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Youth and depression : is social capital a determining factor?

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Title:
Youth and depression : is social capital a determining factor?
Creator:
Romo, Tattiana
Place of Publication:
Denver, CO
Publisher:
University of Colorado Denver
Publication Date:
Language:
English

Thesis/Dissertation Information

Degree:
Doctorate ( Doctor of philosophy)
Degree Grantor:
University of Colorado Denver
Degree Divisions:
Department of Psychology, CU Denver
Degree Disciplines:
Clinical health psychology
Committee Chair:
Everhart, Kevin
Committee Members:
Borrayo, Evelinn
Kilbourn, Kristin
Cordova, David

Notes

Abstract:
Adolescence is a critical developmental period which consists of physical, cognitive, and social changes. These changes have the potential to lead to mental health risks such as depression. Although there are evidence-based interventions targeting adolescent mental health, the prevalence of depression continues to rise, which has led researchers to studying alternative psychosocial factors that may inform the development of future interventions, such as exploring social capital. A hierarchical multiple regression was performed to test whether social capital, coping, gender, and ethnicity were significant predictors of depression. In terms of model fit, the final regression model created in predicting depression was statistically significant (F(10, 186) = 5.430, p = .000). This indicated that the regression model with the social capital, coping, and gender, had an acceptable model fit. Examination of the unstandardized beta coefficient (β) showed that social capital (β = -0.975) had a significant negative predictive relationship with depression, which indicated that youth who reported higher social capital endorsed fewer depression symptoms. Additionally, gender (β = 4.655) had a significant positive predictive relationship with depression symptoms, indicating females experienced more depression symptoms compared to males, indicating females experienced more depression symptoms compared to males. Coping, ethnicity, and interactions were not significant in the hierarchical multiple regression model.

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University of Colorado Denver
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Auraria Library
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Copyright Tattiana Romo. Permission granted to University of Colorado Denver to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.

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Full Text
YOUTH AND DEPRESSION: IS SOCIAL CAPITAL A DETERMINING FACTOR?
by
TATTIANA ROMO B.A. Colorado Mesa University, 2010 M.A. University of Colorado Denver, 2015
A dissertation submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Clinical Health Psychology Program
2019


©2019
TATTIANA ROMO
ALL RIGHTS RESERVED


This dissertation for the Doctor of Philosophy degree by
Tattiana Romo has been approved for the Clinical Health Psychology Program by
Kevin Everhart, Chair Evelinn Borrayo, Advisor Kristin Kilbourn David Cordova
Date: August 3, 2019


Tattiana Romo (Ph.D., Clinical Health Psychology)
Youth and Depression: Is Social Capital a Determining Factor?
Dissertation directed by Evelinn Borrayo
ABSTRACT
Adolescence is a critical developmental period which consists of physical, cognitive, and social changes. These changes have the potential to lead to mental health risks such as depression. Although there are evidence-based interventions targeting adolescent mental health, the prevalence of depression continues to rise, which has led researchers to studying alternative psychosocial factors that may inform the development of future interventions, such as exploring social capital. A hierarchical multiple regression was performed to test whether social capital, coping, gender, and ethnicity were significant predictors of depression. In terms of model fit, the final regression model created in predicting depression was statistically significant (F(10, 186) = 5.430, p = .000). This indicated that the regression model with the social capital, coping, and gender, had an acceptable model fit. Examination of the unstandardized beta coefficient (P) showed that social capital (P = -0.975) had a significant negative predictive relationship with depression, which indicated that youth who reported higher social capital endorsed fewer depression symptoms. Additionally, gender (P = 4.655) had a significant positive predictive relationship with depression symptoms, indicating females experienced more depression symptoms compared to males, indicating females experienced more depression symptoms compared to males. Coping, ethnicity, and interactions were not significant in the hierarchical multiple regression model.
The form and content of this abstract are approved. I recommend its publication.
Approved: Evelinn Borrayo
IV


DEDICATION
This dissertation is dedicated to my loving family. Sergio and Maria Romo, thank you for all the sacrifices you had to make to ensure that I could have a great future. I would not have made it here without your encouragement, support, and love. You have always believed in me and I am truly grateful for that support. John Heuton, I love you immensely. You push me to become a better person every day. I appreciate the nights you would stay up and work with me. Thank you for helping me believe that I am smart, capable, and worthy of a PhD. Milly, Katherine, Alex, and Andrew, I hope that you can value and appreciate the importance of an education. I hope you set your minds on becoming better educated than your parents and pass along the desire and determination to gain knowledge and wisdom. My PhD was hardly easy to achieve, and I wanted to quit several times, but it was important to surround myself with loving and supportive family and friends because sometimes all you have to hear is, "ya mero mija" "almost there baby." Sergio and Deedee Romo, I hope I made you proud, a sister cannot ask for better loving supportive siblings. This is for our children; I hope the next generation of children in our family believe that they can accomplish their dreams with hard work and determination. I may be the first female PhD in our family, but I will not be the last.
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ACKNOWLEDGEMENTS
I would like to start off by acknowledging my advisors, Dr. Evelinn Borrayo and Dr. Kevin Everhart. Dr. Borrayo, thank you for giving me the opportunity to join your lab early in my graduate training. I appreciate you hanging on through my thesis and now my dissertation. I am truly grateful for the patience and direction you have provided. Dr. Kevin Everhart, thank you for taking me under your wing. I appreciate you not giving up on my training when things got tough and for providing support to get me through the next phases of this career. Dr. David Cordova, thank you for joining my dissertation committee and providing your knowledge and guidance throughout this study. Dr. Kristin Kilbourn, you have been a great mentor and support. Thank you for being such a positive clinical supervisor. I am truly grateful for the guidance you have all provided. Dr. Jo Vogeli, Dr. Lacey Clement, and Dr. Kaile Ross, you are such amazing women and I feel so proud to call you colleagues. I am so proud and always so interested in what you are doing with your lives. I hope that we remain lifelong friends.
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TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION.........................................................1
Adolescent and Youth Development.....................................1
Depression among Youth...............................................3
Gender and Depression................................................8
Ethnicity and Depression............................................10
Social Capital Theory...............................................13
Social Capital and Mental Health....................................15
Social Capital Measurement Issues...................................18
Summary and Proposed Study..........................................19
II. METHOD..............................................................22
Participants........................................................22
Recruitment and Data Collection.....................................22
Measures ...........................................................23
Variables...........................................................23
Data Cleaning.......................................................26
Data Analysis ......................................................26
III. RESULTS..........................................................30
Hypothesis 1: Coping................................................30
Hypothesis 2: Social Capital........................................30
Hypothesis 3: Social Capital and Gender.............................31
Hypothesis 4: Social Capital and Ethnicity..........................31
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Multicollinearity
32
IV. DISCUSSION AND CONCLUSIONS.........................................34
Intervention and Policy.............................................37
Study Limitations...................................................39
Future Directions...................................................40
REFERENCES ...............................................................52
APPENDIX
A. Demographic Questionnaire ......................................67
B. Center for Epidemiologic Studies-Depression Scale (CES-D).......70
C. Social Capital Questions........................................73
D. Coping Questions.................................................76
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LIST OF TABLES
TABLE
1. Demographic Characteristics.......................................................42
2. Skewness andKurtosis Statistics of Depression Score...............................43
3. Hierarchical Multiple Regression Results..........................................44
4. Tolerance and VIF Statistics of Predictors of Depression..........................48
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LIST OF FIGURES
FIGURE
1. Scatterplot of standardized predicted values....................................49
2. Normal probability plot of residuals in predicting depression...................50
3. Histogram of depression symptoms................................................51
x


LIST OF ABBREVIATIONS
COMIRB
CES-D
DSM-V
Colorado Multiple Institutional Review Board Center for Epidemiologic Studies-Depression scale Diagnostic Manual of Mental Disorders, Fifth Edition


CHAPTER I
INTRODUCTION Adolescent and Youth Development
The World Health Organization (2011) defines youth as individuals between the ages of 10 and 24 years of age. For the purpose of this study, the World Health Organizations definition of youth will encompass the population of interest, however, an understanding of adolescent development is warranted to comprehend the challenges youth may encounter biologically, socially, and psychologically. Adolescent and youth terms will be used interchangeably when addressing the literature of this population. Adolescence is defined as the second decade of life, a period in which a child transitions into adulthood.
