, 2.50, 3.33, 1.67) MPV ( -0.63, -2.14, -1.50, -3.33, -2.25) It is important for me to do what I Pollution from cars is not much of a can to protect the environment. problem where I live. EPV (1.63, 2.57, 0.75, 3.00, 1.75) ELI ( -1.25, -2.43, -2.25, -3.17' -1.83) Children need a sense of If car traffic has to be cut, school independence. trips should be first to go. MBV (1.00, 0.14, -0.25, 2.83, 1.75) ELV (-1.38, -1.57, -2.50, -3.17, -2.25) Walking or biking to school I can't trust other parents to be would take time and planning on responsible getting my kids to my part. school. TPI (-0.88, -0.57, 0.50, 2.83, 0.00) TLI (-2.38, 1.43, -0.50, -2.83, -1.92) I have never really considered Children's obesity is a serious walking or biking my kids to national problem. school. PBV (1.50, 1.57, 2.25, 2.67, 2.33) OPV (-2.38, -2.14, 1.00, -2.17, -2.50) My kids walk to school on the days Walking and biking to school can when there are incentives and help reduce auto emissions. special events. EBV (1.38, 2.43, -1.00, 2.67, 1.92) OLV (-0.63, -0.14, -1.25, -2.17, -0.33) Americans are too dependent on oil and other non-renewable resources. EBI (0.25, 0.43, -0.25, 2.50, 1.42) My children do not have many school friends in the neighborhood. MLV (0.25, -0.86, 0.00, 2.50, -1.67) There are dangerous roads and crossings in our neighborhood. SLI ( 1.63, 0.00, 0. 75, 2.33, -0.17) Walking children to school would be time well spent. TLV (2.25, 1.29, 2.00, 2.33, 1.58) 299

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Ideal Type: Cluster E -Physical Exercise Focus M A ost lgree M o ost tsagree Code Statement and Mean Score Code Statement and Mean Score Physical fitness is very important Schools should focus on students' to our family. grades, not their health. PPV (1.75, 1.43, 2.75, 1.83, 2.58) PLV (-3.88, -1.14, -1.50, -0.67, -3.17) I have never really considered Kids who are physically active walking or biking my kids to are likely to do better in school. school. (OBV (2.50, 0. 71' 0.50, 1.83, 2.50) OPV 2.38, -2.14, 1.00, -2.17, -2.50) Children's obesity is a serious If car traffic has to be cut, school national problem. trips should be first to go. PBV (1.50, 1.57, 2.25, 2.67, 2.33) ELV (-1.38, -1.57, -2.50, -3.17, -2.25) My children can decide how they want to get to school. MPV ( -0.63, -2.14, -1.50, -3.33, -2.25) Kids in our neighborhood mostly stay indoors after school. PLI (-1.00, -0.71, -2.25, -0.83, -2.08) 300

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Statements Eliciting Weak Responses Statement and Code Mean Scores (A,B,C,D,E) Traffic congestion around the schools is a big problem. SLV (0.88, 0.00, 1.00, 0.50, 0.75) Children are less physically active than they used to be. PBI (0.50, 0.43, -0.25, 1.50, 1.83) I can save time by picking up the kids on the way to work or errands. TPV (-0.50, -0.57, -1.25, 1.67, 0.33) Most families have two working parents and are too busy to walk to school. TBI (-1.50, 0.57, -0.25, 0.50, 0.25) I spend most of my days chauffeuring my children to school and other activities. MPI (-1.50, -0.57, -0.75, -1.17, -0.83) Most kids know their way around the neighborhood. MLI (-0.25, -0.57, -0.25, -0.33, 0.25) It feels good to be able to drive places when I want to. OPI (0.63, -0.57, 1.50, 1.83, -0.08) Kids are too stressed out these days because of all of the demands on them. OBI (0.88, -0.71' 0.00, 1.33, -1.58) 301

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APPENDIX G MAIN STUDY IDEAL TYPES Edison Ideal Types Cluster Cluster Cluster Cluster Cluster Cluster A B C D E F N 9 8 7 2 5 8 PTR 0.33 0.00 -1.29 0.00 1.75 0.22 0.62 PTS -0.43 ... :,.t< -0.20 1.88 PTW -0.78 -0.87 .2.80 1.75 PTM 0.67 0.62 -0.57 -2.00 1.00 1.50 PCE 0.00 >, 2.12 -0.71 -2.00 0.60 PCI 2:00 1.75 1.29 0.00 1.20 2.25 PCP -1.00 -0.50 1.57 -1.50 1.00 -0.25 PCB 1.56 1.50 1.14 -0.50 1.20 1.12 PSG 0.67 -0.25 -0.14 2.00 -0.80 0.75 PSU -1.67 -2.38 -1.71 -0.50 -1.40 -1.88 PSC 1.33 -0.12 0.43 2.so -o.8o 0.87 PSA ... 2:89 -2.50 -1.43 -1.50 -2.40 -0.62 NIT -1.11 0.12 0.86 .2.00. 1.60 1.00 NIF 1.67 0.87 1.43 -0.50 1.40 1.25 NIS ..... 2 .78 0.75 0.57 1.00 0.80 0.25 NIA -0.11 -1.75 0.50 ... 2.QO. .. 25 NHD NHA : 2.oo 1.40 2?ls" NHE 1.00 -0.20 1.50 NHS -0.38 -0.71 NOD 0.22 -1.62 1.29 NOA 1.00 0.20 1.38 NOB -1.11 -0.88 -0.57 1.00 0.40 -1.62 NOS -0.44 -0.38 -0.71 -1.50 -1.60 -1.00 CEG 1.44 .. -2.00 -1.00 0.38 CEP -0.33 -0.50 -0.40 -0.63 CEI 0.00 0.60 ;: .. 2.62 302

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CEW 2 ;.\78 ':) ,, /iz.oo, 0.50 1.80 0.75 CTC -0.67 1.62 0.86 -l.I2 CTD -0.33 -0.12 1.14 1.00 I.40 0.75 CTR -1.44 _; y : t : ' 4 2'' 3 ' -1.50 CTE -0.78 0.75 0.71 CDP -1.00 -0.86 -0.50 CDC -1.67 -1.00 -0.14 -0.50 -0.20 0.38 CDS 0.1 1 0.12 0.71 1.00 -0.80 0.25 CDK -0.1 1 0.00 -0.71 -0.50 Edison Cluster Correlations (Pearson, s Coefficients) Cluster Cluster Cluster Cluster Cluster Cluster A B c D E F Cluster A I 0.78 0.66 0.26 0.20 0.64 Cluster B 0.78 1 0.72 O.I2 0 50 0.69 Cluster c 0.66 0.72 1 0.14 0.18 0.47 Cluster D 0.26 0.12 0.14 1 0.15 0.03 Cluster E 0.20 0.50 0.18 0.15 I 0.47 Cluster F 0.64 0.69 0.47 0.03 0.47 1 303

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Edison Cluster A Id 1 T -ea ypes Most Agree Most Disagree II) II) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A, B, C, D, E, F) u (A, B, C, D, E, F) We want physical activity to be I can't trust other adults to get my a part of our children's lives (3.33, 3 .12, 2.57' 2.00, 1.40, z 2.75) kids to school. < ( -2.89, -2.50, -1.43, -1.50, -2.40, r.n c.. 0.62) Physical fitness is very There really aren't many safe important to our family. ::t (3.33, 3 00, 2.86, 1.00, -0.20, routes to our school. -( -2.44, -1.50 -2.14, 0.00, 0 60, z 1.50) u 2.62) Children can be safe on the street if they learn the right We worry about our kids skills. r.n (2.78, 0.75, 0.57, 1 00, 0.80, -z 0.25) 0 becoming obese ::t ( -2.22, -1.25, 0.29, 1.50, -2.40, -z 2.12) Colorado weather is ideal for We try to sleep in as late as walking and biking to school. UJ (2.78, 2.12, 2.00, 0.50, 1.80, u 0.75) r.n possible. ::t (-2.22, -0.38 -0.71, -3. 50, 0.80 z 2.62) Kids who are physically active There are strangers out there
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Edison Cluster B Id 1 T -ea ypes Most Agree Most Disagree II) II) "'0 Statement and Mean Scores 0 Statement and Mean Scores 0 u (A, B, C, D, E, F) u (A, B, C D, E, F) Kids who are physically active There are no busy roads in our < do better in school. 0 (2.22, 3.50, 2.71, 1.00, 0.20, z 1.38) c:::: neighborhood. r-( -1.44, -3.00, -2.43, -1.50, -3.60,u 1.50) We want physical activity to be I can't trust other adults to get my a part of our children's lives. (3 .33, 3.12, 2.57, 2.00, 1.40, z 2.75) kids to school. < ( -2.89, -2.50, -1.43, -1.50, -2.40, r.n c.. 0.62) Physical fitness is very There aren't many kids in our w important to our family. :r: (3.33, 3.00, 2.86, 1.00, -0.20 z 1.50) neighborhood. (-1.67, -2.38, -1.71, -0.50, -1.40,-r.n c.. 1.88) Driving to school is very convenient. U.l (0.00, 2.12, -0.71' -2.00, 0.60, u c.. 2.25) Colorado weather is ideal for walking and biking to school. U.l (2.78, 2.12, 2.00, 0.50 1.80, u 0.75) 305

