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Predictors of high school dropout among students with individual education plans

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Predictors of high school dropout among students with individual education plans
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Haaland, Kaite Johanna ( author )
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English
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High school dropouts ( lcsh )
Dropouts ( lcsh )
Emotional problems of children ( lcsh )
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theses ( marcgt )
non-fiction ( marcgt )

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Students with disabilities have a disproportionately higher rate of dropout compared to typical peers. This study was intended to investigate the predictive factors of dropout for youth with disabilities. Specifically, this study examines how disability classification and demographics impact dropout rates for youth with disabilities. Participants in this study were all students in a large urban school district who were identified with a disability (as defined as having a IEP) in grades 9-12 (N=1439). As most variables were categorical, nonparametric analyses were used, including cross-tabulations and contingency table analyses. In addition, a binary logistic regression model was used to isolate the variables associated with dropout. Findings suggest that youth with serious emotional disability (SED) have a high risk of dropout compared to students who fall under other disability categories. Additionally, students who did not qualify for free and reduced lunch and students who were never retained (repeated a grade) were more at risk for dropout. Practical implications and future directions are discussed.
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Thesis (D.Ed.)--University of Colorado Denver
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Includes bibliographical references.
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by Kaite Johanna Haaland.

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999609197 ( OCLC )
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Full Text
PREDICTORS OF HIGH SCHOOL DROPOUT AMONG STUDENTS WITH
INDIVIDUAL EDUCATION PLANS by
KAITE JOHANNA HAALAND B.S., Lesley University, 2008
A thesis submitted to the Faculty of the Graduate School of the University of Colorado Denver in partial fulfillment of the requirements for the degree of Doctor of Psychology School Psychology Program
2017


This thesis for the Doctor of Psychology degree by
Kaite Johanna Haaland
has been approved for the
School Psychology Program by
Franci Crepeau-Hobson, Chair Bryn Harris Colette Hohnbaum
Date: May 13, 2017
11


Haaland, Kaite Johanna (PsyD, School Psychology Program)
Predictors of High School Dropout Among Students With Individual Education Plans thesis directed by Associate Professor Franci Crepeau-Hobson
ABSTRACT
Students with disabilities have a disproportionately higher rate of dropout compared to typical peers. This study was intended to investigate the predictive factors of dropout for youth with disabilities. Specifically, this study examines how disability classification and demographics impact dropout rates for youth with disabilities. Participants in this study were all students in a large urban school district who were identified with a disability (as defined as having a IEP) in grades 9-12 (N=1439). As most variables were categorical, nonparametric analyses were used, including cross-tabulations and contingency table analyses. In addition, a binary logistic regression model was used to isolate the variables associated with dropout. Findings suggest that youth with serious emotional disability (SED) have a high risk of dropout compared to students who fall under other disability categories. Additionally, students who did not qualify for free and reduced lunch and students who were never retained (repeated a grade) were more at risk for dropout. Practical implications and future directions are discussed.
The form and content of this abstract are approved. I recommend its publication.
m
Approved: Franci Crepeau-Hobson


DEDICATION
I dedicate this capstone to my family. Without the unwavering support and encouragement of my partner Eric I could not have completed this work. I would also like to thank my two wonderful children Liam and Emmett, who have grown up with a mother who was always at school. I hope my hard work will inspire you one day to follow your dreams, whatever they may be. This work is also dedicated to my father George Humphrey who led by example and encouraged me to follow my dreams. Dad, you have supported me for many years so I could attain this; thank you.
Lastly, a very special thanks to my mother Sytske Humphrey who would not let me disappear into the world without graduating High School. Without her dedication and belief in my ability to not only succeed but also positively impact the world around me, I would have remained a high school dropout. I am here because you were an innovative educator who fought for and believed in me. Thank you!
IV


TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION.................................................1
Problem Statement............................................2
Research Questions...........................................3
II. LITERATURE REVIEW............................................4
Risk Factors.................................................5
Outcomes.....................................................6
III. METHOD.......................................................7
Participants.................................................7
Procedures...................................................8
Measures.....................................................9
Analysis....................................................10
IV. RESULTS.....................................................11
Demographics................................................11
Research Question 1.........................................12
Research Question 2.........................................13
V. DISCUSSON ..................................................17
Limitations.................................................19
Conclusion..................................................20
REFERENCES........................................................21
v


CHAPTER I
INTRODUCTION
High school dropout has been recognized as a social problem in the United States since the second half of the 20th century. Originally dropout was a benign term used to denote students who had not completed high school, but by the early 1960s it became a depreciatory term used to describe degenerate youth (Dorm, 1996). Alterations in societal expectations of high school completion likely changed due to shifts in the workforce demands of the country and a link between education and the economy (Gonzalez, Kennedy, & Julien, 2009): as the United States moved from an industrialized nation to a more technological one, labor demands required that students achieve higher levels of education and training, making high school completion a necessity (Gonzalez, Kennedy, & Julien, 2009).
Some efforts were made in the 1960s to tackle student attrition rates, but by the 1970s, high school dropout was an under-addressed problem in the nation until the passage of the No Child Left Behind Act (NCLB) in 2001 (Gonzalez, Kennedy, & Julien, 2009).
With NCLB, states are held accountable for attendance and graduation rates under Adequate Yearly Progress measures (P.L. 107-110). Further, under Title IV, Part A, Safe and Drug-Free Schools and Communities, state education agencies are required to report all dropout rates by school (Seeley, 2006). The most current report from the National Center of Education Statistics (NCES), using data from the 2013-2014 school year, shows the national dropout rate is at 6.4% (NCES, 2016).
On the national level, dropout rates vary significantly between students from different demographic groups. The 2014 report from NCES showed that Hispanic students (10.6%)
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and black students (7.4%) have higher rates of dropout compared to white students (5.2%) (NCES, 2016). There are also a disproportionate number of English Language Learners, homeless students, children in foster care, and students with Individual Education Plans, who drop out (NCES, 2016; Hammond, Smink, & Drew, 2007). Student dropout primarily affects high school students with a plurality of dropouts leaving school in the twelfth grade (NCES, 2012).
Problem Statement
National data show that there is a disproportionate number of students with school-identified disabilities served through Individualized Education Plans (IEPs) who drop out (Martinez & Parra, 2015; Zablocki & Krezmien, 2013; Barrett, Katsiyannis, Willson, & Zhang, 2007), with as many as 28% of students with IEPs dropping out compared to 10% of non-disabled students (Wagner, Newman, Cameto, Garza, & Levine, 2005). Children identified with emotional disabilities are most impacted with dropout rates ranging from 40% to as high as 65% in some schools years (Zablocki & Krezmien, 2013; Pyle & Wexler, 2012; Wagner et al., 2005). While Colorados dropout rate is much lower than the national rate, the state dropout rate has significantly increased for students with disabilities from 1.7% in 2013 to 2.9% in 2014. This was the highest percentage increase across all student population categories in that school year, including students with limited English proficiency and students who are economically disadvantaged (Martinez & Parra, 2015). Among Colorado school districts with more than 25,000 pupils, five have dropout rates for students with disabilities that are over the 2.9% state average (Colorado Department of Education; CDE, 2016).
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Table 1: Dropout rate of students with disabilities by Colorado School districts with more than 25,000 pupils in 2014/2015 school year._____________
County Pupil Count Dropouts Dropout Rate
Denver 4624 250 5.4%
Aurora Public Schools 2311 110 4.8%
Colorado Springs 1307 49 3.7%
Adams 12 1935 67 3.5%
St. Vrain 1477 44 3.0%
This project is an investigation into the predictive factors of dropout for students with IEPs. This study will add to the professional literature and benefit the field of school psychology by identifying potential targets for intervention in those groups of students with IEPs who are most at risk. Intervention with these at-risk students has the potential to reduce dropout rates. Students with IEPs often are already receiving targeted interventions and receive services and support from school psychologists and special education teams. These teams are uniquely situated to quickly and efficiently intervene to remediate dropout in this population. In order to begin the process of remediating the problem of dropout among students with disabilities, we must first determine whether it is the specific type of disability that is the driving factor or if other factors (e.g., race, sex, etc.) have a significant impact on student graduation rates for youth with disabilities.
Research Questions
The research questions that guide this study are as follows:
1. Is there a relationship between student dropout rate and category of disability in a large urban school district?
2. In this school district, is the relationship between dropout and disability category affected by any of the following factors: socioeconomic status (SES), English as a
3