Adolescence is an important developmental period which can be conceptualized into three stages: Early (10-13 years), middle (14-16 years), and late (17-19 years) (Kar, Choudhury, & Singh, 2015). Physical, cognitive, and emotional changes take place during this sensitive time period (Seifert, & Hoffnung, 2000). During this time children gain about 50% of their adult body weight, are capable of reproducing, and experience significant changes in their brains (McNeely & Blanchard, 2009). Adolescents may also become particularly concerned about their body image, as they reach puberty. Physical changes can vary throughout adolescence which may cause stress and anxiety, although very normal for this time period (Seifert, & Hoffnung, 2000). What may be stressful for adolescents are the competing stressors such as the pressure they feel to develop sexual identities, relationships, and a strong desire to feel accepted (McNeely & Blanchard, 2009).
Between the ages of 10-14 years old a sense of identity begins to develop and therefore an increased self-focus begins to become more obvious. Relationships also change
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in that friendships appear to become more important than relationships with their parents (McNeely & Blanchard, 2009). Abstract thinking, reasoning, and decision-making is a critical element of the changes occurring during this development period (Seifert, & Hoffnung, 2000). Adolescents continue to develop a more solid personal identity, increase their independence, become more concerned for others, increased ability for delayed gratification, and social networks typically expand with newly formed friendships (McNeely & Blanchard, 2009). Cognitive changes involve the ability to think in abstract terms, debating, challenging ideas or values, and questioning (which help shape their ability to judge and solve problems), goal setting, and thinking about their future (McNeely & Blanchard, 2009). Advanced reasoning skills help adolescents think about hypotheticals and logical thought processes which are important to decision-making. For example, abstract thinking may embrace contemplating thoughts regarding faith, love, trusts, beliefs, spirituality, (McNeely & Blanchard, 2009). Finally, metacognition is an important cognitive development during this period which allows for adolescents to think about how they are feeling, what they are thinking, and pondering about how they are perceived by others. Increased cognitive development is followed by an increase in emotional competence (McNeely & Blanchard, 2009).
Emotional and social competences are learned skills that help adolescents navigate their emotions and how they relate with others. Social competence development is also important to peer relationships which tend to take importance over relationship and time with family (McNeely & Blanchard, 2009). For example, peers generally influence values, attitudes, behaviors, identity, and relationships. Adolescents develop their ability to label their emotions, such as recognizing anxiety or sadness in relevant situations (Seifert, &
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Hoffnung, 2000). Difficulties may arise when lack of awareness of feelings inhibits adolescent’s ability to figure out ways to resolve problems that may explain these feelings (McNeely & Blanchard, 2009). With significant changes to emotional development, it is estimated that one in ten adolescents may experience a serious emotional disturbance that can affect their ability to function at home, school, or their community. Depression has been identified as one of the most common mental health disorders in adolescence (McNeely & Blanchard, 2009). According to the Diagnostic Manual of Mental Disorders, Fifth Edition (DSM-V) depression may include symptoms of sadness, lack of interest and pleasure in daily activities, difficulties with sleep, significant weight changes, lack of energy, inability to concentrate, feelings of worthlessness and/or guilt, and recurrent thoughts of death or suicide (American Psychiatric Association, 2013). According to the Diagnostic Manual of Mental Disorders, Fifth Edition (DSM-V) depression may include symptoms of sadness, lack of interest and pleasure in daily activities, difficulties with sleep, significant weight changes, lack of energy, inability to concentrate, feelings of worthlessness and/or guilt, and recurrent thoughts of death or suicide (American Psychiatric Association, 2013). It is important to note the difference between diagnosed depression in the literature and depression symptoms, studies have explored both given the benefits and challenges of measuring depression and depression symptoms. This study will focus on depression symptoms; however, the literature will include studies that have measured depression either way.
Depression among Youth
Experiencing a wide range of emotions is expected during adolescent development. However, mental health disorders characterized by symptoms affecting emotions, cognitions, and behaviors can also have an impact on and interfere with daily functioning (National
3


Institute of Mental Health, 2016). During adolescence, youth are preoccupied with having to build an understanding around healthy relationships, exploring one’s interests and identity, learning and developing important life skills, entering the workforce, and depression during this time can seriously affect further successful development. For example, long-term socioeconomic standing, peer, familial, and romantic relationships may all be negatively influenced by depression experienced during adolescence (Claybome, Varin, & Colman, 2019). According to the 2016 National Survey on Drug use and Health, 12.8% of the U.S. adolescent population aged 12 to 17, an estimated 3.1 million, indicated that they experienced at least one major depressive episode. Additionally, approximately 9.0% of U.S. adolescents reported at least one major depressive episode with severe impairment, which is an estimated 2.2 million adolescents. Approximately 60% of adolescents who reported a major depressive episode indicated that they did not receive treatment for their depression. The U.S. Department of Health & Human Services (2018) advised that youth with depression can experience problems with their school work, relationships, and health if left untreated. Of concern is that the prevalence rates for youth who are experiencing mental health illness may be underrepresented given that youths who are not seriously impaired may be excluded from meeting the criteria for depression which may result in lower prevalence rates reported (Novak, Colpe, Barker, & Gfroerer, 2010).
As children transition into adolescence the risk for depression greatly increases. Limited data on the trends in prevalence of depression in adolescents and young adults warranted a study exploring the national trends in a 12-month prevalence of major depressive episodes (Mojtabai, Olfson, & Han, 2016). The study examined cross-sectional data from the U.S. population gathered from the National Surveys on Drug Use and Health from 2005
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to 2014. Participants included adolescents aged 12 to 17 and adults 18 to 25 years. The results of this study indicated that during the 12-month prevalence of major depressive episodes in adolescents and young adults, there was an increase from 8.7% to 11.3% (2005 to 2014) in adolescents and from 8.8% to 9.6% in young adults. The increase was significant only in the age ranged from 12 to 20 years.