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Edison Cluster C ld 1 T -ea .ypes Most Agree Most Disagree (1) (1) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A, B, C, D, E, F) u (A, B, C, D, E, F) Walking to school is a good way Air pollution is NOT a problem at c to save $$ on gas. w ( 1.44, 0.88, 3.43, -2.00, -1.00, our kids' school. t:l. ( -0.33, -0.62, -2.57, -0.50, -0.40, w u 0.38) u 0.63) Physical fitness is very There are no busy roads in our w important to our family. ::r:: (3.33, 3.00, 2.86, 1.00, -0.20, z 1.50) neighborhood. f-< (-1.44, -3.00, -2.43, -1.50, -3.60,-u 1.50) Kids who are physically active By third grade, kids can get to < do better in school. 0 (2.22, 3.50, 2.71, 1.00, 0.20, z 1.38) school okay on their own. < ( -0.11' -I. 75, -2.14, 0.50, -2.00,-z 2.25) We want physical activity to be There really aren't many safe a part of our children's lives. (3.33, 3.12, 2.57, 2.00, 1.40, z 2.75) routes to our school. ..... ( -2.44, -1.50, -2.14, 0.00, 0.60, w u 2.62) Colorado weather is ideal for Working parents can't take time to walking and biking to school. w (2. 78, 2.12, 2.00, 0.50, 1.80, u 0.75) walk to school. f-< (-0.78, -0.87, -2.00, 1.50, 2.80, t:l. 1.75) 306

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Edison Cluster D Id 1 T -ea .ypes Most Agree Most Disagree 0 Q) "'0 Statement and Mean Scores 0 "0 Statement and Mean Scores 0 u (A, B, C, D, E, F) u (A, B, C, D, E, F) Kids need protection from other Too many drivers disobey traffic :::,G kids and youth. 0 (-0.11, 0.00, -0.71, 3.50, 2.20,rules and signals. f(-0.78, 0.75, 0.71, -4.00, 2.20, u 0.50) u 2.00) If children yelled for help in our area, someone would protect We try to sleep in as late as them. u ( 1.33, -0.12, 0.43, 2.50, -0.80, r:/'1 c.. 0.87) r/) possible. ::r: (-2.22, -0.38, -0.71, -3.50, 0.80,-z 2.62) Kids may get into trouble if they Walking to school is a good way to go to school unsupervised. f(-1.11, -0.12, 0.86, 2.00, 1.60, z 1.00) 0 save money on gas. ( 1.44, 0.88, 3 .43, -2.00, -1.00, u 0.38) We want physical activity to be Families plan their schedules :2 a part of our children's lives. (3.33, 3.12, 2.57, 2.00, 1.40, z 2.75) around their trips to school. r/) (0.22, 0.62, -0.43, -2.00, -0.20, f-c.. 1.88) Children would be safe ifthey Parents save time by combining traveled in groups. 0 (0.67, -0.25, -0.14, 2.00, -0.80, r:/'1 school trips with errands. (0.67, 0.62, -0.57, -2.00, 1.00, c.. 0.75) c.. 1.50) Driving to school is very convenient. (0.00, 2.12, -0.71, -2.00, 0.60, u c.. 2.25) 307

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Edison Cluster E Id 1 T -ea .ypes Most Agree Most Disagree Q) Q) "'0 Statement and Mean Scores 0 ] Statement and Mean Scores u (A, B, C, D, E, F) U (A, B, C, D, E, F) Families are in too much of a There are no busy roads in our rush to walk to school. (0.33, 0.00, -1.29, 0.00, 2.80, f-Cl.. 1.75) neighborhood. f(-1.44, -3.00, -2.43, -1.50, -3.60,u 1.50) The location ofthe school Working parents can't take time influences where we choose to to walk to school. f( -0. 78, -0.87' -2.00, 1.50, 2.80, Cl.. 1.75) 0 live. 0 (0.22, -1.62, 1.29, 0.00, -2.60, -z 0.62) Too many drivers disobey I can't trust other adults to get my traffic rules and signals. f( -0. 78, 0. 75, 0. 71' -4.00, 2.20, kids to school. ( -2.89, -2.50, -1.43, -1.50, -2.40,u 2.00) Cl.. 0.62) There are strangers out there We worry about our kids Cl.. waiting to take your kids. a ( -2.22, -1.00, -0.86, 0.50, 2.20, -u 0.50) becoming obese. ( -2.22, -1.25, 0.29, 1.50, -2.40, -z 2.12) Kids need protection from other By third grade, kids can get to :::.G kids and youth. a (-0.11, 0.00, -0.71, 3.50, 2.20,u 0.50) school okay on their own. < ( -0.11' -1. 75, -2.14, 0.50, -2.00, ....... z 2.25) There are dangerous crossings u in our neighborhood. f-(-0.67, 1.62, 0.86, 1.50, 2.20,u 1.12) 308

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Edison Cluster F Id 1 T -ea ypes Most Agree Most Disagree Q) Q) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A, B, C, D, E, F) u (A, B, C, D, E, F) We want physical activity to be We try to sleep in as late as :2 a part of our children's lives. (3.33, 3.12, 2.57, 2.00, 1.40, z 2.75) en possible. ::r: (-2.22, -0.38, -0.71, -3. 50, 0.80,z 2.62) Driving to school is very There really aren't many safe convenient. routes to our school. Ul (0 00 2 .12, -0.71 -2 00 0 60, u 0.. 2.25) G3 ( -2.44 -1.50 -2.14 0.00, 0.60 -u 2.62) Walking to school is more By third grade, kids can get to enjoyable than driving. school okay on their own. (2.00, 1 75, 1.29, 0.00, 1.20, u 0.. 2.25) ...( ( -0.11' -1. 75, -2.14, 0.50 -2.00, z 2.25) Too many drivers disobey We worry about our kids Ul traffic rules and signals f( -0. 78, 0. 75, 0. 71' -4.00, 2.20, u 2.00) @ becoming obese. ( -2.22, -1.25 0.29, 1.50, -2.40, -z 2.12) 309

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Sabin Ideal Types Cluster A Cluster B Cluster C Cluster D N 10 10 12 5 PTR 0.50 -0.20 -0.17 1.20 PTS -0.50 -0.40 PTW 1.00 1.00 -0.75 1.60 PTM -0.90 -1.10 0.25 0.40 PCE 0.60 0.90 o.oo I / 2.60 PCI 0.00 0.80 1.00 PCP 1.20 1.70 0.42 .i.bo PCB 0.40 1.60 1.58 PSG 0.40 1.33 0.00 PSU -1.50 -1.60 -1.75 -1.00 PSC -0.40 -0.42 2.80 PSA 0.10 -1.70 -0.67 0.00 NIT 1.60 -0.40 -0.42 -1.60 NIF 0.30 1.30 0.42 -0.80 NIS -1.10 1.50 0.67 0.20 NIA -:-2.50 -1.30 -0.75 0.00 NHD -1.10 -0.10 -0.58 -2.20 NHA 1 70 -; :-Y'2 ',l o I : . ; ...... . ... NHE 1.10 1.10 .. 2.00 NHS : -3:00 NOD -0.80 -0.10 -0.50 -0.80 NOA -0.30 1.30 0.00 NOB -0.40 -0.70 -1.17 -1.60 NOS -1.80 -0.10 -0 08 -0.20 CEG 0.60 1.20 1.42 0.60 CEP -1.50 -0.30 -1.00 -1.20 CEI 0.10 -1.33 CEW -1.20 0.30 0.25 -1.60 CTC 1.80 0.40 -0.83 -1.60 CTD .... 3.10 1.30 1.17 0.80 CTR -2.00 -1.67 -2.00. 310

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CTE /2tJ6. 2 : :30 0.50 1.60 CDP . 2AO -0.10 0.58 0.40 CDC 0.00 -1.30 0.50 -0.40 CDS 0.00 0.10 1.08 -1.00 CDK 0.40 0.90 -0.42 -0.60 Sabin Cluster Correlations (Pearson's Coefficients) Cluster A Cluster B Cluster C Cluster D Cluster A 1.00 0.56 0.49 0.44 Cluster B 0.56 1.00 0.68 0.64 Cluster C 0.49 0.68 1.00 0.59 Cluster D 0.44 0.64 0.59 1.00 311