second language (ESL), race, sex, or grade retention history?
CHAPTER II LITERATURE REVIEW
There are several predictive factors for youth dropout in the United States. The principal factors include: absenteeism, behavior problems, low achievement, grade retention, race (black and Hispanic), and low socioeconomic status (SES). Conversely, students with positive views of school, low absences, high GPA, and low course failures have high rates of retention and school completion (Zablocki & Krezmien, 2013). National data show that for students with disabilities, race does not play a significant factor in predicting dropout (Zablocki & Krezmien, 2013). However, SES, grade retention, emotional engagement, and grades do predict dropout for students with disabilities (Zablocki & Krezmien, 2013).
In the state of Colorado, the annual dropout rates in 2014-2015 for the general population are much lower than the national average: 2.4% compared to 7% (Martinez & Parra, 2015). The Colorado Department of Education (CDE) has supported districts in implementing several pilot programs and interventions to increase school engagement that address some of the driving risk factors. These programs include teen pregnancy support, expulsion prevention, before- and after-school care, restorative justice, violence prevention, and student counseling grants. Additionally, there has been an increased focus on parental involvement, post-secondary workforce readiness, and student safety (Martinez, Fritz, & Krueger, 2013).
While the state of Colorado has done well developing programming for dropout overall, there is clearly room for improvement in remediating dropout for children with disabilities. In 2014, Colorados graduation rate for students with IEPs was 54% compared to
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the national average of 68% (National Center for Learning Disabilities; NCLD, 2014; Martinez & Parra, 2015). The alarming disparity in school completion rates for students with disabilities warrants examination. However, little research has been done to identify the factors that contribute to dropout for children with disabilities in Colorado.
Risk Factors
In addition to the predictive factors listed above, many other risk factors for dropout in the general population have been identified. Student or individual risk factors include poor social-emotional skills, learning disabilities, intellectual disability, emotional disability, trauma, and physical health problems (Campbell, Davidson, Onifade, & Smith Nyandoro, 2010). These risk factors impact student integration and success in the school setting, which can result in avoidance or dropout (Campbell et al., 2010). Juvenile delinquency, gang activity, and substance abuse are also significantly related to dropout rates (Barrett et al., 2007). Other risk factors include school size, cultural competency, attitudes, and expulsion policies (Campbell et al., 2010). School climate, which includes bullying, cheating, school failure, and relational aggression can impact student engagement and increase the risk of dropping out (Martinez & Parra, 2015). Familial risk factors include parental substance addiction, domestic violence, and neglect (Campbell et al., 2010). Economic factors affecting high school student dropout can include single parent households, high mobility (e.g., migrant), homelessness, immigration status, and student employment (Campbell et al., 2010). It is also possible that dropout is driven by gradual disengagement from the school community and these aforementioned risk factors are part of the catalyst to disengagement (Sullivan & Sadah, 2016; Finn, 1989).
In Sullivan & Sadahs (2016) review of the literature on dropout prevention among
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students with Emotional Disability, they noted that for students with Emotional Disability or Specific Learning Disability, lack of school connection and engagement could be a stronger risk factor for dropout than for individuals without disabilities. Additionally, a report by the National Longitudinal Transition Study found that 28% of students with disabilities who dropped out cited a dislike of their school experience and 36% named poor relationships with teachers and peers as a driving factor for dropping out (NLTS-2, 2005). It is also possible that perceived social alienation may be a risk factor for students with disabilities who drop out of school (Kortering & Braziel, 1999). According to Sullivan and Sadeh (2016), students with disabilities who drop out have multiple risk factors as early as elementary school, including low achievement, retention, absenteeism, mobility, aggression, problem behaviors, and low socioeconomic status.
Outcomes
The ramifications of dropout are severe and the impact on both the individual and society profound. In the general population, students who drop out of school earn significantly less income over their lifetime. Students who drop out incur a 13% higher joblessness rate and account for significantly higher rates of institutionalization compared to their peers who graduate (Tanner-Smith & Wilson, 2013). For students with disabilities who drop out, only 9% enroll in some form of postsecondary education as compared to 39% of non-dropouts with disabilities (NLTS-2, 2005). Some research suggests that as many as 73% of students with Emotional Disabilities who drop out are arrested within 5 years of leaving school (Sullivan & Sadah, 2016). These outcomes translate to significant costs to society in terms of higher rates of juvenile crime and increased spending on social services, Medicaid, and incarceration (Hendricks et al., 2010).
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Dropping out of school can also have significant impacts on the individuals mental health outcomes. Kaplan and Damphousse (1984) found that even when controlling for previous psychological functioning, students who drop out are more likely to experience anxiety, depression, and self-degradation. In addition, there is some evidence that dropping out of school is associated with major chronic health conditions including diabetes, heart disease, asthma, and high blood pressure (Vaughn, Salas-Wright, & Maynard, 2014). In order to remediate the personal and social costs of dropping out, continued investigation of the driving factors that lead to student disengagement among a variety of populations needs to be undertaken.
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CHAPTER III
METHOD
The extant literature related to school dropout prevention has provided school personnel with a great deal of information relating to the risk factors associated with dropping out, in addition to identifying effective dropout prevention programs. However, there are few studies that have specifically examined those factors in relation to the disproportionately high rates of dropout among students with disabilities. The present study will examine the relationship between various disability categories and demographics contributing to known risk factors in a large, urban school district in Colorado.
Participants
Pre-existing data from the 2014-2015 were collected from a large, urban School District. In the 2014-2015 school year, the district had a total population of 39,184 students enrolled among 4 early childhood centers, 29 elementary schools, 7 P-8/K-8 schools, 7 middle schools, 9 high schools, and 9 charter schools. Seventy-one percent of students in the district qualified for free or reduced lunch and 36% of students identified as English language learners (ELL). The breakdown of race/ethnicity for the total population in the district was as follows: 1% Native American, 5% Asian, 18% black, 55% Hispanic, 17% white, 1% Hawaiian, 4% two or more races.
Study participants (n=1439) included all students in the large urban school district who were identified with a disability (defined as having a IEP) in grades 9 through 12.
Procedure
Following university and school district Institutional Review Board approval, the
8


Director of Assessment and Research for the participating district provided deidentified data of all students in grades 9-12 who were identified with a disability. Data included: end status (the enrollment status of the student at the end of the school year; i.e., dropped out, transferred, etc.), disability, sex, race, grade retention history, English language learner status, and free or reduced lunch status. Students were coded as either dropped out (1) or not dropped out (0). The definition of dropout under Colorado State law was used by the school district and thus a dropout was defined as a "person who leaves school for any reason, except death, before completion of a high school diploma or its equivalent, and who does not transfer to another public or private school or enroll in an approved home study program (CDE, 2017). Not-dropped out students were originally coded as graduated, continuing, transferred or expelled (see appendix 1 for details of CDE exit codes).
Participants were coded as (1) for belonging to each of the following groups: free and reduced lunch, English language learner, and grade retention, and (0) if they did not belong. Sex was coded (0) for female and (1) for male. Race was dummy coded by designating each subject as (1) for the race to which they belong, and (0) for all other races. The same dummy coding procedure was used for disability category.
Initially there were 12 categories of disability as listed in the Colorado Exceptional Childrens Education Act (1 CCR 301-8): 1.Other Health Impairment, 2.Autism, 3.Deaf/Blindness, 4.Hearing Impairment including Deafness, 5.Intellectual Disability,
6.Multiple Disabilities, 7.Orthopedic Impairment, 8.Serious Emotional Disability, 9.Specific Learning Disability, 10. Speech and Language Impairment, 11.Traumatic Brain Injury,
12. Visual Impairment including blindness. Each of these categories and their respective
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dropout figures were used in addressing research question 1. As several of the categories of disability had few participants, and others were ambiguous as to the specifics of the disability type (e.g., multiple disabilities, other health impairment, etc.), several related categories were collapsed in order to allow for more robust and reliable logistic regression used to analyze research question 2. Intellectual Disability (ID), Serious Emotional Disability (SED) and Significant Learning Disability (SLD) were left uncombined. Hearing Impairment, Visual Impairment, Physical Disability and Orthopedic Disability were combined into a single category representing non-cognitive disabilities. Language-based disabilities (Speech and Language and Autism Spectrum Disorders [ASD]) were combined into one category, while Traumatic Brain Injury (TBI), Other Health Impairment (OHI), and Multiple Disabilities were collapsed into an other category.
Measures
The dependent variable for this study is dropout (yes/no). The independent variables for this study are the demographics of race, sex, free or reduced lunch, grade retention, and the six aforementioned condensed disability categories: Intellectual Disability (ID); Serious Emotional Disability (SED); Language-based disabilities (Speech and Language and Autism Spectrum Disorders); Physical/Sensory Disabilities (Hearing Impairment, Visual Impairment, Physical Disability and Orthopedic Disability); Other (Traumatic Brain Injury, Other Health Impairment, and Multiple Disabilities); Significant Learning Disability (SED).
Analysis
The data in this study represented categorical variables and did not meet the assumptions necessary to employ parametric statistical procedures. As such, basic descriptive
10