Addressing mental health disorders such as depression in youth is important because once established such disorders become risk factors for adult psychopathology (Bluth, Campo, Rutch, & Gaylord, 2017). In addition, several studies have found that experiencing depression in early life is associated with poor outcomes such as being at risk of obesity, type 2 diabetes, and comorbid mental health disorders (i.e. anxiety and substance use) (Clayborne, Varin, & Colman, 2019). Goodman and Whitaker (2002) conducted a longitudinal study to determine whether depressed mood predicted the development of and perseverance of obesity in adolescents. Their sample consisted of 9,374 adolescents in grades 7-12th grade that completed the National Longitudinal Study of Adolescent Health. Baseline data were gathered followed by depressed mood and body mass index information at 1-year follow-up. Goodman and Whitaker's (2002) findings supported their hypotheses and found that depressed adolescents were at an increased risk for development and continuance of obesity. Adolescent depression has not only demonstrated the health consequences, but studies have also provided support for psychosocial outcomes (Clayborne, Varin, & Colman, 2019).
Psychosocial outcomes influenced by early-life depression include lower educational attainment, unemployment, and lower perceived social support (Fergusson & Woodward, 2002; Galambos, Zeng, Sethilselvan, & Colman, 2002). Fergusson and
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Woodward (2002) conducted a longitudinal study of New Zealand children to examine to what extent adolescents (ages 14-16) with depression develop an increased risk of subsequent mental disorders (e.g. substance use), academic underachievement, and reduced life opportunities. The researchers found that youth with depression were significantly at risk of developing major depression, anxiety disorders, nicotine dependence, alcohol abuse or dependence, suicide attempt, educational underachievement, unemployment, and early parenthood. Results of associations were similar for males and females. However, Fergusson and Woodward (2002) highlight that the evidence of their study does not indicate that the psychosocial adverse outcomes are a consequence of early depression, but instead that they develop as a result of common social, familial, and personal factors (e.g. parental change, maternal educational underachievement, childhood sexual abuse, IQ, etc.). The authors emphasize the context of a young person's life history, social, and personal circumstances when looking at early depression.
Clayborne, Varin, and Colman (2019) conducted a systematic review and metaanalysis on youth depression and long-term psychosocial outcomes. The study gathered articles published from 1980 through 2017 from five databases. The researchers wanted to combine the data investigating the relationship between youth depression and long-term psychosocial outcomes; educational attainment, income, employment, pregnancy/parenthood, marital and relationship status, social support, and loneliness. The results from this metaanalysis found that youth depression was indeed associated with outcomes such as failure to complete secondary school, unemployment, and pregnancy/parenthood. These studies highlight the importance of targeted mental healthcare early in youth development to reduce these adverse psychosocial outcomes. The authors highlight the complexity of the processes
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relating depression and psychosocial outcomes. For example, given the noteworthy functional impairment caused by depression, schoolwork may become too difficult to comprehend, which in turn can affect how an adolescent performs and therefore affect their educational attainment. Similarly, lack of educational attainment can influence employment opportunities (Lewinsohn, Rohde, Seeley, Klein, & Gotlib, 2003). Depression may also affect school attendance as highlighted by a study conducted by Suris, Michaud, and Viner (2004), which can influence educational attainment and the formation and maintenance of relationships because of the reduced time spent with peers. The consequences of early depression can be seen throughout the lifespan such as the negative impact on social impairment may affect the development of stable relationships and social networks which may also influence workplace advancement in their future (Bums, Fitzpatrick, Pinfold, & Priebe, 2007; Sandberg-Thoma & Kamp Dush, 2014). Poorer marital and relational functioning may be the result of socioeconomic consequences of depression as demonstrated by several studies (Bhoman, Hjern, et al., 2011; Bemdt, Koran, Finkelstein, et al., 2000; Jonsson, Bohman, Hjern, von Knorring, Olsson, & von Knorring, 2010). Studies have shown the persistence of adolescent depression into adulthood, which may consist of recurrent bouts of depression, can also be stressful for partners and in turn lead to relationship problems, dissolution, and/or divorce (Rehman, Gollan, & Mortimer, 2008). As extensively highlighted, depression experienced during an important developmental period, adolescence, may have serious adverse consequences leading towards a future complicated by poor psychosocial outcomes compared to non-depressed peers. Identifying and targeting at-risk sub-groups is crucial in the prevention and treatment of youth depression, given that the research has identified associations with school failure, alcohol and substance use disorders,
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and suicide. Suicide being the third leading cause of death among adolescents ages 15-19 (Grose et al., 2014). Continual efforts towards understanding the effects of adolescent depression can inform future interventions.
Unfortunately, recent studies have found that there have not been many significant changes in mental health treatment among youth (Curtin, Warner, & Hedegaard, 2016).
With little change in mental health treatments, youths’ untreated depression continues to rise. Understanding what factors may be protective for youth mental health may be helpful in the development of future mental health treatments tailored for youth. Hayden and Mash (2014) identified interconnected and shared protective factors for psychopathology such as community factors (i.e., caring community relationships and positive role models), psychological factors (i.e. high self-esteem and self-efficacy, positive coping strategies, high distress tolerance, and resilience), and familiar factors (i.e. availability of resources, positive parenting, and spiritual beliefs). Treatment and interventions for depression have predominantly consisted of psychotherapeutic medications and evidenced based therapies (Mark, Levit, Buck, Coffey, & Vandivort-Warren, 2007). Mark et al. (2007) highlights the need to examine other treatment strategies given the concerns regarding utility and validity of current treatments to address mental health illness.
Gender and Depression
Studies have identified inclinations in major depressive episodes differing among males and females. Mojtabai, Olfson, and Han (2016) found that their study aligned with previous findings on the larger increases in depressive symptoms experienced in females compared to males. Additionally, Curtin, Warner, and Hedegaard (2016) found greater increases in suicide among female youth compared to males. The prevalence of major
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depressive episode was higher among female youth (19.4%) compared to males (6.4%) (National Institute of Mental Health, 2016). Psychosocial factors and biological changes due to puberty explain the higher vulnerability females have for depression (Lewis, Kremer, Douglas, Toumborou, Hameed, Patton, & Williams, 2015). However, the rise in female depression despite biological vulnerability has given way for researchers to identify social, cultural, and historical factors that influence depressive symptoms (Lewis, Kremer, Douglas, Toumborou, Hameed, Patton, & Williams, 2015). For example, researchers hypothesize that female youth may be exposed to greater depression risk factors in the recent years due to cyberbullying and increased use of mobile phones (Mojtabai, Olfson, and Han, 2016). As previously discussed, adolescents become more self-focused given their increased metacognitive ability, particularly for females, this inwardly directed focus may lead to increases in rumination which has also been linked to depression and anxiety (Nolen-Hoeksema, 2000). In addition, females tend to experience significant increases in stressors and stressful life events during adolescence, which in turn, may contribute to vulnerability to depressive symptoms (Hankin, Mermelstein, & Roesch, 2007).
A recent study conducted by Malooly, Flannery, and Ohannessian (2017), surveyed 905 adolescents in the U.S. to understand gender and racial differences in depressive symptomatology and coping. The results indicated that female youth reported more depressive symptomology than males and were more likely to engage in coping strategies such as seeking emotional social support, instrumental social support, and venting emotions. As previously highlighted, late adolescence is a critical period in which adolescents are at a higher risk, studies show a six fold surge of first episodes of clinical depression (Hankin and Abramson, 2002, Grose et al., 2014). The literature on gender
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differences in depression have been supported, furthermore, Hankin and Abramson (2002) add that there are also important ethnic differences in depression to consider.