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Sabin Cluster A Id 1 T -ea ypes Most Agree Most Disagree (!) (!) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A, B, C, D) u (A, B, C, D) Cl Drivers are too distracted by their fphones and kids. u (3.10, 1.30 1.17, 0.80) r/) We try to sleep in as late as possible ::r:: z (-3.10, -3.40, -2.58, -3.00) IJ.l Too many drivers disobey traffic frules and signals. u (2.70, 2.30, 0.50, 1.60) There are no busy roads in our neighborhood. u (-2.60, -2.00, -1.67, -2.00) t:l.. There are strangers out there waiting Cl to take your kids. u (2.40, -0.1 0, 0.58, 0.40) By third grade, kids can get to school < okay on their own. z ( -2.50, -1.30, -0 75, 0.00) 312

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Sabin Cluster B Id I T -ea ypes Most Agree Most Disa_gree (I) (I) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A, B, C, D) u (A, B, C, D) Children would be safe ifthey c.:l traveled in groups. r/:J (0.40, 2.70, 1.33, 0.00) V) We try to sleep in as late as possible. ::t z (-3.10, -3.40, -2.58, -3.00) l:.t:l Too many drivers disobey traffic !rules and signals. u (2. 70, 2.30, 0.50, 1 60) There really aren't many safe routes to -our school. (.l.l u (0.1 0, -3.10, -1.33, -2.801 If children yelled for help in our area, u someone would protect them. r/:J 0.. ( -0.40, 2.10, -0.42, 2.80) cG There are no busy roads in our fneighborhood. u (-2.60, -2.00, -1.67, -2.00) We want physical activity to be a part of our children's lives. z (1.70, 2.10, 3.08, 1.80) Sabin Cluster C Id I T -ea .ypes Most Agree Most DisaEree (I) (I) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A,B,C,D) u (A, B, C, D) We want physical activity to be a part of our children's lives. z (1.70, 2.10, 3.08, 1.80) V) We try to sleep in as late as possible. ::t z (-3.10, -3.40, -2.58, -3.00) Walking to school is more enjoyable -than driving. u 0.. (0.00, 0.80, 2.58, 1.00) l:.t:l Physical fitness is very important to :I: our family. z ( 1.1 0, 1.1 0, 2.50, 2 00) < Kids who are physically active do 0 better in school. z (-0.30, 1.30, 2.08, 0.00) 313

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Sabin Cluster D ld 1 T -ea .ypes Most Agree Most Disagree Q) Q) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A,B,C,D) u (A, B, C, D) If children yelled for help in our area, u someone would protect them. r./1 ( -0.40, 2 1 0, -0.42, 2.80) r./1 We try to sleep in as late as possible. ::t z (-3.10, -3.40, -2.58, -3.00) t.Ll Driving to school is very convenient. u 0.. (0 60, 0.90, 0.00, 2.60) There really aren't many safe routes to -our school. t.Ll u (0.1 0, -3.1 0, -1.33, -2.80) Families plan their schedules around r./1 their trips to school. r-(-0.50, -0.40, 0.92, 2.40) 0 We worry about our kids becoming ::t obese. z ( -1.1 0, -0.1 0, -0 58, -2.20) Parents bond with their kids on the co trip to school. u 0.40, 1.60, 1.58, 2.40) There are no busy roads in our rneighborhood. u ( -2.60, -2.00, -1.67, -2.00) Parking near the school is difficult 0.. and frustrating u 0.. ( 1.20, I. 70, 0.42, 2.00) t.Ll Physical fitness is very important to ::c our family z (1.10, 1.10, 2.50, 2.00) 314

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Sl ld l T avens-ea lypes Cluster A Cluster B Cluster C N 14 13 9 PTR 0.21 -0.31 1.89 PTS 0.14 -0.38 1.89 PTW -0.57 -1.23 0.33 PTM 0.64 -0.15 0.22 PCE 0.57 -0.69 1.56 PCI 2.07 2.23 1.67 PCP -1.29 0.00 -0.22 PCB 0.86 1.77 2.44 PSG 0.79 0.31 -0.22 PSU .. 2.71 -3.08 -3.00 PSC 1.07 0.69 0.89 PSA -3.00 -2.46 -2.00 NIT -0.64 -0.62 -0.67 NIF 2.07 1.85 1.00 NIS 1.36 1.85 -0.22 NIA -0.86 0.08 -1.78 NHD -2.14 1.00 ...... -2.22 NHA 3.14 3.46 3.00 NHE 2.64 .. 3.00 1.56 NHS -1.93 -1.85 0.33 NOD 1.43 1.69 0.33 NOA 1.50 238 1.78 NOB -1.57 -1.62 -1.44 NOS 0.43 0.92 -0.22 CEG 0.36 1.54 0.44 CEP -0.50 -1.23 -1.11 CEI -1.43 -1.67 CEW 1.93 2;08 -0.33 CTC 1.93 0.85 1.11 CTD 0.86 1.69 -0.22 315

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CTR 1. ' -1.31 -1.67 CTE 1.07 1.15 0.00 CDP -1.00 -1.00 1.00 CDC -0.57 -1.00 -0.56 CDS -0.14 0.31 -1.11 CDK -0.57 -2.15 -0.67 Slavens Cluster Correlations (Pearson's Coefficients) Cluster A Cluster B Cluster C Cluster A 1.00 0.85 0.74 Cluster B 0.85 1.00 0.60 Cluster C 0.74 0.60 1.00 316

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Slavens Cluster A Id I T -ea ypes Most A_gree Most Disagree II) II) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A, B, C) u (A, B, C) We want physical activity to be a part of our children's lives. z (3.14, 3.46, 3.00) I can't trust other adults to get my < kids to school. C/) c.. (-3.00, -2.46, -2 00) w Physical fitness is very ::t important to our family z (2.64, 3.00, 1.561 There are no busy roads in our f-< neighborhood. u ( -2.93, -1.31' -1.67) Walking to school is more -enjoyable than driving. u There aren't many kids in our C/) neighborhood. c.. (2.07, 2.23, 1.671 c.. (-2.71, -3.08, -3.00) It's fun for kids to go to school (.J... with their friends. 0 We worry about our kids becoming ::r: obese. z (2.07, 1.85, 1.00) z ( -2.14, 1.00, -2.22) 317

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Slavens Cluster B Ideal Types Most Agree Most Disagree (1) (1) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A, B, C) u (A, B, CJ :2 We want physical activity to be a part of our children's lives. z (3.14, 3.46, 3.00) There aren't many kids in our :J neighborhood. rn Q.... (-2.71, -3.08, -3. 00) Physical fitness is very important to our family. z (2 64, 3.00, 1 56) < I can't trust other adults to get my rn kids to school. Q.... ( -3 .00, -2.46 -2 00) < Kids who are physically active 0 do better in school. z ( 1.50, 2.38, 1. 78) There really aren't many safe -routes to our school. u (-1.43, -2.23, -1.67) Walking to school is more -enjoyable than driving. u Q.... (2.07, 2.23, 1.67) Kids need protection from other 0 kids and youth. u (-0.57, -2.15, -0.67) :::: Colorado weather is ideal for walking and biking to school. u _(1.93, 2.08, -0.33) 318

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Slavens Cluster C ld 1 T -ea .ypes Most Agree Most Disagree II) II) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A, B, C) u (A,B,C) -< We want physical activity to be ::r: a part of our children's lives. z (3.14, 3.46, 3.00) There aren't many kids in our ::> neighborhood. C/') t:J,... (-2.71, -3.08, -3.00) Parents bond with their kids on a:l the trip to school. u t:J,... (0.86, 1.77, 2.44) 0 We worry about our kids ::r: becoming obese. z (-2.14, 1.00, -2.22) -< I can't trust other adults to get my C/) kids to school. t:J,... (-3.00, -2.46, -2.00) 319

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Cory Ideal Types Cluster A Cluster B Cluster C Cluster D N 8 3 10 7 PTR 1.50 1.00 -1.20 0 00 PTS -0.50 1.00 1.20 0.43 PTW -1.12 0.67 -1.50 -0.29 PTM 0.00 -0.33 0 20 0.43 PCE , ... 1.00 -0.20 -1.14 PCI .:/2:so -0.67 '3A3 PCP -0 .88 -1. 67 -0.20 -0.14 PCB 2.i 3 8 0 .00 .2.50< 2 .29 PSG 0.38 0.33 1.10 0 57 PSU .. : 2 12 0.00 -1.50 -3.00 PSC -0.25 1.00 0.70 0 86 PSA -1.12 -1.67 -2.43 NIT -0.13 1.33 -0.50 0.29 NIF 1.88 1.00 1.90 1.86 NIS 0.00 -0.67 1.70 0.57 NIA 0 30 -2 71 NHD -1.12 -1.40 -1.14 NHA . , 2 :86 NHE ':'' 3':29 NHS -1.00 -0.57 NOD 0 90 0 14 NOA 1.70 1_,:, : 2 .00 NOB -1.14 NOS 0 00 0 29 CEG 1.12 1.00 1.90 1.71 CEP -0 88 -1.67 -0.20 0 00 CEI -0.75 0.00 -1.71 2 .71 '<: 0.25 2 33': CEW CTC 1.75 0.33 -0.40 0.57 CTD 1.50 1.00 0 90 0 .71 CTR -3 ,33 ..:it4 CTE 0.75 1.00 -0.80 1.14 320