statistics and nonparametric analyses were used, including cross-tabulations and contingency table analyses. The research questions were as follows:
1. Is there a relationship between student dropout rate and category of disability in a large urban school district?
A chi square analysis was used to examine the relationship between student dropout rates and disability category. Because the dependent variable was measured on a dichotomous, categorical scale, a binomial logistic regression model was used to determine whether belonging to an individual disability category significantly predicted dropout.
2. In this school district, is the relationship between dropout and disability category affected by any of the following factors: socioeconomic status (SES), as measured by Free or Reduced Lunch status); English as a second language (ESL); race; sex; or grade retention history?
Binary logistic models were constructed to determine the significance of risk factors and their impact on the relationship between disability category and dropout.
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CHAPTER IV
RESULTS
Demographics
The demographics of the population were 34.3% female and 65.7% male. Just over 53% of the study sample qualified for free and reduced lunch, 12% had been retained/repeated a grade, and 39% were identified as English Language Learners. The breakdown for race/ethnicity in the study sample was as follows: 1.1% American Indian or Alaskan Native, 1.7% Asian, 24.3% black, 49.6% Hispanic, 19% white, .2% Native Hawaiian, and 4% for two or more races.
Table 2: Characteristics of Study Population
Dropout No Yes Total
Race
American Indian or Alaska Native 14 2 16(1.1%)
Asian 24 1 25 (1.7%)
Black/African American 323 27 350 (24.3%)
Hispanic/Latino 670 44 714 (49.6%)
White 254 19 273 (19.0%)
Native Hawaiian or Alaska Native 3 0 3 (0.2%)
Two or more races 53 5 58 (4.0%)
Sex
Male 876 69 945 (65.7%)
Female 465 29 494 (34.3%)
Free or Reduced Lunch
Yes 753 16 769 (53.4%)
No 588 82 670 (46.6%)
English Language Learner
Yes 531 30 561 (39.0%)
No 810 68 878 (61.0%)
1167 98 1265 (87.9%)
174 0 174 (12.0%)
Grade Retention
0
1
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Research Question 1
Is there a relationship between student dropout rate and category of disability in a large urban school district?
All 12 disability categories from Colorado Exceptional Childrens Act were analyzed and overall dropout rate for the sample was 6.8% (Table.2). All groups except for SED, SLD, and TBI had disproportionately low numbers of dropouts. SLD (n=894) was a marginally overrepresented category for dropout, comprising 62.1% of the total sample and 69.4% of the dropouts. The disproportionality within SED was more striking; the group made up only 6.7 % of the study population but comprised 17.3 % of dropouts.
Table 3: Disability Type x Dropout Crosstabs
Dropout: No Yes Total
Disability Type
Count 109 3 112
Intellectual Disability % of Dropout Total 8.1% 3.1% -
% of Total 7.6% 0.2% 7.8%
Count 80 17 97
Serious Emotional Disability (SED) % of Dropout Total 6.0% 17.3% -
% of Total 5.6% 1.2% 6.7%
Count 826 68 894
Specific Learning Disability (SLD) % of Dropout Total 61.6% 69.4% -
% of Total 57.4% 4.7% 62.1%
Count 19 0 19
Hearing Impairment % of Dropout Total 1.4% 0.0% -
% of Total 1.3% 0.0% 1.3%
Count 6 0 6
Visual Impairment % of Dropout Total 0.4% 0.0% -
% of Total 0.4% 0.0% 0.4%
Count 55 3 58
Physical Disability % of Dropout Total 4.1% 3.1% -
% of Total 3.8% 0.2% 4.0%
Count 24 1 25
Speech or Language Disability % of Dropout Total 1.8% 1.0% -
% of Total 1.7% 0.1% 1.7%
Count 104 2 106
Multiple Disabilities % of Dropout Total 7.8% 2.0% -
% of Total 7.2% 0.1% 7.4%
cont d on next page
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Table 2 contd
Dropout: No Yes Total
Disability Type
Count 53 1 54
Autism Spectrum Disorder (ASD) % of Dropout Total 4.0% 1.0% -
% of Total 3.7% 0.1% 3.8%
Count 8 1 9
Traumatic Brain Injury % of Dropout Total 0.6% 1.0% -
% of Total 0.6% 0.1% 0.6%
Count 2 0 2
Orthopedic Impairment % of Dropout Total 0.1% 0.0%
% of Total 0.1% 0.0% 0.1%
Count 55 2 57
Other Health Impairment (OHI) % of Dropout Total 4.1% 2.0% -
% of Total 3.8% 0.1% 4.0%
Total no. (%) 1341 (93.2) 98 (6.8) 1439
2 X 31.336 p = .001
Research Question 2
In this school district, is the relationship between dropout and disability category affected by any of the following factors: socioeconomic status (SES) as measured by Free or Reduced Lunch status; English as a second language (ESL); race; sex; or grade retention history?
For the binary logistic regression model, the SLD group was used as the reference category because it had the most proportional distribution of dropouts compared to the size of the group (62.1% of the study population and 69.4% of all dropouts). The SLD category was also the largest disability category (n = 826) in the sample. As described above, the 12 disability categories were collapsed into 6 categories: Intellectual Disability (ID), Serious Emotional Disability (SED), Significant Learning Disability (SLD), Non-Cognitive Disability, Language-based Disability, and Other.
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When a binary regression model was constructed with disability category alone (Table 3) against dropout, the overall category was significant (p < .000). Among the disability groups, only SED was significant (p < 0.001) with an Exp (B) value of 2.581. This implies that a student in this cohort identified as SED is approximately 2.5 times more likely to drop out than a student in the reference category (SLD).
Table 4: Summary of Binary Logistic Regression Analysis with Disability Category
Dropout
95% C.I.for EXP(B)
Predictor B SE B eB Lower Upper
Disability Group
Intellectual Disability -1.096 .599 .334 .103 1.081
Serious F.motional ...... ...
Disturbance 94g*** .295 2.581 1.447 4.605
Speech/Language Disabilities -1.154 .727 .316 .076 1.312
Physical/Sensory Disabilities -.811 .601 .444 .137 1.444
Other Disabilities -1.011 .471 .364 .144 .916
Note: eB = exponentiated B. For the race variable, White was the reference category. For the Disability group variable, Significant Learning Disability was the reference category. Reference categories were chosen based upon the variable with the most representative distribution of dropout. *p < .05. **p < .01. ***p < .001.
When a binary logistic regression model was constructed with disability groups and the potential confounding independent variables (free or reduced lunch, retention status, sex,
ESL status, and race; Table 4), disability category remained significant (p < .004). Of the potential confounders, only free or reduced lunch status had a statistically significant result (p < .000, Exp(B) = .157). This indicates that students without free and reduced lunch status were 15.7% as likely to drop out as students who qualified for free or reduced lunch. As with the first model described above, the only category within disability type that was a significant
15


predictor of dropout was SED (p < .022, Exp(B) = 2.09E This suggests that while free or reduced lunch status does predict dropout to a degree independently, it does not fully account for the disproportionality in dropout among students with SED.
Grade retention status was not significant in the regression model. However, as seen in Table 1, while there were 174 students who were retained, no one in this group dropped out. In order for the logistic regression model to determine significance or odds ratio for a variable, each group within the variable needs to have a non-zero number of events in each category of the dependent variable. In this case, the model was unable to determine significance or odds because all dropouts were in the group who were not retained. However, results of a Chi square analysis indicate that the distribution of dropouts among students based upon their retention status is very unlikely due to chance alone (p = .000).
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Table 5: Summary of Binary Logistic Regression Analysis with All Variables
Dropout
Predictor B SEB B e 95% C.I.for Lower EXP(B) Upper
Free or Reduced Lunch -1.851*** .288 .157 .089 .276
Grade Retention -18.274 2836.64 .000 .000
Sex .183 .239 1.201 .752 1.919
ESL Status -.109 .323 .897 .476 1.688
Race
American Indian or Alaska Native 1.543 .859 4.681 .869 25.200
Asian -.179 1.102 .836 .096 7.243
Black .284 .327 1.328 .700 2.521
Hispanic Latino .228 .356 1.256 .626 2.523
Two or More Races .333 .550 1.396 .475 4.103
Native Hawaiian or other Pacific Islander -17.929 22881.61 .000 .000
Disability Group
Intellectual Disability -.810 .614 .445 .133 1.482
Serious Emotional Disturbance .737* .323 2.091 1.111 3.935
Speech/Language Disabilities -1.313 .744 .269 .063 1.157
Physical/Sensory Disabilities -.942 .611 .390 .118 1.292
Other Disabilities -.835 .484 .434 .168 1.121
Note: eB = exponentiated B. For the race variable, White was the reference category. For the Disability group variable, Significant Learning Disability was the reference category. Reference categories were chosen based upon the variable with the most representative distribution of dropout. *p < .05. **p < .01. ***p < .001.
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CHAPTER V
DISCUSSION
This study aimed to determine if a specific disability category acted as a significant predictive factor leading to dropout among youth with Individualized Education Plans (IEPS) in a large urban school district. When controlling for race, sex, grade retention, English language proficiency (ESL), socioeconomic status (SES) as measured by free or reduced lunch status, and disability category, three major factors relating to dropout among students with IEPs emerged. These factors are: students with an identification of Serious Emotional Disability (SED), students who do not qualify for free and reduced lunch, and students who were never retained/repeated a grade. Students identified with Serious Emotional Disability (SED) or who do not qualify for free and reduced lunch were more likely to drop out than other students with IEPs in this large urban school district.
The results demonstrating a relationship between dropping out and not being retained are contrary to previous research (e.g., Zablocki & Krezmien, 2013; Sullivan & Sadah,
2016). This was an unexpected finding that may be isolated to this particular sample. There are no district-wide dropout and retention data for the districts general population; thus, a comparison between the groups to see if this was a district trend was not possible. While there is an overwhelming amount of literature indicating that retention can often lead to poor outcomes such as dropout (Jimerson, Anderson, & Whipple. 2002), there are some factors that lead to positive outcomes among retained students, such as higher self-esteem, positive school behavior, and academic success (Jimerson, Ferguson, Whipple, Anderson, & Dalton. 2002). The groups that were most likely to be retained were students with intellectual disability, specific learning disability (SLD), and multiple disabilities. It is possible that
18