Ethnicity and Depression
Although studies show that non-Hispanic White youth have the highest suicide rates, the incidence has been growing for racial and ethnic minorities (Balis & Postolache, 2009). Hankin and Abramson (2002) found that there are also important ethnic differences in depression. The researchers note that while non-Hispanic White adults have the highest rates of suicide, minority adolescents’ depressive symptomology may be differentially impacted by gender, race and ethnicity (Sen, 2004). The literature on racial and ethnic differences in depressive symptoms has been inconsistent (Maag & Irvin, 2005). Malooly, Flanery, and Ohannessian’s results did not find significant differences based on race/ethnicity. However, the authors noted that when racial/ethnic differences have been examined, most of the studies included populations consisting of non-Hispanic White and African-American adolescents (Malooly, Flannery, & Ohannessian, 2017).
According to the 2016 National Survey on Drug use and Health, the prevalence of major depressive episode was highest among adolescents reporting two or more races (13.8%) compared to non-Hispanic White adolescents. Even when controlling for family structure, socioeconomic status, and community ethnic composition, Hispanic and African American adolescents are still more likely to experience symptoms of depression compared to Caucasian adolescents (Wight et al., 2006). Mojtabai et al.'s (2016) study found that Hispanic and non-Hispanic White adolescents had greater prevalence of depression compared to Black and Asian adolescents. However, their findings concluded that biracial adolescents had the highest rates of depression (Mojtabai et al., 2016). Studies have explored
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reasons for ethnic and racial differences in depression and have found that symptoms of depression may be linked with discrimination and/or victimization which may also complicate detecting symptoms of depression among racial and ethnic minority populations (Stockdale, Lagomasino, Siddique, McGuire & Miranda, 2008). Studies have found that there may be a relationship among rates of increasing racial discrimination in the U.S. and mental health disparities (American Psychological Association, 2016). Racial identity development during this critical developmental period can greatly impact mental health of adolescents.
Cross and colleagues proposed Black identity development models which focus on racial identity development (Cross Jr. & Strauss, 1998). Their theories led to further identity development models that were applicable to Latino/Latina, Asian American, disabled, feminist, and gay and lesbian (Cross Jr. & Strauss, 1998). Generally, these earlier racial identity models described an early stage in which the individual has a discrimination encounter from which psychological well-being may follow if the individual develops a positive racial or ethnic identity and may resolve the distress experienced from this encounter (Cross, 1995). Helms (1995) developed the Person of Color Racial Identity Model which discusses identity formation for Caribbean Black American and African American adolescents. Identity formation describes the conscious processes of examining one's feelings, thoughts, behaviors, and relating to others who may or may not share commonalities (Cross, 1995). In this model, identity formation is associated with perceived discrimination and how it influences conflicted racial identity versus resolved racial identity. Conflicted racial identity is associated with increased psychological distress (Sanchez, Bentley-Edwards, Matthews, & Granillo, 2016).
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Sanchez, Bentley-Edwards, Matthews, and Granillos (2016) study examined the relationship among racial identity, perceived discrimination, and psychological concerns among 189 Caribbean Black American and African American adolescents and found that for all participants having a less mature racial identity profile was related to perceived discrimination and psychological concerns. The researchers found that in this study Caribbean Black American adolescents presented with confusion and/or anxiety over awareness of the salience of race such as being racially profiled for being Black or being ignored by their teacher in class which results in maintaining a preference to not associate with their race. Furthermore, Caribbean Black American adolescents identified as having conflicted racial identity may experience distress because they were mistaken as Black due to their phenotype. To cope with the effects of racism, dissonance and conformity to racial identity attitudes may be at play, however, research has found that they result in negative mental health outcomes (Munford, 1994).
Similarly, immigration and acculturation have also been identified as risk factors for depression. Acculturation is a process of psychological and cultural changes that involve accommodation between two groups, usually some form of longer-term adaptation to living from the groups in contact (Berry, 1992). For example, acculturation may involve learning the language, sharing food, and adopting forms of dress and social interactions (Berry, 2005). Acculturation involves adaption in physical, psychological, financial, spiritual, social, language, and family adjustments which can be a very stressful process for individuals (Mui & Kang, 2006). The process of acculturation can pose socio-psychological stress for immigrant adolescents. Acculturation stress is broadly defined as the stress individuals encounter from the acculturation process (Gelfand & Yee, 1991). Acculturation stress may
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result from having to negotiate in-group and outgroup challenges (Patil, Porche, Shippen, Dallenback, & Fortuna, 2018). Developing a positive racial/ethnic identity in a dominant non-Hispanic White culture carries many challenges for minority adolescents, which can unfortunately lead to mental health risks (Patil, Porche, Shippen, Dallenback, & Fortuna, 2018). Wakefield, David, & Hudley's (2007) review support the findings that a strong, positive ethnic identity is beneficial for adolescent mental health. They further encourage strategies parents, teachers, and school can take to support positive ethnic identity development in racial and ethnic minority students such as parents teaching their children how to cope with perceived discrimination and stereotypes and working collaboratively with schools implementing culturally competent parent education programs (Hudley, & Taylor, 2006). An emphasis on maximizing an adolescent’s exposure to parental, community, and peer support may help adolescents develop healthy and positive ethnic identities (Wakefield, David, & Hudley, 2007).
Social Capital Theory
Social capital theory is a complex concept originating from the works of Bourdieu, Putnam, and Coleman (McPherson, Kerr, Morgan, McGee, Cheater, McLean, and Egan, 2013). Bordieu and colleagues were the original researchers investigating and trying to understand the social capital construct. Bourdieu defined social capital as “the aggregate of the actual or potential resources which are linked to possession of a durable network of more or less institutionalized relationships of mutual acquaintance and recognition” (Bourdieu, 1986). Robert Putnam’s empirical work identified community-level resource as a product of social capital and defined social capital as “features of social organization such as networks, norms and social trust that facilitate coordination and cooperation for mutual benefit”
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(Putnam, 1993). In 2000, Putnam referred to social capital as “the connections among individuals - social networks and the norms of reciprocity and trustworthiness that arise from them.” In addition, Coleman et al. (1988) conceptualized social capital in relation to family relationships. Social capital is currently articulated and researched slightly differently from these original definitions, which has caused variability regarding the construct’s definition and measurement. Although, the construct is conceptualized slightly differently depending on researcher's definition, the common thread appears to be that social capital takes into consideration the importance of social networks and relationship to bring about social, economic, and health changes among different groups, hierarchies, and societies (McPherson, Kerr, Morgan, McGee, Cheater, McLean, & Egan, 2013).
Theoretically, social capital operates at multiple levels within the social structure including bonding, bridging, and linking being commonly studied in the literature (Gittell & Vidal, 1998; Putnam, 2000; Stone & Hughes, 2002; Van Deth, 2003). Kawachi (1997) has conceptualized social capital as consisting of both structural and cognitive dimensions with several additional components such as bonding and bridging social capital. Bonding and bridging social capital have been identified as different “types” of social capital in the literature. Bonding social capital refers to relationships between individuals or groups sharing similar demographic characteristics. Bridging and/or linking social capital refers to relationships across different communities or individuals (e.g., neighbors and work colleagues) (Narayan, 1999). Linking is also included in the social capital construct in that they are the social connections formed across power hierarchies, for example teachers and students. In addition, social capital consists of four analytic levels in relation to health: the macro-level (countries, states regions, and local municipalities), meso-level (neighborhoods
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and blocks), micro-level (social networks and social participants), and individual psychological level (trust and norm) (Macinko & Sartfield, 2001).