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CDP 1.38 -0.33 -1.50 -0.29 CDC -1.50 0.00 -1.80 -0.29 CDS 0.50 0.33 -0.10 0.86 CDK -0.25 -:-3;;3j: -1.50 0.14 Cory Cluster Correlations (Pearson's Coefficients) Cluster A Cluster B Cluster C Cluster D Cluster A 1 0.42 0.67 0.76 Cluster B 0.42 1 0.39 0.49 Cluster C 0.67 0.39 1 0.82 Cluster D 0.76 0.49 0.82 1 321

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c ory Cl uste r A Id IT ea ypes Most Agree Most Disagree Q) Q) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A,B,C,D) u (A, B, C, D) We want physical activity to be a part of our children's lives. z (3.62, 2.00, 2. 70, 2.86) There are no busy roads in our !neighborhood. u (-3.25, -3.33, -2.10, -2.14) Walking to school is more enjoyable ...... than driving. u Q.. (2.50, -0.67, 2.50, 3.43) rJ) We try to sleep in as late as possible ::r: z ( -2.38, 2.00, -1.00, -0.57) Parents bond with their kids on the a:l trip to school. u Q.. (2.38, 0.00, 2.50, 2.29) By third grade, kids can get to school < okay on their own. ...... z (-2.13, -2.00, 0.30, -2.71) < Kids who are physically active do 0 better in school. z (2.38, 1.00, 1.70, 2.00) There aren't many kids in our tZl neighborhood. Q.. (-2.12, 0.00, -1.50, -3.00) Physical fitness is very important to ::r: our family. z (2.25, 3.33, 2.50, 3.29) a:l Backpacks are too heavy for kids to 0 carry to school. z (-2.00, 1.67, -2.00, -1.14) Driving to school is very convenient. u Q.. (2.25, 1.00, -0.20, -1.14) 322

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c ory Cl uste r B Id IT -ea .ypes Most Agree Most Disagree Q) "'0 Statement and Mean Scores 0 Q) "'0 Statement and Mean Scores 0 u (A,B,C,D) u (A,B,C,D) Physical fitness is very important to :r: our family. z (2.25, 3.33, 2.50, 3.29) 0 We worry about our kids becoming :r: obese. z (-1.12, -3.33, -1.40, -1.14) Colorado weather is ideal for walking and biking to school. u (0.25, 2.33, 2.50, 2. 71) "' There are no busy roads in our f-. neighborhood. u (-3.25, -3.33, -2.10, -2.14) 22 We want physical activity to be a part of our children's lives. z (3.62, 2.00, 2.70, 2.86) Kids need protection from other kids 0 and youth. u (-0.25, -3.33, -1.50, 0.14) Kids will walk to school if there are rJJ We try to sleep in as late as possible. ::I: z ( -2.38, 2.00, -1.00, -0.57) V'J special events. 0 z (-0.62, -2.33, 0.00, 0.29) By third grade, kids can get to school < okay on their own. -z (-2.13, -2.00, 0.30, -2.71) 0 The location ofthe school influences 0 where we live. z (1.00, -2.00, 0.90, 0.14) 323

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C Cl t r C Id IT ory use -ea .y_Qes Most Agree Most Disagree j)) j)) "'0 Statement and Mean Scores 0 "0 Statement and Mean Scores 0 u (A,B,C,D) u (A ,B,C,D) We want physical activity to be a part of our children's lives. z (3.62, 2.00, 2.70, 2.86) I can't trust other adults to get my kids < to school. V1 (-1.12, -1.67, -2.70, -2.43) Walking to school is more enjoyable There really aren't many safe routes to than driving. u Q.. (2.50, -0.67, 2.50, 3.43) our school. I:J,) u (-0.75, 0.00, -2.50, -1.71) Parents bond with their kids on the a:l trip to school u (2.38, 0.00, 2.50, 2.29) cG There are no busy roads in our f-< neighborhood. u (-3.25, -3.33, -2.10, -2.14) Physical fitness is very important to our family. z (2.25, 3.33, 2.50, 3.29) a:l Backpacks are too heavy for kids to 0 carry to school. z (-2.00, 1.67, -2.00, -1.14) Colorado weather is ideal for walking I:J,) and biking to school. u (0.25, 2.33, 2.50, 2.71) 324

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C Cl t r D ld IT ory use -ea ypes Most Agree Most Disagree Cl) Cl) "'0 Statement and Mean Scores 0 -o Statement and Mean Scores 0 u {A, B, C, D) u (A,B,C,D) Walking to school is more enjoyable -than driving. u There aren't many kids in our :::::> neighborhood. r.n Q..., (2.50, -0.67, 2.50, 3.43) Q..., (-2.12, 0.00, -1.50, -3.00) Physical fitness is very important to ::r: our family z (2.25, 3.33, 2.50, 3.29) By third grade, kids can get to school < okay on their own. -z (-2.13, -2.00, 0.30, -2.71) We want physical activity to be a part of our children's lives z (3.62, 2.00, 2.70, 2.86) < I can't trust other adults to get my kids C/) to school. Q..., ( -1.12, -1.67' -2. 70, -2.43) Colorado weather is ideal for walking (.lJ and biking to school. u (0.25, 2.33, 2.50, 2.71) et::: There are no busy roads in our fneighborhood u (-3.25, -3.33 -2.10, -2.14) Parents bond with their kids on the t:C trip to school. u Q..., (2.38, 0.00, 2.50, 2.29) < Kids who are physically active do 0 better in school. z (2.38, 1.00, 1.70, 2.00) 325

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Bromwell Ideal T__JTges Cluster A Cluster B Cluster C Cluster D Cluster E N 7 5 3 4 6 PTR -0.57 -0.60 -1.00 -1.50 -0.17 PTS 0.57 -0.20 1.33 0.00 -1.00 -1.00 '' 1.50 PTW 1.00 -1.71 PTM 1.29 -1.00 0.00 -1.00 -0.50 PCE -0.57 -0.80 0.33 -1.25 -0.33 PCI 1.33 2.75 1.83 PCP 0.67 -0.75 1.33 PCB ..... . . 1.60 0.67 1.00 1.00 PSG -0.33 -0.25 0.67 PSU -1.33 -0.75 PSC 0.71 0.60 0.67 0.50 -1.00 PSA .'c.-... 2:40 -1.33 -0.50 __:_ r3.l7 NIT 0.29 -0.60 -1.00 0.25 -0.83 0.25 1.83 NIS -0.43 1.00 2.33 0.75 -0.17 NIA ., -0.40 -0.67 ------' ,.350 j_ NHD -1.29 -1.00 -1.33 _ .. -:-:: -2.00 ;,.2.17 NHA /.:.--' .:, : ;-.-0,<. --2.33 . .:e.r,)V2z.o!t: ... NHE 1.57 ,;-1.00 ?< :'"'': ., ;>:_-,-,'.;2 : : 17 NHS 0.00 I'/ '>, .:-Z.OO -0.75 -1.00 NOD _, 1.40 0.67 : ;: .. _,'. .. >: 32ZS 0.00 NoA t;E,::.. ::,::,/M{t,;r.;.z;Qo, 1.11 NoB -1.80 -0.67 -1.oo NOS 0.00 -0.20 0.00 -0.25 0.67 CEO 1.14 1.80 -1.00 1.00 0.17 CEP -0.29 -0.40 -0.67 -1.67 cEI IE'+,\. -1.oo ..:2.61 o.5o 1.67 CEW '.;'':>tJ12{$6: 1.40 1.33 -1.75 1.83 CTC 0.14 1.40 -0.67 '''''\(,.;' CTD 0 40 1 75 1 33 o. 71 ,; , . 326

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CTE 0.29 0.20 -0.33 1.83 CDP 1.75 -1.00 CDC 0.25 1.33 CDS -0.33 CDK r . -0.00 -I.OO -2.33 -0.25 -l.I7 Bromwell Cluster Correlations (Pearson's Coefficients 1 Cluster A Cluster B Cluster C Cluster D Cluster E Cluster A I 0.73 0.54 0.58 0 56 Cluster B 0.73 1 0 .53 0.48 0.68 Cluster C 0.54 0.53 I 0.39 0.34 Cluster D 0.58 0.48 0.39 I 0.56 Cluster E 0.56 0.68 0.34 0.56 I 327