children within these groups possessed more supports and/or protective factors than their counterparts.
The results indicating that the likelihood of dropping out was increased in students who did not qualify for free or reduced lunch are contrary to previous research (e.g., Zablocki & Krezmien, 2013; Sullivan & Sadah, 2016). This school district had a significantly lower rate (approximately 75% lower) of dropout among students with free or reduced lunch than other nearby large urban districts (CDE, 2016). It is possible that free or reduced lunch status may serve as a protective factor because the program incentivizes students to attend school to gain access to food. In his research on the impacts of the National School Lunch program, Hinrichs (2010) found that the program had increased educational attainment and that the free or reduced lunch program may have prompted children to attend school.
The finding that this districts SED population was twice as likely to drop out than students with other disability categories is consistent with the literature and national trends in dropout among students with disabilities (Sullivan & Sadah, 2016; NLTS-2, 2005; NCES, 2015). One possible explanation for the disproportionally high rate of dropout among students with SED is that there do not appear to be dropout prevention programs in place tailored to this specific population. In their review of the literature, Sullivan & Sadah (2016) were able to find only one dropout prevention program in the U.S. targeted to students with emotional disturbance. The program, Check and Connect, did not show a significant effect on the likelihood of dropping out, but it did reduce some risk factors for dropout such as truancy and mobility (Sullivan & Sadah, 2016).
Another potential barrier to successful school completion for students with SED is the fact that they are hospitalized or placed in alternate education programs at a higher rate than
19


their peers, and thus have more extended periods of absence from school (Clemens, Welfare, & Williams, 2011). When they return to the school environment, they are expected to reengage with their learning in the absence of specialized support. The literature is limited in terms of the specific impact of hospitalization on student engagement upon reentry to school for individuals with SED, but research related to children returning to school following medical hospitalizations indicates that students can experience many barriers, including social isolation, trouble with medical management, and negative school experiences (Clemens et al. 2011). Given that one of the driving factors for dropout is disengagement (Sullivan & Sadah, 2016; Finn, 1989), it is likely that when students with emotional disturbance miss school due to hospitalizations, they are at increased risk of dropout.
Limitations
The current study has several limitations that need to be considered when examining the findings presented. First, the data used in this study were collected by the school district and include all students with disabilities in grades 7-12. As a third party collected these data, it is possible that the validity of the data set contains inconsistent documentation, error, or bias. Second, this study focused solely on an urban school district and did not investigate rural or suburban environments. As such, it may not be appropriate to generalize results outside of the urban setting. Further, schools provide services and supports to students that impact the likelihood that students will complete school and graduate. These data do not account for such interventions, which could have a variety of impacts, good and bad, and could skew findings one way or the other. Another limitation may be problems with intersectionality; that is, that individuals who drop out may belong to more than one risk group population (i.e., both SED, and low SES) and the significance of the relationships between population
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membership and disability category were suppressed in the binary analysis.
Conclusion and Implications
It has been well established that students with disabilities especially emotional disabilities are at higher risk of dropout than their typical peers, and thus it is critical that future research focuses on the development of interventions to remediate this disproportionality. Additionally, much of the literature on free or reduced lunch programs has focused on nutritional benefits rather than the programs impacts on dropout and school engagement. As free or reduced lunch status was found to be a protective factor in this population, further exploration into this relationship should be undertaken.
Investigation into possible protective factors for children with SED, impacts of increased supports and engagement in elementary school, and the efficacy of programs that support youth with emotional disabilities at the secondary level is crucial. As students with SED are already being served under IEPs and often receive services from school psychologists, we are well situated to develop and implement programming and interventions to remediate dropout for this group. One such promising program is the Bridge for Resilient Youth in Transition Program (American Psychological Association, 2014), which began in Brookline, MA and aims to ease the transitional and reintegration process following mental health institutionalization. This program has been expanded to 17 school systems in Massachusetts and a promising 95% on-time graduation rate for the 1500 students who have been served by the program is reported (APA 2014). As we strive for more equitable and just outcomes among all students, it is imperative that school psychologists be made aware of the issue of dropout among students with SED early on in order to lay the foundation of engagement for these students. It will only be through continued action that we can help these students
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overcome their challenges so that they may attain school completion.
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REFERENCES
American Psychological Association. (2014). APA Gold Achievement Award: A school-
based transition program for adolescent returning to high school after a mental health emergency. Psychiatric Services, 65:11, e6-e8.
Barrett, D. E., Katsiyannis, A., Wilson, V., & Zhang, D. (2007). Truancy offenders in
juvenile justice system: Examinations of first and second referrals. Remedial and Special Education, 28, 244-256.
Campbell, C., Davidson II, W. S., Onifade, E., & Smith Nyandoro, A. (2010). Truancy and patterns of criminogenic risk in a young offender population. Youth Violence and Juvenile Justice, 5(1), 3-18.
Carter, S. D., Evans, E. W., Hendricks, M. A., McKinley, L., & Sale, C. J. (2010). Evaluation of a truancy court intervention in four middle schools. Psychology in the Schools, 47(2), 173-193.
Check & Connect: A Comprehensive Student Engagement Intervention. (2015) Institute of Community Integration, University of Minnesota. Retrieved from http://checkandconnect.umn.edu/model/default.html
Clemens, E. V., Welfare L. E. & Williams, A. M., (2011). Elements of successful school reentry after psychiatric hospitalization. Preventing School Failure: Alternative Education for Children and Youth, 55, 202-213.
Colorado Department of Education. (2017) Definitions of selected terms. Retrieved from https://www.cde.state.co.us/cdereval/rvdefme
Colorado Department of Education. (2016). 2015-2016 Drop out Rates by district and instructional service program type. Retrieved from https://www.cde.state.co.us/cdereval/dropoutcurrent
Dorn, S. (1996). Creating the dropout: An institutional and social history of school failure. Praeger: Westport, CT.
Finn, J. (1989). Withdrawing from school. Review of Educational Research. 59(2), 117-142. Retrieved from
http://gse.buffalo.edu/gsefiles/documents/alumni/Fall09 Jeremy Finn Withdrawing, pdf
Gonzalez. E, Kennedy. P, & St. Julien. T (2009). Dropout prevention: History, politics, and policy. Retrieved from
http://alOOeducationalpolicy.pbworks.com/f/Dropout+Prevention. Compiled, pdf
Hammond. C., Smink. J., & Drew. S. (2007). Dropout risk factors and exemplary programs:
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A technical report. National Dropout Prevention Center. D. Linton: Communities in Schools, Inc.. Retrieved from http://dropoutprevention.org/resources/maior-research-reports/dropout-risk-factors-and-exemplary-programs-a-technical-report/
Hendricks, M. A., Sale, E. W., Evans, C. J., McKinley, L., & DeLozier Carter, S. (2010).
Evaluation of a truancy court intervention in four middle schools. Psychology in the Schools, 47(2), 173-183.
Hinrichs, P. (2010). The effects of the National School Lunch Program on education and health. The Journal of Policy Analysis and Management 29, 479-505. doi: 10.1002/pam.20506
Jimerson, S. R., Anderson, G., & Whipple, A. (2002). Winning the battle and losing the war: Examining the relation between grade retention and dropping out of high school. Psychology in the Schools, 39(4), 441-457
Jimerson, S. R., Ferguson, P., Whipple, A. D., Anderson, G. E., Dalton, M. J. (2002).
Exploring the association between grade retention and dropout: A longitudinal study examining socio-emotional, behavioral, and achievement Characteristics of retained students. California School Psychologist, 7, 51-62.
Kaplan. D.S., & Damphousse.K.R. (1994). Mental health implications of not graduating from high school. Journal of Experimental Education, 621(2), 105-124.
Kortering. L. J., & Braziel, P. M. (1999). School dropout from the perspective of former students. Remedial and Social Education, 20(2), 78-83.
Krueger, J., Fritz, P., & Martinez, J. (2013). Colorado Department of Education 2011-2012 state policy report dropout prevention and student engagement. Retrieved from http://www.boarddocs.com/co/cde/Board.nsf/files/96DULC7C8EA7/$file/2012 DPS E Legislative Report.pdf
Martinez, J., & Parra, J. (2015). Colorado Department of Education 2013-2014 state policy report dropout prevention and student engagement. Retrieved from https://www.cde.state.co.us/dropoutprevention/2014statepolicyreportv2
National Center for Learning Disabilities. (2014). The state of learning disabilities. Retrieved from: https://www.ncld.org/wp-content/uploads/2014/ll/2014-State-of-LD.pdf
Pyle, N., & Wexler, J. (2012). Preventing students with disabilities from dropping out. Intervention in School and Clinic, 47(5), 283-289. Retrieved from http://0-search.proquest.com.skyline.ucdenver.edu/docview/1312420733?accountid=14506
Sullivan, A., & Sadeh. S. (2016). Does the empirical literature inform prevention of dropout among students with emotional disturbance? A systematic review and call to action. Exceptionality, 24(4), 251-262. doi: 10.1080/09362835.2016.1196440
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U.S. Department of Education, National Center for Education Statistics. (2015). The condition of ddncation 2015 (NCES 2015-144), Status dropout rates.
U.S. Department of Education, National Center for Education Statistics. (2016). The condition of education 2016 (NCES 2016-144), status dropout rates.
Vaughn, M. G., Salas-Wright, C. P., & Maynard, B. R. 2014). Dropping out of school and chronic disease in the United States. Journal of Public Health, 22, 265-270. Doi:
10.1007/s 103 89-014-0615-x
Wagner, M., Newman, L., Cameto, R., Garza, N., and Levine, P. (2005). After high school: A first look at the post school experiences of youth with disabilities. A report from the National Longitudinal Transition Study-2 (NLTS2) Menlo Park, CA: SRI International. Available at
www.nlts2.org/reports/2005_04/nlts2_report_2005_04_complete.pdf.
Zablocki, M., & Krezmien, M. P. (2013). Drop-out predictors among students with high-incidence disabilities: A national longitudinal and transitional study 2 analysis. Journal of Disability Policy Studies, 24{ 1), 53-64. doi:http://0-dx. doi. org. skyline.ucdenver.edu/10.1177/1044207311427726
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! PREDIC TORS OF HIGH SCHOOL DROPOUT AMO NG STUDENTS WITH INDIVIDUAL EDUCAT ION PLANS by KAITE JOHANNA HAALAND B.S., Lesley University, 2008 A thesis submitted to the Faculty of the Graduate School of the University of Colorado Denver in partial fulfillment of the requirements for the degree of Doctor of Psychology School Psychology Program 2017