Social Capital and Mental Health
McPherson, Kerr, McGee, Morgan, Cheater, McLean, and Egan (2014) conducted an integrative systemic review exploring the association between social capital and mental health and behavior problems in children and adolescents. This review focused on the influence of family and community social capital on mental health and was the first to focus on children and adolescents. In addition, their article not only identifies the literature of the subject matter but also makes recommendations, discusses implications for future research, and policy development. In this review, studies conducted between 1990 and 2012 which categorized indicators of family and community social capital, were included. FSC explored the role of family structure, quality of parent-child relations, adult interest in the child, parents monitoring of the child, and extended family support and exchange. They also included CSC, which examined the social support networks, civic engagement in local institutions, trust and safety, religiosity, the quality of the school, and the quality of the neighborhood. The researchers involved reviews in which children (5-10 years old) and adolescents (10-19 years old) were included. The review included studies in which they directly collected data from the young person in addition to data gathered from a parent, teacher, or professional reporting on the young person. The outcomes of interest were measured self-esteem and self-worth, internalizing behaviors (e.g. depression and anxiety), and externalizing behaviors (e.g. aggression, violence, conduct disorders, and disobedience). McPherson et al. (2013) identified 55 studies meeting their criteria for review. In 29 of the studies identified, ethnicity, race, or nationality of participants was not included, while 11
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studies reported that most of their sample was black. The remainder of studies included majority participants of American Indian, Dutch, Latino, Mainland Chinese, and Southeast Asian descent. With respect to self-esteem and self-worth, four studies were identified in which self-esteem and self-worth was explored in the context of FSC. There appears to be support for positive communication, nurturance, and low levels of conflict between parent-adolescents resulted in better self-esteem/worth (Bimdorf et al. 2005; Yugo & Davidson, 2007; Ying & Han, 2008). Parental monitoring and control were found to have negative effects on self-esteem/worth. CSC also provided positive outcomes in that those who reported positive relationships beyond the family, feeling safe at school, and being engaged with school reported higher levels of self-esteem/worth.
When examining the literature on internalizing behaviors (includes thoughts, feelings, emotions, and behaviors directed inward), McPherson and colleagues (2014) were interested in studies exploring depressive symptoms, anxiety and social anxiety, moods, emotions, and composite scores that measured the mentioned behaviors. The researchers found that positive parent-child relationships were associated with lower levels of internalizing behaviors (Caughy et al., 2008; Springer et al., 2006; Ying & Han, 2008). Furthermore, when children and adolescents live in higher quality and wider networks, they tend to report fewer internalizing behaviors. It is important to note that although findings suggest that living in higher quality neighborhoods were beneficial to children and adolescents’ mental health, in impoverished communities the opposite was the case. These families benefitted most from having their primary caregiver knowing fewer of their neighbors. Young people who live in a two-parent family home were also less likely to have internalizing/externalizing problems (Galboda-Liyange et al., 2003; Wen, 2008).
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A longitudinal study of adolescents and their parents from seven cities in mainland China investigated the relationship between social capital in the family and the community with family human capital and financial capital on depressive symptoms of urban Chinese adolescents (Wu, Xie, Chou, Palmer, Gallaher, & Anderson Johnson, 2010). Their findings suggested that higher community social capital was associated with lower level of adolescent depressive symptoms. In addition, they reported that family social capital significantly mediated the effects of all other contextual factors on adolescent depressive symptoms. Surprisingly, increased depressive symptoms were observed in higher family financial capital. Higher financial capital was found to negatively affect family social capital. In their study, female adolescents reported more depressive symptoms due to less available family social capital.
Rothon, Goodwin, and Stansfeld (2012) examined the relationship between family social support, community social capital and mental health and educational outcomes.
Family social support was defined as quality of parent-child relationships, evening meals with family, and parental surveillance while community social capital included parental involvement at school, sociability, and involvement in activities outside of the home. This was a longitudinal study in which baseline measures of social capital were taken when adolescents were 13-14 years-old. Mental health was measured at 14-15 years-old using the General Health Questionnaire (GHQ) which screens for anxiety and depression. Educational achievement was measured at 15-16 years-old using national examination (General Certificate of Education). Results from this study found that "good paternal and maternal" relationships, higher parental surveillance, and frequent evening family meals were associated with lower odds of poor mental health. Although the findings on mental health
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are encouraging, these results focus on the associations between mental health and educational outcomes, and do not discuss health outcomes. Few studies have examined health outcomes in relation to social capital, ethnicity, and mental health.
Not only do adolescents benefit from the direct effects of their social networks, but studies found that they can also indirectly benefit from social networks their parents are involved in (McPherson et al., 2014). McPherson et al. (2014) concluded that interventions targeted at improving the parent-child relationship such as the Triple P- Positive Parenting Program (Sanders, 2008) can be utilized as a protective intervention against adolescent health risk behaviors. There is substantial support for increase of self-worth, self-esteem, and confidence achieved through positive youth experiences that alleviate the effects of health stressors (McMahon Felix, & Nagarajan, 2011).
Social Capital Measurement Issues
As previously mentioned, social capital theory is complex and consists of multiple domains, because of this Putnam (1993) created a measurement of social capital theory which consisted of summing several indicators of social capital characteristics: the existence of community networks, civic engagement, civic identity, reciprocity, and trust (Kritsotakis, Gamarnikow, 2004; Putnam, 1993). However, there is still a need for more consistent measures of social capital that consider the multiple dimensions of the construct but in relation to specific mental health. The social capital construct and its dimensions might be best understood and measured if framed in the context of the specific mental health outcomes of interest.
The Integrated Questionnaire for the Measurement of Social Capital (SC-IQ) attempts to measure six dimensions of social capital (groups and networks, trust and solidarity,
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collective action and cooperation, information and communication, social cohesion and inclusion, and empowerment and political action (Grootaert et al., 2004). Although this measure is an attempt to address the critical issues of the social capital construct, it is not appropriate to apply to youth samples. In addition, social capital in the context of youth also remains limited (Grootaert, Narayan, Woolcock, & Nyhan-Jones, 2004; Harpham, Grant, & Thomas, 2002; Lochner, Kawachi, & Kennedy, 1999; Pronyk et al., 2008). In an attempt to contribute to the existing social capital measurement literature addressing sexual and reproductive health, Cordova et al. (2017) identified factors related to social capital such as condom self-efficacy, civic engagement, and adult and community support as a composite measurement of social capital aimed at addressing social capital and sexual and reproductive health. However, that study tailored their social capital measure to include items of sexual and reproductive health, which is not applicable in this study.