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Bromwell Cluste A ld 1 T r -ea ypes Most Agree Most Disagree Q) Q) "'0 Statement and Mean Scores 0 "0 Statement and Mean Scores 0 u (A, B, C, D, E) u {A, B, C, D, E) Walking to school is more -enjoyable than driving. u 0... (3.43, 2.60, 1.33, 2.75, 1.83) c::: There are no busy roads in our 1neighborhood. u (-3.00, -1.40, -0.67, -3.50, -2.50) 0 The location of the school 0 influences where we live. z (2.86, 1.40, 0.67, 3.25, 0.00) There aren't many kids in our :;J neighborhood. VJ 0... (-2.86, -1.60, -1.33, -0.75, -2 67) Parents bond with their kids on a:l the trip to school. u < I can't trust other adults to get my VJ kids to school. 0... (2.86, 1.60, 0.67, 1.00, 1.00) 0... (-2.71, -2.40, -1.33, -0.50, -3 17) Colorado weather is ideal for I..Ll walking and biking to school. u (2.86, 1.40, 1.33,-1.75, 1.831 There really aren't many safe routes to our school. I..Ll u ( -2. 71, -1.00, -2.67' 0.50, 1.67) It's fun for kids to go to school By third grade, kids can get to .... with their friends -z (2.71, 3.00, -0.67, 0.25, -0.83) < school okay on their own. z (-2.14, -0.40, -0.67, -3.50, -2.17) < We want physical activity to be ::r: a part of our children's lives. z (2.43, 3.40, 2.33, 2.00, 2.67) a:l Backpacks are too heavy for kids 0 to carry to school. z (-2.00, -1.80, -0.67, -1.00, -2.00) < Kids who are physically active 0 do better in school. z (2.43, 3.00, 3.33, 2.00, 1.17) Children would be safe if they 0 traveled in groups. CJ) 0... (2.00, 1.20, -0.33, -0.25, 0.67) 328

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Bromwell Cluste B Id I T r -ea 2'.2_es Most Agree Most Disagree Q) Q) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u {A, B, C, D, E) u (A, B, C, D, E) We want physical activity to be a part of our children's lives. z _{2.43, 3.40, 2.33, 2.00, 2 67) < I can't trust other adults to get my r.n kids to school. 0.. _(-2.71, -2.40, -1.33, -0.50, -3.17) It's fun for kids to go to school '"'"' with their friends. z 2.71, 3.00, -0.67, 0.25, -0.83) r.n We try to sleep in as late as ::t possible. z (0.00, -2.40, -2.00, -0. 75, -1. 00) < Kids who are physically active 0 do better in school. z (2.43, 3.00, 3.33, 2.00, 1.17) 0.. There are strangers out there 0 waiting to take your kids u (0. 71' -2.40, 1.33, 1. 75, -1.00) Walking to school is more -enjoyable than driving. u 0.. (3.43, 2.60, 1.33, 2.75, 1.83) r.n People no longer know their 0 neighbors like they once did. u ( -0.29, -2.20, 0.67, 2.00, -0.33) Parking near the school is 0.. difficult and frustrating. u 0.. -0.14, 2.00, 0.67, -0.75, 1.33) 329

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Bromwell Cluste C ld 1 T r -ea .ypes Most Agree Most Disagree v v "0 Statement and Mean Scores 0 "0 Statement and Mean Scores 0 u (A, B, C, D, E) u (A, B, C, D, E) <: Kids who are physically active 0 do better in school. ;z (2.43, 3.00, 3.33, 2.00, 1.17) V'J We try to sleep in as late as :I: possible. ;z (0.00, -2.40, -2.00, -0. 75, -1.00) Children can be safe on the street if they learn the right rJJ skills. -;z (-0.43, 1.00, 2.33, 0.75, -0.17) Kids need protection from other 0 kids and youth u (0.00, -1.00, -2.33, -0.25, -1.17) We want physical activity to be a part of our children's lives. ;z (2.43, 3.40, 2.33, 2.00, 2.67) There really aren't many safe -routes to our school. IJ.l u (-2.71, -1.00, -2.67, 0.50, 1.67) 0 Drivers are too distracted by f-< their phones and kids. u (0.71, 0.40, 2.33, 1.75, 1.33) u If you leave a bike outside it will 0 get stolen or vandalized. u (-1.29, -1.40, -2.67, 0.25, 1.33) 330

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Bromwell Cluste D Id I T r ea ypes Most Agree Most Disagree Q) Q) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A, B, C, D, E) u (A, B, C, D, E) 0 The location ofthe school 0 influences where we live z 2.86, 1.40, 0.67' 3.25, 0.00} By third grade, kids can get to < school okay on their own. -z -2.14, -0.40, -0.67, -3.50, -2.17) Walking to school is more -enjoyable than driving. u Q.. (3.43, 2.60, 1.33, 2.75, 1.83) There are no busy roads in our fneighborhood. u (-3.00, -1.40, -0.67, -3.50, -2.50) Physical fitness is very ::r: important to our family. z 1.57, 3.80, 1.00, 2.75, 2.17) Working parents can't take time to fwalk to school. Q.. (-1.71, -1.00, 1.00, -2.00, 1.50) u There are dangerous crossings fin our neighborhood. u 0.14, 1.40, -0.67' 2.25, 3 .67) 0 We worry about our kids ::r: becoming obese. z (-1.29, -1.00, -1.33, -2.00,-2.171 We want physical activity to be a part of our children's lives. z (2.43, 3.40, 2.33, 2.00, 2.67) Q.. Air pollution is NOT a problem at w our kids' school. u -0.29, -0.40, -0.67, -2.00, -1.67) < Kids who are physically active 0 do better in school. z (2.43, 3.00, 3.33, 2.00, 1.17) Too many drivers disobey ftraffic rules and signals. u (0.29, 0.20, -0.33, 2.00, 1.83) C/) People no longer know their 0 neighbors like they once did. u (-0.29, -2.20, 0.67, 2.00, -0.33) 331

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Bromwell Cluste E Id l T r -ea .ypes Most Agree Most Disagree Q) Q) "0 Statement and Mean Scores 0 "0 Statement and Mean Scores 0 u (A, B, C, D, E) u (A,B,C,D,E) u There are dangerous crossings rin our neighborhood. u 0.14, 1.40, -0.67' 2.25, 3.67) I can't trust other adults to get my < kids to school. rF.l Q.. -2.71' -2.40, -1.33, -0.50, -3.17) < We want physical activity to be ::c a part of our children's lives. z (2.43, 3.40, 2.33, 2.00, 2.67) There aren't many kids in our neighborhood. C/} Q.. -2.86, -1.60, -1.33, -0. 75, -2.67) u.J Physical fitness is very ::c important to our family. z 1.57, 3.80, 1.00, 2.75, 2.17) There are no busy roads in our rneighborhood. u (-3.00, -1.40, -0.67, -3.50, -2.50) By third grade, kids can get to < school okay on their own. z -2 14, -0.40, -0.67, -3.50, -2.17) Q We worry about our kids ::c becoming obese. z (-1.29, -1.00, -1.33, -2.00, -2.17) t:C Backpacks are too heavy for kids 0 to carry to school. z (-2.00, -1.80, -0.67, -1.00, -2.00) 332

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11ps-ea PhT Id 1 T {pes Cluster A Cluster B N 18 15 PTR 1.00 -0.53 PTS 0.67 0 .33 PTW -0.67 1.47 PTM -0.50 0.33 PCE -0.06 0.60 PCI 0.39 -0.27 PCP 0.89 -0.07 PCB 1.83 2.00 PSG 1.61 0.93 PSU : .. : f 4:{7 .. 0.53 PSC 0.00 1.07 PSA -0.39 0.73 NIT 1.72 0.40 NIF 1.06 0.67 NIS -0.61 0.93 NIA -0.27 NHD -1.56 -0.53 NHA 1.94 1.93 NHE 1.22 1.93 NHS -1.44 -1.93 NOD -0.67 -1.40 NOA 0.06 -0.07 NOB -0.56 -0.87 NOS -0.67 0.13 CEG 1.61 1.20 CEP -1.06 -0.67 CEI -1.72 -1.53 CEW 0.22 -1.00 CTC 1.94 0.60 CTD 1.22 0 80 CTR '. ::3 soo -1.93 CTE 1.72 -0 27 CDP 2.39 0.80 CDC 0.56 0.53 CDS 1.44 -0.07 CDK 1.22 0.87 333