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! ii This thesis for the Doctor of Psychology degree by Kaite Johanna Haaland has been approved for the School Psychology Program by Franci Crepeau Hobson Chair Bryn Harris Colette Hohnbaum Date: May 13, 2017

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! iii Haaland, Kaite Johanna (PsyD, School Psychology Program ) Predictors of High School Dropout Among Students With Individual Education Plans thesis directed by Associate Professor Franci Crepeau Hobson ABSTRACT Students with disab ilities have a disproportionately higher rate of dropout compared to typical peers. This study was intended to investigate the predictive factors of dropout for youth with disabilities. Specifically this study examine s how disability classification and demographics impact dropout rates for youth with disabilities. Participants in this study were all students in a large urban school district who were identified with a disabi lity (as defined as having a IEP) in grades 9 12 (N=1439) As most variables were categorical nonparametric analyses were used, including cross tabulations and contingency table analyses. In addition a binary logistic regression model was used to isolate the variables associated with dropout Findings suggest that youth with serious emotional disability (SED) have a high risk of dropout compared to students who fall under other disability categories. Additionally, students who did not qualify for free and reduced lunch and students who were never retained (repeated a grade) were more at risk for dropout Practical implications and future directions are discussed. The form and content of this abstract are approved. I recommend its publication. Approved: Franci Crepeau Hobson

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! iv DEDICATION I ded icate this capstone to my family. Without the unwavering support and encouragement of my partner Eric I could not have completed this work. I would also like to thank my two wonderful children Liam and Emmett who h ave grown up with a mother who was always at school. I hope my hard work will inspire you one day to follow your dreams, whatever they may be. This work is also dedicated to my father George Humphrey who led by example and encouraged me to follow my dreams. Dad, you have supported me for many years so I could attain this ; thank you. Lastly, a very special thanks to my mother Sytske Humphrey who would not let me disappear into the world without gradu ating High School. Without her dedic ation and belief in my ability to not only succeed but also positively impact the world around me, I would have remained a high school dropout I am here because you were an innovative educator who fought for and believed in me Thank you!

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! v TABLE OF CONTENTS CHAPTER I. INTRODUCTION ................................ ................................ ............................. 1 Problem Statement ................................ ................................ ............................. 2 Research Questions ................................ ................................ ............................ 3 II. LITERATURE REVIEW ................................ ................................ .................. 4 Risk Factors ................................ ................................ ................................ ....... 5 Outcomes ................................ ................................ ................................ ........... 6 III. M ETHOD ................................ ................................ ................................ .......... 7 Participants ................................ ................................ ................................ ......... 7 Procedures ................................ ................................ ................................ .......... 8 Measures ................................ ................................ ................................ ............ 9 Analysis ................................ ................................ ................................ ............ 10 IV. RESULTS ................................ ................................ ................................ ........ 11 Demographics ................................ ................................ ................................ .. 11 Research Question 1 ................................ ................................ ........................ 12 Research Question 2 ................................ ................................ ........................ 13 V. D ISCUSSON ................................ ................................ ................................ .. 17 Limitations ................................ ................................ ................................ ....... 19 Conclusion ................................ ................................ ................................ ....... 20 REF E RENCES ................................ ................................ ................................ ............ 2 1

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! 1 CHAPTER I INTRODUCTION High school dropout has been recognized as a social problem in the United States since the second half of the 20 th century. Originally "dropout" was a benign term used to denote students who had not completed high school, but by the early 1960s it became a depreciatory term used to de scribe degenerate youth (Dorm, 1996) A lteration s in societal expectations of high school completion likely changed due to shifts in the workforce demands of the country and a link between education and the economy (Gonzalez, Kennedy, & Julien, 2009) : a s the United States moved from an industrialized nation to a more technological one, labor demands required that students achieve higher levels of education and training, making high school completion a necessity (Gonzalez, Kennedy, & Julien, 2009) Some effort s w ere made in the 1960s to tackle student attrition rates, but by the 1970s high school dropout was an under addressed problem in the nation until the passage of the No Child Left Behind Act (NCLB) in 2001 (Gonzalez, Kennedy, & Julien, 2009). With NCLB, states are held accountable for attendance and graduation rates under Adequate Yearly Progress measures (P.L. 107 110). Further, under Title IV, Part A, Safe and Drug Free Schools and Communities, state education agencies are required to report all d ropout rates by school (Seeley, 2006). The most current report from the National Center of Education Statistics (NCES), using data from the 2013 2014 s chool year, shows the national dropout rate is at 6.4% (NCES, 2016). On the national level, dropout rates vary s ignificantly between students from different demographic groups. The 2014 report from NCES s howed that Hispanic students (10.6 %)

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! 2 and black students (7 .4 %) have higher rates of dropout compared to white students (5.2%) (NCES, 2016 ). There are al so a disproportionate number of English Language Learners, homeless students, children in foster care, and students with Individual Education Plans, who drop out (NCES, 2016 ; Hammond, Smink, & Drew, 2007). Student dropout primarily affects high school stud ents with a plurality of dropouts leaving school in the twelfth grade (NCES, 2012). Problem Statement N ational data show that there is a disproportionate number of students with school identified disabilities served through Individual ized Education Plans (IEPs) who drop out (Martinez & Parra, 2015; Zablocki & Krezmien, 2013; Barrett, Katsiyannis, Willson, & Zhang, 2007) with a s many as 28% of students with IEPs dropping out compared to 10% of non disabled students ( Wagner, Newman, Cameto, Garza, & Levine, 2005) Children identified with emotional disabilities are most impacted with dropout rate s ranging from 40% to as high as 65% in some schools years (Zablocki & Krezmien, 2013; Pyle & Wexler, 2012; Wagner et al. 2005) W hile Colorado's dropout rate is much lower than the national rate, the stat e dropout rate has significantly increased for students with disabilities from 1.7% in 2013 to 2.9% in 2014 This was the highest percentage increase across all student population catego ries in that school year including students with limited English proficiency and students who are economically disadvantaged (Martinez & Parra, 2015). Among Colorado school districts with more than 25,000 pupils five have dropout rates for students with disabilities that are over the 2.9% state average ( Colorado Department of Education; CDE, 2016)

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! 3 Table 1: Drop out rate of students with disabilities by Colorado School districts with more than 25,000 pupils in 2014/2015 school year County Pupil Count Dropouts Dropout Rate Denver 4624 250 5.4% Aurora Public Schools 2311 110 4.8% Colorado Springs 1307 49 3.7% Adams 12 1935 67 3.5% St. Vrain 1477 44 3.0% This project is an investigation into the predictive factors of dropout for students with IEPs This study will add to the professional literature and benefit the field of school psychology by identifying potential target s for intervention in those groups of students with IEPs who are most at risk Intervention with these at risk students has the potential to reduce dropout rates. Students with IEPs often are already receiving targeted interventions and receive services and support from school psychologists and special education teams T hese teams are uniquely situated to q uickly and efficiently intervene to remediate dropout in this population. In order to begin the process of remediating the problem of dropout among students with disabilities, we must first determine whether it is the specific type of disability that is the driving factor or if other factors (e.g. race, sex, etc.) have a significant impact on student graduation rates for youth with disabilities Research Questions The research questions that guide this study are as follows: 1. I s the re a relationship betw een student dropout rate and category of disability in a large urban school district ? 2. In this school district, i s the relationship between dropout and disability category a ffected by any of the following factors : socioeconomic status ( SES ), English as a

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! 4 s econd l anguage (ESL), r ace, s ex or grade retention history? CHAPTER II LITERATURE REVIEW There are several predictive factors for youth dropout in the United States. The principal factors include : absenteeism, behavior problems, low achievement, grade retention, race (black and Hispanic), and low socioeconomic status ( SES ) Conversely, students with positive views of school, low absences, high GPA, and low course failures have high rates of retenti on and school completion (Zablocki & Krezmien, 2013). National data show that for students with disabilities race does not play a significant factor in predicting dropout (Zablocki & Krezmien, 2013). However SES, grade retention, emotional engagement, an d grades do predict dropout for students with disabilities (Zablocki & Krezmien, 2013). In the state of Colorado the annual dropout rates in 2014 2015 for the general population are much lower than the national average : 2.4% compared to 7% (Martinez & Parra, 2015). The Colorado Department of Education (CDE) has supported districts in implementing several pilot programs and interventions to increase school engagement that address some of the driving risk factors. These programs include teen pregnancy support, expulsion prevention, before and after school care restorative justice, violence prevention, and student counseling grants. Additionally, there has been an increased focus on parental involvement, post secondary workforce readiness, and student safety (Martinez, Fritz, & Krueger, 2013). While the state of Colorado has done well developing programming for dropout overall, there is clearly room for improvement in remediating dropout for children with disabilities. In 2014 Colorado's graduation rate for students with IEPs was 54% compared to