Summary and Proposed Study
Youth encompasses a developmentally challenging period and one in which potential for engaging in risk-related activities is heightened (Smylie, Medaglia, and Maicka-yndale, 2006). The rise in prevalence of depression may indicate the limited efficacy of existing interventions and prevention efforts regardless of increasing research in the treatment of depression (Mojtabai et al., 2016). Furthermore, mental health issues during youth are also important due to how preventable mental health and health-risk behaviors are (CDCP, 2010; Kessler et al., 2005). The literature emphasizes the importance of social capital within the family context, specifically positive parent-child interactions. There is a dearth of research on the role of social capital in depressive symptomatology among youth compared to the literature on adults (Crosby, Holtgrave, DiClemente, Wingood, & Gayle, 2003; Murayama et
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al., 2012; Viner et al., 2012). Therefore, not only understanding the relationship between social capital and adolescent relationships with parents, adults, and peers is important, it is also crucial to understand the effect of different cultural and environmental contexts. Moreover, research on the influence of social capital and depression is important because it is possible that findings on social capital may aid policy endeavors that support interventions aimed at addressing and improving social capital among youth, leading to improvements in the prevention of depression symptoms among at-risk youth.
This study focuses on the impact of social capital, coping, gender, and ethnicity on youth depression symptoms. The hypotheses of this study are based primarily on evidence that social capital has been found to be inversely related to depressive symptoms outcomes (Rothon, Goodwin, & Stansfeld, 2012). In addition, there is empirical evidence that supports females tend to endorse more depression symptoms than males. A hierarchical multiple regression model will test the effects of social capital, coping, gender, ethnicity, and depression symptoms. Further, interactions between social capital and gender, as well as social capital and ethnicity for depression symptoms will be examined in the hierarchical regression model. Based on the supporting evidence, we expect to replicate these finding and hypothesize the following:
(1) When controlling for gender and ethnicity, coping has a negative relationship with depression, such that youth (14-21 years old) who endorse more coping have less depression symptoms.
(2) When controlling for gender, ethnicity, and coping, social capital and depression symptoms will be inversely related such that youth (14-21 years old) in the sample
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who have higher social capital will have lower depression symptoms than youth who have lower social capital.
(3) There will be a significant interaction effect of social capital and gender on depression symptoms of youth (14-21 years old), such that females with lower social capital will have more depression symptoms compared to males.
(4) There will be a significant interaction effect of social capital and ethnicity on depression symptoms of youth (14-21 years old). There will be significant ethnic group differences in the relationship between social capital and depression symptoms, such that youth who identify as Hispanic, African American, and Asian (14-21 years old) who have lower social capital will have more depressive symptoms compared to non-Hispanic White youth (14-21 years old).
To test these hypotheses, the following specific aims are planned:
Aim 1: Using hierarchical multiple regression, examine the unique contributions of gender, ethnicity, coping, and social capital, on depression symptoms among youth (14-21 years old).
Aim 2: Examine the interaction between gender and social capital on depression symptoms within a hierarchical regression model.
Aim 3: Examine the interaction between ethnicity and social capital on depression symptoms within a hierarchical regression model.
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CHAPTER II
METHOD
Participants
Participants considered for the study met the following criteria, 1) were between the ages of 14-21 years old, 2) resided in the Denver Metropolitan area, 3) female or male, 4) and were able to read and understand English. Participants were excluded if they are unable to provide informed consent.
Table 1 presents demographic information for the 200-youth surveyed. The mean age of the participants was 17.4 years (SD = 1.7 years, range = 14-21 years). Females comprised 57.2% of the participants surveyed. With respect to ethnicity/race, participants were permitted to select more than one ethnicity, therefore all responses were considered in the analyses. Of the ethnicities selected, 96 (48.%) participants identified as Hispanic/Latino, followed by 71 (35.50%) non-Hispanic White, 32 (16%) as Asian, 23 (11.50%) Black, and 2 (1.00%) Other, 5 (2.50%) as Native Hawaiian or Other Pacific Islander, 12 (6%) as American Indian or Alaska Native, respectively. The majority of youth reported being in 11th grade (58.5%), followed by some college (29.0%), 12th grade (4.0%), 8th grade (3.0%), and 10th grade (2.5%), respectively. No significant differences in the distribution of participants by gender across different racial/ethnic groups were found, %2 (19) = 20.065 (p = .391).
Recruitment and Data Collection
Data for the current study were collected when approval from the Institutional Review Board and the Colorado Multiple Institutional Review Board was obtained. In addition, approval was obtained by the Research Review Committees of the partnering collaborators at the National Latina Institute for Reproductive Health, Florence Crittenton
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Services, and YTH (Youth, Tech, Health). Participants were recruited from community-based agencies and educational institutions serving a large youth sample between February and March 2014. Participants were identified through these agencies and were asked to refer others in their social networks for the study. Participants were prescreened for the eligibility criteria to include the youth demographics described under participants section. Three graduate research assistants recruited and collected data in-person using paper surveys and iPads. Upon completion of the survey, participants were given a $15 cash incentive for completing the survey.
Participants were asked to complete self-report measures at one time point via a computer-based survey using a tablet or paper form. The measures took approximately 20-30 minutes to complete. Participants had the consent read aloud to them or watched a video describing the consent process. A waiver of parental consent was signed based on Colorado law allowing those under 18 years old to seek information and services related to sexual and reproductive health. Participants who provided assent were enrolled in the study. Each participant completed demographic, Center for Epidemiologic Studies-Depression scale (CES-D), social capital related questions concerning community support, adult support, civic engagement, and coping questions.
Measures
Demographics (age, race, ethnicity, grade level, gender, language, citizenship, highest level of education attained by parents, who they reside with, how many people currently live in their home, and a description of their living conditions) were collected to use as possible covariates in analyses. Participants completed the following measures at one time point. Predictor Variables
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Social Capital
Social Capital was initially measured using 11 items assessing perceptions of adult support, community support, civic engagement. The reliability of the measure of the independent variable of social capital was tested with Cronbach’s alpha to test reliability in terms of internal consistency of the 11 question items. The 11-item measure of social capital (a = 0.73) had an acceptable reliability in terms of internal consistency since the Cronbach’s alpha value was greater than the minimum acceptable value for Cronbach’s alpha of 0.70. However, when reducing the social capital variable down to 5 items including only information from adult support the reliability of the measure improved to (a = 0.83). In addition, this change in the measure improved the regression model.
Adult and Community support. Community support was measured with one item, “Do you feel like your neighborhood and community are supportive places?” Responses ranged from “l=not at all,” to “5=very supportive.” Adult support included 5 items. An example question is, “How often do the adults in your life provide you with love and support?” Responses ranged from “l=never,” to, “5=all of the time.”
Civic engagement. Civic engagement included 5 items. An example question is, “How often do you take part in clubs or groups in school or out-of-school?” Responses ranged from “l=never,” to “5=all of the time.”
Coping
Coping included 13 binomially scored (present or not present) items from which participants could indicate how many of the coping strategies they engage in. The question was, “Think about the times when you have felt stress of been in stressful situations in the past three months. Did you do any of the following” there were 13 available responses to
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choose from (breaking up an upsetting problem down into smaller parts, think about one problem at a time, make new friends, keep from feeling sad, make unpleasant thoughts go away, etc.) Responses ranged from “0=no,” to “l=yes ” The reliability of the measure of the coping was tested with Cronbach’s alpha to test reliability in terms of internal consistency of the 13 items. The 13-item measure of coping (a = 0.71) had an acceptable reliability in terms of internal consistency since the Cronbach’s alpha value was greater than the minimum acceptable value for Cronbach’s alpha of 0.70.