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Philips Cluster Correlations (Pearson's Coefficients) Cluster A Cluster B Cluster A 1.00 .584 Cluster B 1.00 PhT Cl ter A Id 1 T 1 lpS us -ea ypes Most Agree Most Disagree Cl) Cl) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A, B) u (A, B) There are strangers out there waiting 0 to take your kids. u (2.39 0.80) There are no busy roads in our fneighborhood. u (-3.00, -1.93) By third grade kids can get to school
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Philips Revised Ideal Types Cluster A Cluster B Cluster C Cluster D Cluster E Cluster F N 4 13 4 3 5 3 PTR 0.50 1.00 -1.75 1.00 -1.80 1.67 PTS 0.25 0.77 -0.50 2:67.< ,, .:,. 0.40 -1 PTW -1.75 -0.46 1.25 0.20 : \:'J.i 67 PTM -1.50 -0.23 -1.25 0.67 0.20 PCE 1.00 1.25 -1.00 -0.40 3 PCI 1.00 0.38 1.67 -1.00 1.33 PCP 1.75 0.54 -0.75 -1.33 1.20 0 PCB 0.75 2.31 3.00 1.60 1.33 PSG 3 .00 1.08 1.25 2.33 0.20 0.33 PSU -,;2.00 2 25 -1.33 0.40 0.33 PSC -1.00 0.38 0.50 2.00 0.60 1.67 PSA 1.50 -1.31 3.00 0.67 -0.80 0.33 ,,,_ ,-_-; NIT 1.38 0.00 -0.67 1.00 1 NIF 0.75 1.31 0.75 1.67 0.60 -0.33 NIS -0.75 NIA NHD NHA 0.50 NHE 0.50 NHS -0.25 NOD 1.00 -1.38 -1.75 -0.67 -1.40 -1.67 NOA -1.00 0.54 1.25 0.00 -1.20 0 NOB -0.50 -0.46 0.00 -1.33 0.33 ... ,i., .. NOS -0.25 ,.0.77 1.50 1.33 -0.40 -2 CEG 1.00 1.85 CEP -0.25 -1.38 CEI -1.69 CEW -1.25 0.69 -0.75 0.67 1>. ;.2.oo -1.33 CTC 0.75 0. 75 -1.00 ,1 -1.67 CTD 0.00 1.69 t -1.67 -0.80 3 CTR -0.75 335

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-0.25 0.40 0.33 1.75 1.67 0.40 -0 67 CDC 1.00 0.31 1.25 0.33 -0.40 1.33 CDS 1.00 1.54 . .;.;:too -1.00 1.20 1.33 .... ,' CDK 1.50 1.15 0.75 1.33 -0.20 Philips Revised-Cluster Correlations (Pearson's Coefficients) Cluster Cluster Cluster Cluster Cluster Cluster A B c D E F Cluster 1.00 0.58 0.14 0.25 0.22 0.18 A Cluster 0.58 1.00 0.29 0.23 0.54 0.49 B Cluster 0.14 0.29 1.00 0.15 0.22 0.27 c Cluster 0.25 0.23 0.15 1.00 0.19 0.23 D Cluster 0.22 0.54 0.22 0.19 1.00 0.23 E Cluster 0.18 0.49 0.27 0.23 0.23 1.00 F 336

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Ph T R d Clust A Id 1 T 11ps ev1se er -ea .ypes Most Agree Most Disagree Q) Q) "0 Statement and Mean Scores 0 "0 Statement and Mean Scores 0 u (A, B, C, D, E, F) u (A, B, C, D, E, F) Children would be safe ifthey We worry about our kids c traveled in groups. Vl (3.00, 1.08, 1.25, 2.33, 0.20, c.. 0.33) @ becoming obese. (-3.50, -0. 77, -0.50, -3.00, 2.20,z 2.67) There are strangers out there Driving to school is very c.. waiting to take your kids 0 (3.00, 2.15, 1.75, 1.67, 0.40,u 0.67) convenient. IJ.l ( -3.00, 1.00, 1.25, -1.00, -0.40, u c.. 3.00) Kids may get into trouble if they There are no busy roads in our go to school unsupervised. 1--(2.25, 1.38, 0.00, -0.67, 1.00, -z 1.00) neighborhood. 1--(-2.75, -3.00, -0.75, -1.67, -2.60,u 2.67) Too many drivers disobey traffic By third grade, kids can get to IJ.l rules and signals. 1---(2.00, 1.77, -0.25, -2.00, 0.40, u 0.33) school okay on their own. < (-2.25, -2.69, -2.00, 2.33, -0.20,-z 0.67) There aren't many kids in our ::J neighborhood Vl (-2.00, -2.15, 2.25, -1.33, 0.40, c.. 0.33) There really aren't many safe routes to our school. (-2.00, -1.69, -0.25, -2.33, -2.20, IJ.l u 1.33) 337

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Ph T R d Clust B Id 1 T 11ps ev1se er -ea .ypes Most Agree Most Disagree Q) Q) "0 Statement and Mean Scores 0 "0 Statement and Mean Scores 0 u {A, B, C, D, E, F) u {A, B, C, D, E, F) We want physical activity to be a There are no busy roads in our part of our children's lives. (0.50, 2.54, 1.25, 3.00, 1.60, z 2.33) neighborhood. f(-2.75, -3.00, -0.75, -1.67, -2.60,-u 2.67) There are dangerous crossings in By third grade, kids can get to u our neighborhood. f-(0. 75, 2.54, 0. 75, -1.00, 2.80, -u 1.67) school okay on their own. < (-2.25, -2.69, -2.00, 2.33, -0.20,-...... z 0.67) Parents bond with their kids on There aren't many kids in our co the trip to school. u (0.75, 2.31, 2.25, 3.00, 1.60, neighborhood. Vi ( -2.00, -2.15, 2.25, -1.33, 0.40, c.. 1.33) c.. 0.33) There are strangers out there c.. waiting to take your kids. 0 (3.00, 2.15, 1.75, 1.67, 0.40,u 0.67) 338

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Ph T R d Clust C Id 1 T 11ps ev1se er -ea .ypes Most Agree Most Disagree G) G) Statement and Mean Scores 0 "0 Statement and Mean Scores 0 u (A, B, C, D, E, F) u (A, B, C, D E F) I can't trust other adults to get Walking to school is more
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PhT R d Clust D ld 1 T 11ps evtse er -ea ypes Most Agree Most Disagree C1) C1) "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A, B, C, D, E, F) u (A, B, C, D, E, F) We want physical activity to be a We worry about our kids part of our children's lives. (0.50, 2.54, 1.25, 3.00, 1.60, z 2.33) Cl becoming obese. ::r: (-3.50, -0.77, -0.50, -3.00, 2.20,z 2.67) Parents bond with their kids on There really aren't many safe co the trip to school. u (0. 75, 2.31' 2.25, 3.00, 1.60, c.. 1.33) routes to our school. -( -2.00, -1.69, -0.25, -2.33, -2.20, U..l u 1.33) Families plan their schedules We try to sleep in as late as around their trips to school. tJ) (0.25, 0.77, -0.50, 2.67, 0.40,-f-c.. 1.00) Cl) possible. :I: ( -0.25, -1.69, -2.00, -2.00, -2.60, -z 0.67) Working parents can't take time Too many drivers disobey traffic to walk to school. f(-1.75, -0.46, 1.25, 2.67, 0.20, c.. 2.67) U..l rules and signals. f-(2.00, 1.77, -0.25, -2.00, 0.40, u 0.33) By third grade, kids can get to school okay on their own. < (-2.25, -2.69, -2.00, 2.33, -0.20,-z 0.67) Children would be safe ifthey traveled in groups. Cj (3.00, 1.08, 1.25, 2.33, 0.20, tJ) c.. 0.33) If children yelled for help in our area, someone would protect them. u ( -1.00, 0.38, 0.50, 2.00, 0.60, tJ) c.. 1.67) Children can be safe on the street if they learn the right skills. tJ) (-0.75, -0.69, -0.75, 2.00, 2.40,-z 0.33) Air pollution is NOT a problem at our kids' school. c.. (-0.25, -1.38, 0.50, 2.00, -2.20,-U..l u 2.33) 340

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Ph T R d Clu t E ld 1 T 1 Ips evtse s er -ea :_xpes Most Agree Most Disagree "0 Statement and Mean Scores 0 "0 Statement and Mean Scores 0 u (A, B, C, D, E, F) u (A, B, C, D, E, F) There are dangerous crossings in We try to sleep in as late as u our neighborhood. f-(0. 75, 2.54, 0. 75, -1.00, 2.80, -u 1.67) r/) possible. :I: ( -0.25, -1.69, -2.00, -2.00, -2.60, z 0.67) Physical fitness is very There are no busy roads in our important to our family. (0.50, 1.54, 2.25, 0.33, 2.60, z 2.00) neighborhood. (-2.75, -3.00, -0.75, -1.67, -2.60,-u 2.67) Children can be safe on the street if they learn the right Air pollution is NOT a problem at skills. our kids' school. r/) ( -0. 75, -0.69, -0. 75, 2 00, 2.40, -z 0.33) Q., (-0.25, -1.38, 0.50, 2.00, -2.20,u 2.33) We worry about our kids There really aren't many safe @ becoming obese. (-3.50, -0.77, -0.50, -3.00, 2.20, z -2.67) routes to our school. -( -2.00, -1.69, -0.25, -2.33, -2.20, tJ.l u 1.33) Walking to school is a good way Colorado weather is ideal for 0 to save$$ on gas. tJ.l (1.00, 1.85, -0.25, 1.00, 2.20, :=:: walking and biking to school. tJ.l ( -1.25, 0.69, -0. 75, 0.67, -2.00, -u 1.67) u 1.33) a:l Backpacks are too heavy for kids 0 to carry to school. z (-0.50, -0.46, 0.00, -1.33, -2.00) 341