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! 5 the national average of 68% ( National Center for Learning Disabilities; NCLD, 2014; Martinez & Parra, 2015) The alarming disparity in school completion rates for students with disabilities warrants examination. However, l ittle research has been done to identify the fa ctors that contribute to dropout for children with disabilities in Colorado. Risk Factors I n addition to the predictive factors listed above, many other risk factors f or dropout in the general population have been identified. Student or individual risk factors include poor social emotional skills, learning disabilities, intellectual disability, emotional disability, trauma, and physical health problems (Campbell, Davidson, Onifade, & Smith Nyandoro, 2010). These risk factors im pact student integration and success in the school setting, which can result in avoidance or dropout (Campbell et al., 2010). Juvenile delinquency, gang activity, and substance abuse are also significantly related to dropout rates (Barrett et al., 2007). O ther risk factors include s chool size, cultural competency, attitudes, and expulsion policies (Campbell et al., 2010). School climate, which includes bullying, cheating, school failure, and relational aggression can impact student engagement and increase t he risk of dropping out (Martinez & Parra, 2015). Familial risk factors include parent al substance addiction, domestic violence, and neglect (Campbell et al., 2010). Economic factors affecting high school student dropout can include single parent household s, high mobility (e.g. migrant) homelessness, immigration status, and student employment (Campbell et al., 2010). It is also possible that dropout is driven by gradual disengagement from the school community and these aforementioned risk factors are part of the catalyst to disengagement (Sullivan & Sadah, 2016; Finn, 1989) In Sullivan & Sadah's (2016) review of the literature on dropout prevention among

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! 6 students with Emotional D isability they noted that for students with Emotional Disability or Specific Learning Disability lack of school connection and engagement could be a stronger risk factor for dropout than for individuals without disabilities. Additionally, a report by the National Longitudinal Transition Study found that 28% of students wi th disabilities who drop ped out cited a dislike of their school experience and 36% named poor relationship s wi th teachers and peers as a driving factor for drop ping out (NLTS 2, 2005 ). It is also possible that perceived social alienation may be a risk factor for students with disabilities who drop out of school (Kortering & Braziel, 1999). According to Sullivan and Sadeh (2016) student s with disabilities who drop out have multiple risk factors as early as elem entary school including l ow achievement, retention, absenteeism, mobility, aggression, problem behaviors, and low socioeconomic status Outco mes The ramifications of dropout are severe and the impact on both the individual and society profound. In the general population s tudents who drop out of school earn significantly less income ove r their lifetime. S tudents who drop out incur a 13% higher joblessness rate and account for significantly higher rates of institutionalization compared to their peers who graduate ( Tanner Smith & Wilson, 2013 ). For students with disabilities who drop out, only 9% enroll in some form of postsecondary education as compared to 39% of non drop outs with disabilities (NLTS 2, 2005). S ome research suggests that as many as 73% of students with Emotional Disabilities who drop out are arrested within 5 years of leaving school (Sullivan & Sadah, 2016). These outcomes translate to significant costs to society in terms of higher rates of juvenile crime and increased spending o n social services, Medicaid, and incarceration (Hendricks et al., 2010).

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! 7 Dropping out of school can also have significant impacts on the individual s mental health outcomes. Kaplan and Damphousse (1984 ) found that even when controlling for previous psy chological functioning students who drop out are more likely to experience anxiety, depression, and self degradation. In addition, there is some evidence that dropping out of school is associated with major chronic health conditions including diabetes, heart disease, asthma, and high blood pressure (Vaughn, Salas Wright, & Maynard, 2014). In order to remediate the personal and social costs of dropping out conti nued investigation of the driving factors that lead to student disengagement among a variety of populations need s to be undertaken

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! 8 CHAPTER III METHOD T he extant literature re lated to school dropout prevention has provided school personnel with a great deal of information relating to the risk factors associated with dropping out in addition to identifying effective dropout prevention programs. However, there are few studies th at have specifically examined those factors in relation to the disproportionately high rates of dropout among students with disabilities. T he present study will examine the relationship between various disability categories and demographics contributing to known risk factors in a large urban school district in Colorado. Participants Pre existing data from the 2014 2015 were collected from a large urban School District. In the 2014 2015 school year the district had a total population of 39,184 students enrolled among 4 early childhood centers, 29 elementary schools, 7 P 8/K 8 schools, 7 middle schools, 9 high schools, and 9 charter schools. Seventy one percent of stu dents in the district qualified for free or reduced lunch and 36% of students identified as English language learners (ELL). The breakdown of race/ethnicity for the total population in the district was as follows : 1% Native American, 5% Asian 18% black, 55% Hispanic, 17 % white, 1% Hawaiian, 4% two or more races. Study participants ( n =1439 ) include d all students in the large urban school district who were identified with a disabilit y ( defined as having a IEP) in grades 9 through 12 Procedure Following university and school district Institutional Review Board approval, the

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! 9 Director of Assessment and Research for the participating district provide d deidentified data of all students in grades 9 12 who were identified with a disability Da ta include d : end status (the enrollment status of the student at the end of the school year ; i.e. dro pped out, transferred, etc.) disability, sex race, grade retention history, English language learner status, and free or reduced lunch status. Students were coded as either "dropped out" ( 1 ) or not dropped out ( 0 ) The definition of dropout under Colorado State law was used by the school district and thus a dropout wa s defined as a "person who leaves school for any reason, except death, before completion of a high school diploma or its equivalent, and who does not transfer to anoth er public or private school or enroll in an approved home study program" (CDE, 2017). "N ot dropped out" students were originally coded as graduated, continuing, transferred or expelled (see appendix 1 for details of CDE exit codes). Participants were cod ed as (1) for belonging to each of the following groups: free and reduced lunch, English language learner, and grade retention and (0) if they d id not belong. Sex was coded (0) for female and (1) for male Race was dummy coded by designating each subject as (1) for the race to which they belong, and (0) for all other races. The same dummy coding procedure was used for disability category. Initially there were 12 categories of disability as listed in the Colorado Exceptional Children's Education Act ( 1 CCR 301 8 ): 1.Other Health Impairment, 2.Autism, 3.Deaf/Blindness, 4. Hearing Im pairment including Deafness, 5.Intellectual Disability, 6. Multiple Disabiliti es, 7.Orthopedic Impairment, 8. S erious Emotional Disability, 9. Sp ecific Learning Disability, 10. Spe e ch and Language Impairment, 11 Traumatic Brain Injury, 12. Visual Impairment including blindness Each of these categories and their respective

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! 10 dropout figures were used in addressing research question 1. As several of the categories of disability had few participants and others were ambiguous as to the specifics of the disability type (e.g. multiple disabilities, other health impairment, etc.) several related categories were collapsed in order to allow for more robust and reliable logistic regression us ed to analyze research question 2 Intellectual Disability (ID) Serious Emotional Disability (SED) and Significant Learning Disability (SLD) were left uncombined Hearing Impairment, Visual Impairment, Physical Disability and Orthopedic Disability were co mbined into a single category representing non cognitive disabilities Language based disabilities ( Speech and Language and Autism Spectrum Disorders [ ASD ] ) were combined into one category, while Traumatic Brain Injury (TBI ) O ther Health Impairment (OHI) and Multiple Disabilities were collapsed into an "other" category. Measures The dependent variable for this study is dropout (yes/no) The i ndependent variables for this study are the demographics of race, sex, free or reduced lunch grade retention, and the six aforementioned condensed disability categories: Intellectual Disability (ID) ; Serious Emotional Disability (SED) ; Language based disabilities (Speech and Language and Autism Spectrum Disorders); Physical /Sensory Disabilities (H earing Impairment, Visual Impairment, Physical Disability and Ort hopedic Disability); Other (Traumatic Brain Injury, Other Health Impairment, and Multiple Disabilities) ; Significant Learning Disability (SED) Analysis T he data in this study represented categorical variables and did not meet the assumptions necessary to employ parametric statistical procedures. As such, basic descriptive

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! 11 statistics and nonparametric analyses were used including cross tabulations and contingency table analyses. The resea rch questions were as follows: 1. Is there a relationship between student dropout rate and category of disability in a large urban school district? A chi square analysis was used to examine the relationship between student dropout rates and disability catego ry. Because the dependent variable was measured on a dichotomous, categorical scale, a binomial logistic regression model was used to determine whether belonging to an individual disability category significantly predicted dropout. 2. In this school district, is the relationship between dropout and disability category affected by any of the following factors: socioeconomic status ( SES ), as measured by Free or Reduced Lunch status ); English as a second language (ESL ); race ; sex ; or grade retention history? Binary logistic models were constructed to determine the significance of risk factors and their impact on the relationship between disability category and dropout.