Gender and Ethnicity
To better understand the ethnic and gender differences between social capital and depression symptoms youth had to complete demographic questions. Participants were asked to indicate whether they were female, male, transgendered (male to female), transgendered (female to male), or other/don’t want to answer. Participants were asked to check the following races that applied to them: American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or other Pacific Islander, White, Other, or don’t want to answer.
Outcome variables Depression
Depression symptoms were assessed using the 20-item Center for Epidemiologic Studies depression (CES-D) Scale (Radioff, 1977). The scale asks to report on feelings and experiences during the last week (e.g. “You felt depressed” and “You felt that you couldn’t shake off the blues, even with help from your family and friends”) with response options ranging from “never or rarely” to “Most of the time or all the time.” Four questions were reverse scored (“You felt that you were just as good as other people,” “You felt hopeful
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about the future,” “You were happy,” and “You enjoyed life”). The scale reliability is high in the general population (a = .85). The CES-D has been shown to have good sensitivity and specificity, with high internal consistency (Lewinsohn, Seeley, Roberts & Allen, 1997). In addition, the measure has been used with different racial and ethnic groups (Roth et al.,
2008). The total score of the CES-D will be the datum point for analyses. This 20-item self-report scale yields scores ranging from 0 to 60, with a score of 16 or above indicating a clinical level of depression. However, a cutoff score above 24 has been used to detect more accurately clinically cases among adolescents (Roberts, Lewinsohn, & Seeley, 1991; Radi off, 1991).
Data Cleaning
The full Ford Survey dataset included 200 youth surveys which were entered into IBM SPSS statistical software (IBM Corp., 2017). Assumptions for multiple regression were assessed and are presented below. Data with missing values were coded “99999” in order to distinctly identify these items. Descriptive statistics including mean, median, mode, standard deviation, and frequency distribution charts were analyzed using SPSS, and there were no identifiable outliers or data entry errors. Examination of frequency histograms was computed for observed variables to assess for normality and linearity. Continuous variables were checked for normality. Scale scores were calculated according to the author’s instructions. Bivariate associations among study variables were examined.
Data Analysis
Power analysis is conducted through GPower 3.1 software (Faul, 2007), the sample size was determined using an alpha of .05, a desired power of .90, and an anticipated small to medium effect size of (f2 = . 15). Using these parameters, it was determined that a sample
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size of N = 92 was needed. This current study was able to satisfy this requirement because the final sample size was a total of 200 samples.
A hierarchical multiple linear regression was conducted to address the research questions. Hierarchical multiple linear regression was tested the effects of social capital, coping, gender, and ethnicity on depression symptoms. First the demographics of gender and ethnicity were entered given their ability to confound the relationship between the independent and dependent variable of depression symptoms. Second, coping was entered into the model, to provide additional control of coping on depression symptoms. Third, the social capital variable was entered into the model to examine its relationship to depression symptoms when the demographics and coping variables are controlled. Fourth, the interaction between the two predictors was entered, specifically, main effects (i.e., social capital and gender) entered the regression equation. Finally, the interactions between social capital and ethnicities were entered into the model. The interaction examines whether a significant proportion of variance is accounted for by interaction terms after partialing the main effects of the predictors in the first couple steps of the analysis. Interaction effects usually tend to be difficult to detect with multiple regression, a more liberal Type I error level of .10 was set (McClelland & Judd, 1993), and in order to explore potentially meaningful interactions. A significant moderator effect is determined with a significant change in R2 for the interaction term.
The first assumption tested was linearity, or that the relationship between the independent variables and the dependent variable is linear. The assumption of linearity is best tested with scatterplot of the standard regression output of standardized predicted values against residuals. The scatterplot is shown in Figure 1. In the scatterplot, it can be observed
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that the data points are symmetrically distributed around a diagonal line in the horizontal line in the residuals versus predicted values plot. This means that the assumption of linearity was observed in the standard regression output in the prediction of the dependent variable of depression. Thus, the assumption of linearity was satisfied based on the investigation of the scatterplot.
The second assumption tested is that the data needs to show homoscedasticity, which means that there should equal variance of all values of the independent variables around the regression line. Test of homoscedasticity was based on a visual inspection of the same scatterplot of the error terms (residuals) and the predicted values of the dependent variable of depression in Figure 1. The scatterplot of standardized predicted values against residuals should be a random pattern centered around the line of zero standard residual value to show homoscedasticity. The scatterplot of the regression in predicting depression showed random scatter. There was no observation of a megaphone structure of residuals in the scatterplots, which is a pattern of non-homoscedasticity. Thus, the assumption of homoscedasticity was satisfied.
The third assumption tested normality of the data or the error distribution of data. Normal probability plot of the residual was used to test the assumption of normality of data. This is shown in Figure 2. Looking at the normal probability plot, the plot closely fall in the diagonal line indicating that regression model created in predicting depression showed normality of the data. In addition, histogram in Figure 3 showed that the distribution of the data of depression symptoms followed a bell-shaped curved of the normal distribution pattern when social capital, gender, and ethnicity were predictors in the regression model.
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Skewness and kurtosis statistics of the data of depression symptoms were also computed for normality investigation. To determine whether the data follows a normal distribution, skewness statistics greater than three indicate strong non-normality and kurtosis statistics between 10 and 20 also indicate non-normality (Kline, 2015). As can be seen in Table 1, the skewness (0.75) and kurtosis (0.43) statistic value of the dependent variable of depression were within the acceptable range enumerated by Kline (2015). Thus, the assumption of normality was satisfied based on the investigation of the normality graphs and skewness and kurtosis statistics.
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CHAPTER IV
RESULTS
Hierarchical multiple regression was conducted to determine whether social capital, coping, gender, and ethnicity are significant predictors of depression. Additionally, the results of the hierarchical multiple regression determined the significance of the combined influences of the social capital, gender, and race on depression. In addition, interaction terms were created in order to determine the impact of group differences of social capital, gender, and ethnicity on depression symptoms to address the following hypotheses. A level of significance of 0.05 was used in the hierarchical multiple regression analysis. The results of the hierarchical multiple regression are presented in Table 3.
Hypothesis 1
To test hypothesis 1, hierarchical multiple regression was conducted to control for gender and ethnicity and examine the relationship between coping and depression symptoms. In step 1, gender and ethnicity were entered, which accounted for 7.7% of the variance (F(8,188) =1.96, p=.053). In step 1, although not specifically answering this research question, there was a significant finding for gender (P = 4.33, p = .00). In step two, coping was entered, which accounted for 8.6% of the variance (F(9,187) =1.96, p=.047). When controlling for gender and ethnicity, coping has no relationship with depression (P = .33, p = .18). Unfortunately, the predicted hypothesis that there would be a negative relationship between coping and depression symptoms among youth was not significant.
Hypothesis 2
The focus was then on examining the relationship between social capital and depression symptoms when controlling for gender, ethnicity, and coping. It was hypothesized
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that social capital and depression symptoms would be inversely related such that youth (14-21 years old) in the sample who have higher social capital will have lower depression symptoms than youth who have lower social capital. In step 3 of the hierarchical multiple regression model, social capital was entered, which counted for 23% of the variance (F(10, 186) =5.43, p<0.00). When controlling for gender, ethnicity, and coping, social capital and depression symptoms were inversely related such that youth (14-21 years old) in the sample who had higher social capital had lower depression symptoms than youth who had lower social capital (B = -.98, p = .00).