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Ph T R d Clu t F Id 1 T 1 Ips ev1se s er -ea .ypes Most Agree Most Disagree Q) Q) "'0 Statement and Mean Scores 0 -g Statement and Mean Scores u (A, B, C, D, E, F) u (A, B, C, D, E F) Drivers are too distracted with Driving to school is very Q phones, kids and other things. f-(0.00, 1.69, 3.00 -1.67, -0.80, u 3.00) convenient. ,l.l (-3.00, 1.00, 1.25, -1.00, -0.40, u c.. 3.00) Working parents can't take time We worry about our kids becoming to walk to school. f(-1.75, -0.46, 1.25, 2.67, 0.20, c.. 2.67) @ obese (-3.50, -0.77, -0 50, -3.00, 2.20,-z 2.67) Parents save time by combining Air pollution is NOT a problem at :; school trips with errands. f( -1.50 -0.23 -1.25, 0.67, 0.20, c.. 2.33) our kids' school. c.. ( -0 25, -1.38, 0 50, 2 00 -2.20 ,l.l u 2.33) We want physical activity to be There are no busy roads in our a part of our children's lives. (0.50, 2.54, 1.25 3.00, 1.60, z 2.33)_ tx: neighborhood f(-2.75 3.00, -0.75, -1.67, -2.60,u 2.67) Kids need protection from other ::.G kids and youth. Q (1.50, 1.15, 0.75, 1.33, -0.20 u 2.33) Physical fitness is very l:l.l important to our family. ::r: (0. 50, 1.54, 2.25, 0.33, 2.60, z 2.00) 342

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Valdez Ideal Types Cluster A Cluster B Cluster C N 5 5 12 PTR : 0. 4 2 PTS 1.00 0.33 PTW -1.60 0.60 0.17 PTM -1.00 1.60 -0.33 PCE -1.80 -1.40 0.42 PCI 0 00 .. 1.00 PCP -1.00 1 -0.42 PCB 0 60 1.00 PSG 0.80 "'f \ : 2 : 00 0.42 PSU -0.40 -0.40 -1.75 PSC 0.60 0.60 0.08 PSA 0.60 -1.33 NIT 0.80 -1.40 1.00 NIF -0.80 0.60 1.75 NIS -1.20 0.20 0.50 NIA 0.20 <,. , ;;-250 NHD 0.00 -0.20 0.17 NHA NHE 0.20 1.60 1.92 NHS NOD 0.80 1.80 -1.83 NOA NOB 0.80 -0.60 0.75 NOS CEG 0.80 0.20 1.33 CEP -1.20 -1.40 -0.75 CEI -0.40 0.00 CEW -1.00 1.00 0.17 CTC 0.20 1.40 1.42 CTD -0.20 1.50 CTR 0.20 CTE -0.20 1.42 343

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CDP -0.20 0.92 CDC -0.60 0.20 0.92 CDS -0.20 -0.50 CDK -0.80 0.17 Valdez Cluster Correlations (Pearson's Cluster A Cluster B Cluster C Cluster A 1.00 0.15 0.22 Cluster B 1.00 0.56 Cluster C 1.00 344

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Valdez Cluster A Id 1 T -ea _yiJ_es Most Agree Most Disagree Q) "'0 Statement and Mean Scores 0 Q) "'0 Statement and Mean Scores 0 u (A, B, C) u (A, B, C) tJ.l Too many drivers disobey traffic frules and signals. u (3.80, -0.20, 1.42) Families plan their schedules around V) their trips to school. f-(-3.00, 1.00, 0.331 0 Drivers are too distracted by their fphones and kids. u _(2.80, -0.20, 1.50} Families are in too much of a rush to walk to school. f-(-2.80, -2.40, 0.42) There are strangers out there waiting 0 to take your kids. u (2.80, -0 20, 0.92) There really aren't many safe routes to our school. w u (-2.20, -0.40, 0.00) ::.G Kids need protection from other kids 0 and youth. u (2.80, -0.80, 0.17) r:/) People no longer know their 0 neighbors like they once did. u (2.60, -0.20, -0.50) 345

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Valdez Cluster B ld 1 T -ea .ypes Most Agree Most Disagree Q) Q) "'C Statement and Mean Scores 0 "0 Statement and Mean Scores 0 u (A, B, C) u (A, B, C) Walking to school is more Parking near the school is difficult -enjoyable than driving. u and frustrating. u (0.00, 2.80, 1.00) (-1.00, -2.60, -0.42) Parents bond with their kids on co the trip to school. u < I can't trust other adults to get my 00 kids to school. (0.60, 2.60, 1.00) (0.60, -2.60, -1.33) We want physical activity to be a part of our children's lives. z (1.80, 2.60, 3.00) Families are in too much of a rush IX to walk to school. f-(-2.80, -2.40, 0.42) c Children would be safe ifthey 00 traveled in groups. (0.80, 2.00, 0.42) 00 We try to sleep in as late as ::t possible. z (-0.40, -2.20, -2.92) < Kids who are physically active 0 do better in school. z (1.20, 2.00, 1.42) By third grade, kids can get to < school okay on their own. z (0.20, -2.00, -2.50) Kids will walk to school if there 00 are special events. 0 z (-0.40, -2.00, -1.75) 346

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Valdez Cluster C ld 1 T -ea ypes Most Agree Most Disagree cu cu "'0 Statement and Mean Scores 0 "'0 Statement and Mean Scores 0 u (A, B, C) u (A, B, C) :2 We want physical activity to be a part of our children's lives. z (1.80, 2.60, 3.00) rJ) We try to sleep in as late as = possible z (-0.40, -2.20, -2.92) There are no busy roads in our fneighborhood. u (0.20, -1.60, -2.58) By third grade, kids can get to -< school okay on their own. z (0.20, -2.00, -2.50) 347

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APPENDIXH PILOT STUDY BEST SPECIMENS ClusterA ClusterB ClusterC ClusterD ClusterE S113 .451 .376 .694 .422 .373 S227 S065 .499 .357 .5o3 .653 .. W9l.: S106 S219 .. ::i?:3.1' .487 .593 )\: Q,l oo ____ .4_1_0,_ ____ .24_5-r ____ _____ s 136 .683 .459 .394 .339 .421 s 148 .570 .311 .432 .594 ..... . 78J S299 .633 .564 .314 .677 ]14}: s 129 .499 .288 .388 L544 .380 .240 .418 :'.'.!i-'/19$ L073 .646 .367 .424 .417 L521 .3 72 029 .280 .508 L393 .582 .554 .430 .520 o L375 .442 .404 . . 816 .484 ....J L419 . : .520 .405 .483 L046 .483 .352 .389 .579 L341 .605 .590 .494 .542 L163 .545 .565 .567 .476 .538 .549 .636 .467 r Ws5 .568 .616 .. M495 .643 .496 M372 M280 Q,l M013 e M19o = = M277 M152 M279 M418 M186 .084 .063 .165 J9o f .-: .364 .543 .563 .530 .433 .292 Y i;{i":' > ' ('J62: 545 .475 .509 573 687 .394 .412 .441 : .. 7:3$ 635 .665 .474 348 -.089 .037 .264 .392 .417 .364 ,507 . .,!}!32'11: .347 .440 .267 .271 .201 .421 .447 .387 .543

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F378 .569 .344 141 1 15 .252 F578 .332 .376 .112 .319 .392 F580 .214 .292 .252 .183 .132 F397 .579 .595 .459 ... :;;;713\ ;.809 F516 .467 [ / :.i .320 .240 .373 '"' = F385 .560 .396 .168 .217 .310 F236 .493 \ > :,;, ... ::-F .324 .358 .352 F155 .509 1' ..... .373 .338 .598 F099 . ... : .543 .671 .505 .670 F422 .460 .496 .391 .321 .. 349

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APPENDIX I MAIN STUDY BEST SPECIMENS Edison 39 Case Best Specimens (Pearson's Coefficients) Cluster Cluster Cluster Cluster Cluster Cluster Case# A B c D E F 260006 I ;797 .580 .454 .076 .096 .482 260022 .484 .576 .404 .284 .407 260028 .010 236 .104 .178 ; .741 .330 260036 .236 .478 .265 -.174 I . .774 .505 260042 .510 .498 .480 -.020 .187 .607 260053 .468 .565 / .7(>'7. -.135 -.008 .321 260056 .224 .174 .156 -.160 .094 .627 260069 .435 .316 .469 -.079 .124 .647 0 .844 ' 260081 r : o o :,.:.JS5 .547 .215 .314 .617 260113 .631 568 .068 -.150 .366 ,, 260127 .673 .. ;,;.i.;; 848 ; .537 .147 .544 .671 260145 .672 '/\;'844 ,f: ),, >' .' .536 .028 .430 .544 260149 .275 .363 .655 .175 .163 .161 260151 .502 ;ic:&er nn : .600 .165 .326 .443 260157 .591 .504 : ,/704' -.127 .094 .402 260160 .357 .615 .316 .020 .495 .666 260200 .187 .412 .346 .366 .643 .287 260212 .433 .584 .692 .147 .381 .421 260243 .290 .428 .041 -.159 .458 o }j32 260247 .539 .607 .473 -.052 .151 .376 260287 -.043 .282 -.072 .064 1 ... . :.'.819 0 .209 260290 .586 .664 .410 .288 .539 c' 783 -. 260298 .366 .427 .018 .124 .373 260399 .497 .642 .406 .040 .384 .299 260404 .576 <\\)}'714; .611 .064 .287 .601 260411 ii;:t 0 c:, 762 '0.
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260450 ::..., .684 .559 -.028 .189 .618 260452 .352 .365 .545 .020 077 .211 260694 529 ,;787 .647 .283 .379 .517 260697 I :so6. .638 .502 .350 .148 .558 260714 .683 .500 .644 .315 142 .323 260731 .433 .375 .342 .203 .293 .536 260776 .116 -.001 .027 < c' :7.85 .148 -.107 260802 .540 .597 .247 .076 .314 260805 .290 .192 .184 ... .18S' 080 .157 260848 .589 .651 .421 .036 .407 .560 260876 ,_>,':( ;740. .462 .372 .374 .037 .343 351