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! 12 CHAPTER IV RESULTS Demographics The demographics of the population were 34.3% female and 65.7% male. Just over 53% of the study sample qualified for free and reduced lunch, 12% had been retained/repeated a grade, and 39% were identified as English Language Learners The breakdown for rac e/ethnicity in the study sample was as follows: 1.1% American Indian or Alaskan Native, 1.7% Asian, 24.3% black 49.6% Hispanic, 19% white, .2% Native Hawaiian, and 4% for two or more races. Table 2: Characteristics of Study Population Dropout Total No Yes Race American Indian or Alaska Native 14 2 16 (1.1%) Asian 24 1 25 (1.7%) Black/African American 323 27 350 (24.3%) Hispanic/Latino 670 44 714 (49.6%) White 254 19 273 (19.0%) Native Hawaiian or Alaska Native 3 0 3 (0.2%) Two or more races 53 5 58 (4.0%) Sex Male 876 69 945 (65.7%) Female 465 29 494 (34.3%) Free or Reduced Lunch Yes 753 16 769 (53.4%) No 588 82 670 (46.6%) English Language Learner Yes 531 30 561 (39.0%) No 810 68 878 (61.0%) Grade Retention 0 1167 98 1265 (87.9%) 1 174 0 174 (12.0%)

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! 13 Research Question 1 Is there a relationship between student dropout rate and category of disability in a large urban school district? All 12 disability categories from Colorado Exceptional Ch ildren's Act were analyzed and overall dropout rate for the sample was 6.8% (Table.2). All group s except for SED, S LD, and TBI had disproportionately low number s of dropouts SLD (n=894) was a marginally overrepresented category for dropout comprising 62.1% of the total sample and 69.4% of the dropouts The disproportionality within SED was more striking; the group made up only 6.7 % of the study population but comprised 17.3 % of dropouts. Table 3: Disability Type x Dropout Crosstabs Dropout : No Yes Total Disability Type Intellectual Disability Count 109 3 112 % of Dropout Total 8.1% 3.1% % of Total 7.6% 0.2% 7.8% Serious Emotional Disability (SED) Count 80 17 97 % of Dropout Total 6.0% 17.3% % of Total 5.6% 1.2% 6.7% Specific Learning Disability (SLD) Count 826 68 894 % of Dropout Total 61.6% 69.4% % of Total 57.4% 4.7% 62.1% Hearing Impairment Count 19 0 19 % of Dropout Total 1.4% 0.0% % of Total 1.3% 0.0% 1.3% Visual Impairment Count 6 0 6 % of Dropout Total 0.4% 0.0% % of Total 0.4% 0.0% 0.4% Physical Disability Count 55 3 58 % of Dropout Total 4.1% 3.1% % of Total 3.8% 0.2% 4.0% Speech or Language Disability Count 24 1 25 % of Dropout Total 1.8% 1.0% % of Total 1.7% 0.1% 1.7% Multiple Disabilities Count 104 2 106 % of Dropout Total 7.8% 2.0% % of Total 7.2% 0.1% 7.4% cont'd on next page

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! 14 Table 2 cont'd Dropout: No Yes Total Disability Type Autism Spectrum Disorder (ASD) Count 53 1 54 % of Dropout Total 4.0% 1.0% % of Total 3.7% 0.1% 3.8% Traumatic Brain Injury Count 8 1 9 % of Dropout Total 0.6% 1.0% % of Total 0.6% 0.1% 0.6% Orthopedic Impairment Count 2 0 2 % of Dropout Total 0.1% 0.0% % of Total 0.1% 0.0% 0.1% Other Health Impairment (OHI) Count 55 2 57 % of Dropout Total 4.1% 2.0% % of Total 3.8% 0.1% 4.0% Total no. (%) 1341 (93.2) 98 (6.8) 1439 2 31.336 p = .001 Research Question 2 In this school district, is the relationship between dropout and disability category affected by any of the following factors: socioeconomic status (SES ) as measured by Free or Reduced Lunch status ; English as a second language (ESL ); race ; sex ; or grade retention history? For the binary logistic regression model, the SLD group was used as the reference category because it had the most proportional distribution of dropouts compared to the size of the group (62.1% of the study population and 69.4% of all dropouts) The SLD categ ory was also the largest disability category (n = 826) in the sample As described above, t he 12 disability categories were collapsed into 6 categories : Intellectual Disability (ID), Serious Emotional Disability (SED), Significant Learning Disability (SLD) Non Cognitive Disability, Language based Disability, and Other.

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! 15 When a binary regression model was constructed with disability category alone (Table 3) against dropout, the overall category was significa nt (p < .000 ). A mong the disability groups, only SED was significant ( p < 0.001) with an Exp ( B ) value of 2.581. This implies that a student in this cohort identified as SED is approximately 2.5 times more likely to drop out than a student in the reference category (SLD). Table 4: Summary of Binar y Logistic Regression Analysis with Disability Category Dropout Predictor 95% C.I.for EXP(B) B SE B e B Lower Upper Disability Group Intellectual Disability 1.096 .599 .334 .103 1.081 Serious Emotional Disturbance .948*** .295 2.581 1.447 4.605 Speech/Language Disabilities 1.154 .727 .316 .076 1.312 Physical/Sensory Disabilities .811 .601 .444 .137 1.444 Other Disabilities 1.011 .471 .364 .144 .916 Note: e B = exponentiated B For the race variable, White was the reference category. For the Disability group variable, Significant Learning Disability was the reference category. Reference categories were chosen based upon the variable with the most representative distribution of dropout. p < .05. ** p < .01. *** p < .001. When a binar y logistic regression model was constructed with disability groups and the potential confounding independent variables (free or reduced lunch, retention status, sex, ESL status and race ; Table 4) disability category remained significant (p < .004). Of t he potential confounders, only free or reduced lunch status had a statistically significant result (p < .000, Exp(B) = .157). This indicates that students without free and reduced lunch status were 15.7 % as likely to drop out as students who qualif ied for free or reduced lunch. As with the first model described above, the only category within disability type that was a significant

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! 16 predictor of dropout was SED (p < .022, Exp(B) = 2.091. This suggests that while free or reduced lunch status does predict dro pout to a degree independently, it does not fully account for the disproportionality in dropout among students with SED. Grade retention status was not significant i n the regression model. However, as seen in Table 1, while there were 174 students who were retained, no one in this group dropped out. In order for t he logistic regression model to determine sig nificance or odds ratio for a variable, each group within the variable needs to have a non zero number of even ts in each category of the dependent variable. In this case, the model was unable to determine significance or odds because all dropouts were in the group who were not retained. However results of a Chi square analysis indica te that the distribution of d ropouts among students based upon their retention status is very unlik ely due to chance alone (p = .000 ).

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! 17 Table 5: Summary of Binary Logistic Regression Analysis with All Variables Dropout Predictor 95% C.I.for EXP(B) B SE B e B Lower Upper Free or Reduced Lunch 1.851*** .288 .157 .089 .276 Grade Retention 18.274 2836.64 .000 .000 Sex .183 .239 1.201 .752 1.919 ESL Status .109 .323 .897 .476 1.688 Race American Indian or Alaska Native 1.543 .859 4.681 .869 25.200 Asian .179 1.102 .836 .096 7.243 Black .284 .327 1.328 .700 2.521 Hispanic Latino .228 .356 1.256 .626 2.523 Two or More Races .333 .550 1.396 .475 4.103 Native Hawaiian or other Pacific Islander 17.929 22881.61 .000 .000 Disability Group Intellectual Disability .810 .614 .445 .133 1.482 Serious Emotional Disturbance .737* .323 2.091 1.111 3.935 Speech/Language Disabilities 1.313 .744 .269 .063 1.157 Physical/Sensory Disabilities .942 .611 .390 .118 1.292 Other Disabilities .835 .484 .434 .168 1.121 Note: e B = exponentiated B For the race variable, White was the reference category. For the Disability group variable, Significant Learning Disability was the reference category. Reference categories were chosen based upon the variable with the most representative distribution of dropout. p < .05. ** p < .01. *** p < .001.

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! 18 CHAPTER V DISCUSSION This study aim ed to determine if a specific disability category acted as a significant predictive factor lead ing t o drop out among youth with I ndividual ized E ducation P lan s (IEPS) in a large urban school district When controlling for race, sex, grade retention, E nglish language proficiency (E SL ) socioeconomic status ( SES ) as measured by free or reduced lunch status, and disability c ategory three major factors relating to d ropout among students with IEP s emerged. Th ese factors are : students with a n identification of Serious Emotional Disability (SED) students who do not qualify for free and reduced lunch and students who were never retained /repeated a grade S t udents identified with S erious Emotional Disability (SED) or who do not qualify for free and reduced lunch we re more likely to drop out than other students with IEP s in this large urban school district. The results demonstrating a relationship between dropping out and not being retained are contrary to previous research ( e.g., Zablocki & Krezmien, 2013; Sullivan & Sadah, 2016) This was an u nexpected finding that may be i s olated to this particular sample There are no district wide dropout and retention data for the district s general population ; thus a comparison between the groups to s ee if this was a district trend was not possible. While there is an overwhelming amount of literature indicating that retention can often lead to poor outcomes such as drop out ( Jimerson, Anderson, & Whipple. 2002) t here are some factors that lead to positive outcomes among retained students, such as higher self esteem, positive school behavior, and academic success (Jimerson Ferguson, Whipple, Anderson, & Dalton. 2002). T he groups that were most likely to be retained were students with intellectual disability, specific learning disability ( SLD ) and multiple disabilities It is possible that

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! 19 children within thes e groups pos sessed more supports and/or protective factors than their counterparts. The results indicating that the likelihood of drop ping out was increased in students who did not qualify for free or reduced lunch are contrary to previous research (e.g., Zablocki & Krezmien, 2013; Sullivan & Sadah, 2016) T his school district had a significantly lower rate (approximately 75% lower) of dropout among students with free or reduced lunch than other nearby large urban districts ( CDE, 2016 ). It is possible that free or reduced lunch status may serve as a protective factor because the program incentivize s students to attend school to gain access to food. I n his research on the impacts of the Nation al School Lunch program Hinrichs (2010) found that the program had increas ed educational attainment and that the free or reduced lunch program may have prompted children to attend school. The finding that this district s SED population was twice as likely to drop out than students with other disability categories is consistent with the literature and national trends in dropout among students with disabilities ( Sullivan & Sadah, 2016; NLTS 2, 2005; NCES, 2015). One possible explanation for the disproportionally high rate of dropout among students with SED is that the re do not appear to be dropout prevention programs in place tailored to this specific population. In their review of the literature, Sullivan & Sa dah (2016) were able to find only one dropout prevention program in the U.S. targeted to stude nts with emotion al disturbance. The program, Check and Connect did not show a significant effect on the likelihood of drop ping out but it did reduce some risk factors for dropout such as truancy and mobility (Sullivan & Sadah, 2016). Another potential barrier to successful school completion for students with SED is the fact that they are hospitalized or placed in alternate education programs a t a higher rate than