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Case 210009 210028 210038 210042 210066 210093 210098 210128 210136 210137 210191 210192 210193 210200 210205 210206 210224 210225 210227 210249 210273 210283 210284 210303 210315 210368 210455 210463 210480 210488 210493 210532 210537 210544 210684 210685 210745 Sabin 37 Case-Best Specimens (Pearson's Coefficients) Cluster A Cluster B Cluster C Cluster D .429 .505 .24 7 .413 .379 .264 .299 .354 .573 .350 .335 .344 .595 .262 -.143 .240 .096 < . .:;,6()7 .330 .574 .464 :832 .250 .355 .232 .463 .543 .335 .149 309 .469 .. .402 .232 .467 .092 .259 .588 .515 .306 .. .442 .415 .457 .238 .295 .483 .372 .173 .047 .326 .512 .249 .321 ,62$ .310 .495 .297 .269 .321 .080 .385 .574 .293 :)'_,.:646 .323 .320 .298 .245 .560 . .317 .384 .458 .456 .482 .315 .206 .167 .469 .510 317 .485 .374 .344 478 .416 .232 .360 .511 .596 .695:: .545 .368 .442 .578 .494 .521 .394 Wtio' .501 .085 .227 .456 .259 352

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Slavens 36 Case Best Specimens (Pearson's Coefficients) Case # Cluster A Cluster B Cluster C 220007 .546 .403 .258 22ooo8 .672 . .554 .285 .579 .457 220075 .642 .'. ir:\Y:, .176 .451 220078 .518 .455 .634 220079 .608 .462 .639 .323 .549 220088 .487 .445 220122 .601 .538 ' ;';778 220124 .575 .654 .372 220133 : > .794 <<, ; .705 .490 220137 ;. 844 . ''' .824 .598 220138 .599 .683 .395 220143 .559 .398 .454 220150 ., .. '.722 .516 .685 220166 .376 .180 .573 .607 220178 .337 .289 .582 220179 .681 .471 .351 220182 .599 .411 .383 220210 .322 .217 .501 .546 220238 .648 .583 .392 220246 .497 .619 .334 .594 220250 .508 .522 .658 220265 .511 .613 .283 220269 I , : 737 629 .588 220273 .595 .381 .681 220274 .662 ' .708 .595 353

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Case# 240001 240003 240020 240027 240031 240053 240066 240079 240092 240099 240127 240132 240147 240170 240179 240183 240192 240193 240361 240411 240431 240433 240444 240459 240465 240472 240473 240487 Cory 28 Case Best Specimens (Pearson's Coefficients) Cluster Cluster Cluster Cluster Cluster A B C D E .465 .449 .645 ' .449 .357 .265 .646 .466 .265 .643 .605 ;716 .655 .605 .419 .154 :782 .562 .154 .449 ... ;768 .283 .339 .768 /779 .340 .425 .470 .340 .473 .264 ',.':', /702 .690 .264 .650 .632 .650 .632 .608 .402 CYS':t78 .782. .402 .277 . 8lQ .360 .406 ':. .810 ;866 .359 .685 .132 .359 .313 .284 !t"a88 .581 .284 .620 .208 ,, .813 .690 .208 .527 .325 .562 .432 .325 .463 -.009 _:_c( :18} .553 -.009 .241 .135 .256 .377 ' : :135. .398 .270 .}::::156 .597 .270 .639 .370 .609 )7QJ. .370 ' .716 .416 .607 '}7$9 .416 .. 772 .336 .560 .692 .336 .687 278 .443 .482 .278 .764 .446 .494 .522 .446 .496 208 .515 .680 .208 .473 .145 .481 .682 .145 .362 .214 ::761r .238 .548 .591 .238 354

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Case# 230001 230017 230026 230030 Bromwell 25 Case Best Specimens (Pearson's Coefficients) Cluster Cluster Cluster Cluster Cluster A B C D E .243 .472 .078 .286 ;712 < i .... ...... i <710.; ; 884 ; .537 .455 .577 .627 .859 .552 .284 .581 .266 .261 .267 .397 .638 230037 ' '.:871 .681 .558 .486 .554 230045 . 818. .696 .507 .547 .663 230065 .651 ,826 .408 .422 .585 230096 .629 .453 .461 'i7$4.: .314 230099 .557 .458 .442 .81, 3 .531 230101 .528 .609 .294 .418 .737 230123 .468 .322 .669 .233 .347 230128 .454 .563 .279 .455 : 764 230132 .581 .876 .322 .432 .647 230148 .269 .314 .114 .798 .538 230157 : .>l81J .463 .383 .474 .365 230166 'i'8ZJ .676 .421 .514 .475 230167 ' .652 .614 .502 .453 230169 .427 .321 .255 .432 230174 .673 479 .347 .383 216 230179 .312 .366 .755 .372 .294 230182 .472 .516 .377 .535 < t/78' 230183 _:c; .511 .245 .383 .472 230187 454 I >7/01' .360 .395 .420 '' .' . ... _' 230193 .322 .382 .613 .195 .042 230199 .595 .609 .255 .446 i' 355

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Philips 32 Case Best Specimens (Pearson's Coefficients) Cluster Cluster Cluster Cluster Cluster Cluster Case# A B c D E F 270001 .400 I':E:): ,.:7,93 .279 .160 .510 .364 270021 ;'':':, ;82 .391 .181 .047 .081 -.058 270029 .271 .546 .119 .088 .: \ 790 .119 270030 .284 .314 .236 .262 .674 .247 270046 .469 .742 .229 .125 .474 .335 270050 .249 .326 .180 :795 .030 .356 270059 .020 .193 .321 .021 .186 .703 270060 .498 .602 .035 .106 .093 .206 270074 .408 :;J, .'.707 .124 .468 .409 .440 270091 .041 .048 .108 .803 .214 .071 270095 .237 .524 .177 .078 .421 .334 270204 I .558 .225 .151 .246 .200 270209 .082 .456 .342 .057 'i. .740 .269 270241 .142 .408 .183 .187 .291 270249 .284 .637 .092 .397 .653 .250 270253 .302 .158 .076 :774 .207 .119 270259 .;';;.1Ql .404 -.124 .252 .156 .106 270262 I .. '< ..747.; .424 .144 .317 .175 .288 270277 .202 .452 .073 .049 .. . 737 .152 270290 .162 .137 .674 -.147 .041 .182 270292 .294 .665 .410 .104 .260 369 270307 .125 .486 .129 .203 .245 ': : .,8.03' 270309 .541 :iJl'1.:':: : Q4. .119 .148 .439 .236 270339 .483 .698 .183 -.128 .235 .431 270343 .155 .577 .229 -.174 .160 .214 270344 .202 .207 .575 .226 .309 .212 270356 .241 .379 .151 .283 .068 .684 270365 .522 .. ; '.<719 .397 .243 .182 .312 270368 .304 .673 .140 .223 .530 .497 270373 .396 .622 .000 .200 .332 .271 270394 -.139 .016 <7.Q4 .171 .063 .035 270900 -.073 .146 .016 .213 .621 .038 356

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Valdez 22 Case Best Specimens (Pearson's Coefficients) Cluster Cluster Cluster Case# A B c 250037 .644 .125 .139 250041 .234 .376 .671 250063 .150 .782 .468 250078 .162 .183 .631 250080 .811 .014 .020 250110 .184 .398 .560 250114 .083 .307 .. ,.UA 250136 .062 .470 .549 250165 .676 .184 .328 250176 .084 .497 .7()4 250200 .266 .178 .491 250209 .015 .720 .253 250223 .291 .384 .7_76 250227 -.091 .274 .535 250244 .187 .499 .641 250264 .811 .014 .020 250271 .334 .739 .438 250298 .164 .226 .441 250532 .642 .190 .267 250537 -.028 .320 .585 250573 .108 .458 250584 -.054 .659 .415 357

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