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! 20 their peers and thus have more extended periods of absence from school ( Clemens, Welfare, & Williams, 2011 ). When they return to the school environment, they are expected to reengage with their learning in the absence of specialized support. The literature is limited in terms of the specific impact of hospitalization o n stude nt engagement upon reentry to school for individuals with SED but research related to children returning to school following medical hospitalizations indicates that students can experience many barriers including social isolation, trouble with medical management, and negative school experiences ( Clemens et al. 2011 ). Given that on e of the driving factors for dropout is disengagement ( Sullivan & Sadah, 2016; Finn, 1989 ) it is likely that when students w ith emotional disturbance miss school due to hospitalizations, they are at increased risk of dropout. Limitations The current study has several limitations that need to be considered when examining the findings presented. First, t he data used in this s tudy were collected by the school district and include all students with disabilities in grades 7 12 As a third party collected these data, it is possible that the validity of the data set contains inconsistent documentation, error, or bias. Second, t his study focused solely on an urban school district and did not investigate rural or suburban environments. As such, it may not be appropriate to generalize results outside of the urban setting Further, schools provide services and supports to students t hat impact the likelihood that students will complete school and graduate. These data do not account for such interventions, which could have a variety of impacts, good and bad, and could skew findings one way or the other. Another limitat ion may be proble ms with intersect iona l ity ; that is that individuals who drop out may belong to more than one risk group population (i.e., both SED, and low SES ) and the significance of the relationships between population

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! 21 membership and disability categ ory were suppresse d in the binary analysis. Conclusion and Implications I t has been well established that students with disabiliti es especially emotional disabilities are at higher risk of drop out than their typical peers, and thus it is critical that future research focus es on the development of interventions to remediate this disproportionality. Additionally, m uch of the literature on free or reduced lunch program s has focus e d on nutritional benefits rather than the program's impacts on dropout and school engagement As free or reduced lunch status was found to be a protective factor in this population, further exploration into this relationship should be undertaken. I nvestigation into possible protective factors for children with SED, impacts of increased supports and engagement in elementary school, and the efficacy of programs that support youth with emotional disabilities at the secondary level is crucial As stude nts with SED are a lready being served under IEPs and often receive services from school psychologists, we are well situated to develop and implement programming and interventions to remediate dropout for this group. One such promising program is the Bridge for Resilient You th in Transition P rogram (American Psychological Association, 2014) which began in Brookline, MA and aims to ease the transitional and reintegration process following mental health institutionalization This program has been expanded to 17 school systems in Massachusetts and a promising 95% on time graduation rate for the 1500 students who have been served by the program is reported ( APA 2014) As we strive for more equitable and just outcomes among all students it is imperative that school psychologists be made aware of the issue of dropout among students with SED early on in order to lay the foundation of engagement for these students. It will only be through continued action that we can help these students

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! 22 overcome their challenges so that the y may atta in school completion.

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! 23 REFERENCES American Psychological Association. ( 2014 ). APA Gold Achievement Award: A s chool b ased t ransition p rogram for a dolescent r eturning to h igh s chool a fter a m ental h ealth e mergency Psychiatric Services 65 :11, e6 e8 Barrett, D. E., Katsiyannis, A., Wilson, V., & Zhang, D. (2007). Truancy offenders in juvenile justice system: Examinations of first and second referrals. Remedial and Special Education, 28, 244 256. Campbell, C., Davidson II, W. S., Onifade, E., & Smith Nyandoro, A. (2010). Truancy and patterns of criminogenic risk in a young offender population. Youth Violence and Juvenile Justice, 8 (1), 3 18. Carter, S. D., Evans, E. W., Hendricks, M. A., McKinley, L., & Sale, C. J. (2010). Evaluation of a truancy court intervention in four middle schools. Psychology in the Schools, 47 (2), 173 193. Check & Connect: A Comprehensive Student Engagement Intervention. (2015) Institute of Community Integration, University of Minnesota. Retrieved from http://checkandconn ect.umn.edu/model/default.html Clemens, E. V., Welfare L. E. & Williams, A. M., (2011). Elements of successful school reentry after psychiatric hospitalization. Preventing School Failure: Alternative Education for Children and Youth, 55, 202 213. Color ado Department of Education (2017) Definitions of s elected terms Retrieved from https://www.cde.state.co.us/cdereval/rvdefine Colorado Department of Education. (2016) 2015 2016 Drop o ut Rates by d istrict and i nstructional s ervice p rogram t ype Retrieved from https://www.cde.state.co.us/cdereval/dropoutcurrent Dorn, S. (1996). Creating the dropout: An institutional and social history of school failure Praeger: Westport, CT. Finn, J. (1989) Withdrawing from school. Review of Educational Research. 59 (2) 117 142 Retrieved from http://gse.buffalo.edu/gsefiles/documents/alumni/Fall09_Jeremy_Finn_Withdrawi ng. pdf Gonzalez. E, Kennedy. P, & St. Julien. T (2009). Dropout p revention: History, p olitics, and p olicy. Retrieved from http://a100educationalpolicy.pbworks.com/f/Dropout+Prevention.Compiled.pdf Hammond. C., Smink. J., & Drew. S. (2007 ). Dropout r is k f actors and e xemplary p rograms:

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! 24 A t echnical r eport National Dropout Prevention Center. D. Linton: Communities i n Schools, Inc.. Retrieved from http://dropoutprevention.org/resources/major research reports/dropout risk factors and exemplary programs a technica l report/ Hendricks, M. A., Sale, E. W., Evans, C. J., McKinley, L., & DeLozier Carter, S. (2010). Evaluation of a truancy court intervention in four middle schools. Psychology in the Schools, 47 (2), 173 183. Hinrichs, P. (2010) The effects of the National School Lunch Program on education and health. The Journal of Policy Analysis and Management 29 479 505. doi:10.1002/pam.20506 Jimerson, S. R., Anderson, G., & Whipple, A. (2002). Winning the battle and losing the war: Examining the relation betw een grade retention and dropping out of high school. Psychology in the Schools 39 (4), 441 457 Jimerson, S. R., Ferguson, P., Whipple, A. D., Anderson, G. E., Dalton, M. J. (2002). Exploring the a ssociation b etween g rade r etention and d ropout: A l ongitudinal s tudy e xamining s ocio e motional, b ehavioral, and a chievement Characteristics of r etained s tudents. California School Psychologist 7 51 62. Kaplan. D.S., & Damphousse.K.R. (1994) Mental health implications of not graduating from high school Journal of Experimental Education 62 T ( 2 ) 105 124 Kortering. L. J., & Braziel, P M. (1999) School d ropout from the perspective of former students. R emedial and S ocial E ducation, 20 (2), 78 83. Krueger, J., Fritz, P., & Martinez, J. (2013) Colorado Department of Education 2011 2012 s tate p olicy r eport d ropout p revention and s tudent e ngagement Retrieved from http://www.boarddoc s.com/co/cde/Board.nsf/files/96DULC7C8EA7/$file/2012_DPS E_Legislative_Report.pdf Martinez, J., & Parra, J. (2015) Colorado Department of Education 2013 2014 s tate p olicy r eport d ropout p revention and s tudent e ngagement Retrieved from https://www.cde.state.co.us/dropoutprevention/2014statepolicyreportv2 National Center for Learning Disabilities (2014). The state of l earning d isabilities Retrieved from : https://www.ncld.org/wp content/uploads/2014/11/2014 State of LD.pdf Pyle, N., & Wexler, J. (2012). Preventing students with disabilities from dropping out. Intervention in School and Clinic, 47 (5), 283 289. Retrieved from http://0 search.proquest.com.skyline.ucdenver.edu/docview/1312420733?accountid=14506 Sul livan, A., & Sadeh. S. (2016). Does the e mpirical l iterature i nform p revention of d ropout among s tudents with e motional d isturbance? A s ystematic review and c all to a ction. Exceptionality, 24 ( 4 ) 251 262 doi:10.1080/09362835.2016.1196440

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! 25 U.S. Department of Education, National Center for Education Statistics. (2015). The c ondition of d ducation 2015 (NCES 2015 144), Status d ropout r ates U.S. Department of Education, National Center for Ed ucation Statistics. (2016). The c ondition of e ducation 2016 (NCES 2016 144), s tatus d ropout r ates Vaughn, M. G., Salas Wright, C. P., & Maynard, B. R. 2014). Dropping out of school and chronic disease in the United States. Journal of Public Health, 22 265 270. D oi : 10.1007/s10389 014 0615 x Wagner, M., Newman, L., Cameto, R., Garza, N., and Levine, P. (2005). After high school: A first look at the postschool experiences of youth with disabilities. A report from the National Longitudinal Transition Study 2 (NLTS2) Menlo Park, CA: SRI International. Available at www.nlts2.org/reports/2005_04/nlts2_report_2005_04_complete.pdf. Zablocki M., & Krezmien, M. P. (2013). Drop out predi ctors among students with high incidence disabilities: A national longitudinal and transitional study 2 analysis. Journal of Disability Policy Studies, 24 (1), 53 64. doi:http://0 d x.doi.org.skyline.ucdenver.edu /10.1177/1044207311427726