Hypothesis 3
Interaction effects of social capital and gender on depression symptoms were examined in the hierarchical regression analysis to determine if there was a moderating gender effect on the relationship between social capital and depression symptoms. It was hypothesized that female youth with lower social capital would have higher depression symptoms compared to males. In step 4, the interaction of social capital*gender was entered into the model, which accounted for 23% of the variance (F(11, 185) =5.11, p<0.00). There was no significant interaction effect of social capital and gender on depression symptoms of youth (14-21 years old) (B=-.45, p=.19).
Hypothesis 4
Interaction effects of social capital and ethnicity on depression symptoms were examined in the hierarchical regression analysis to determine where there was a moderating ethnicity effect on the relationship between social capital and depression symptoms. It was hypothesized that there would be a significant interaction effect of social capital and ethnicity on depression symptoms of youth (14-21 years old). There will be significant ethnic group
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differences in the relationship between social capital and depression symptoms, such that youth who identify as Hispanic, African American, and Asian (14-21 years old) who experience lower social capital would have higher depressive symptoms compared to White youth (14-21 years old). In step 5, social capital *Black, social capital*Asian, social capital*Hispanic, social capital*other, social capital*American Indian or Alaska Native, social capital*Native Hawaiian or other Pacific Islander, social capital*White, were entered into the hierarchical multiple regression model, which accounted for 26 % of the variance (F(18, 178)=3.44, p<0.00). There was no significant interaction effect of social capital and ethnicity on depression symptoms of youth (14-21 years old): social capital*Black (B=.22, p=.74), social capital*Asian (B=-.47, p=.40), social capital*Hispanic (B=-.74, p=. 13), social capital*other (B=-2.88, p=.52), social capital*American Indian or Alaska Native (B=-.39, p=.54), social capital*Native Hawaiian or other Pacific Islander (B=3.52, p=..14), social capital*White (B=.57, p=. 15). There are no significant ethnicity group differences in the relationship between social capital and depression symptoms. In the study, the % of variability accounted for showed much of an increase because it went up from 7.7% to 26%. Multicollinearity Investigation
In terms of post-estimation diagnosis for multicollinearity, collinearity statistic of tolerance and Variance Inflation Factor (VIF) for each independent variable were calculated to check for the presence of multicollinearity of the different independent variables in predicting depression. The VIF and tolerance values are summarized in Table 4. It should be noted that predictors that have tolerance values well above 0.2 and VIF values below 10 are not multicollinear in predicting a dependent variable. The VIF statistics of several of the interaction terms in step 5 of the hierarchical regression model were not within the acceptable
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ranges, which indicated that these interaction terms were multicollinear in predicting depression symptoms. Therefore because of this issue, the preferred model for answering hypotheses questions should consider the step 3 model because of the following reasons: tolerance is considered a problem if it is less than .20, correlations between gender and interactions of gender were very high in the Cronbach alphas of .90 and above, the correlations between ethnicity and interactions of ethnicities were also very high, this provided support for dropping those items from the model. Finally, the interaction terms were not statistically significant in the model which provided additional support from excluding steps 4 and 5 from the model. Although removing these multicollinear items created a slightly lower variance, the tolerance and variance of model step 3 was acceptable with a variance of 23%.
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CHAPTER V
DISCUSSION AND CONCLUSIONS
Youth are a vulnerable population given the developmentally challenging period encompassing physical, cognitive, and social changes they encounter. These sudden changes can potentially put them at risk for developing mental health concerns such as depression, anxiety, as well as health risk-related behaviors (Smylie, Medaglia, & Maicka-yndale, 2006). Current research supports that youth are at an increased risk for developing depression symptoms and being a young female or of the racial/ethnic group provides additional challenges. Given the gravity of mental health risk for this population and a_rise in prevalence of depression may indicate the limited efficacy of existing interventions and prevention efforts regardless of increasing research in the treatment of depression (Mojtabai et al., 2016). An attempt to understand and emphasize the importance of social capital on depression among youth is a potential avenue for informing and evaluating future interventions for this population.
The current study explored the predictive relationships of social capital, gender, ethnicity, and depression symptoms using hierarchical multiple regression. The regression model was based on the literature emphasizing the effects of social capital on depression symptoms among youth. Literature on gender and ethnicity differences also guided further exploration of interactions within a multiple regression model. Although, there is evidence supporting social capital’s association with depression, the literature was limited in establishing these findings among youth.
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Research on the role of social capital in depressive symptomatology among youth compared to the literature on adults is lacking (Crosby, Holtgrave, DiClemente, Wingood, & Gayle, 2003; Murayama et al., 2012; Viner et al., 2012). In addition, measurement variation among the social capital literature warranted for additional expoloration. The chronbach alpha in this study indicated that reducing the 11 items of social capital to 5 yielded a better chronbachs alpha. Specifically, the items regarding adult support were better correlated with one another and indicative of what the study was attempting to examine. It is important to consider how adult support in terms of social capital may impact depression. For example, studies have highlighted positive parent-child interactions as a way of improving social capital which may also influence depression symptoms or mood (McPherson, Kerr, McGee, Morgan, Cheater, McLean, & Egan, 2014). In addition, studies vary greatly from conducting exploratory and confirmatory factor analyses to reach a social capital latent variable and others that ask questions related to factors of social capital which are then summed for analyses (Porter, 2008). For example, as previously noted the Cordova, Cole-Minahan, Bull, & Borrayo (2017) implemented exploratory and confirmatory factor analysis to create a social capital measure aimed for their population and questions of interest. Social capital is a multifaceted psychosocial factor which can at times prove challenging during research given the lack of an agreed upon measurement of social capital. Therefore, it is important for researchers to continue studying the various components of social capital and its effects on youth health to inform future interventions and policies.
In addition, the literature emphasized that when racial/ethnic differences have been examined, most of the studies included populations consisting of non-Hispanic White and African-American adolescents (Malooly, Flannery, & Ohannessian, 2017). This study builds
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upon the current social capital literature among youth by studying the influences of social capital, gender, and ethnicity on depression symptoms among youth. Unfortunately, these findings were not supported in this study. There were no sigificant findings in the relationship between ethnicity and depression nor the interaction of social capital and the various ethnicities. There may be several explanations to the non-significant findings, the literature on racial and ethnic differences in depressive symptoms has been inconsistent (Maag & Irvin, 2005). Malooly, Flanery, and Ohannessian’s study did not find significant differences based on race/ethnicity. Another issue may be that ethnic minorities may underreport symptoms of depression, given the long-standing history with mental health stigma. Breland-Noble, Bell, and Burriss (2011) discuss the mental health barriers that African American adolescents including negative perceptions of mental illness, fears of being mislabeled with a conduct disorder, and mistrust of researchers. The literature on underreporting among ethnic/racial minority youth is limited as well, however, it is often listed as concerns in various studies discussions (Nestor, Cheek, & Liu, 2016; Cheng, Hitter, Adams, & Williams, 2016; Klaus, Mobilio, & King, 2009).
The results suggest that youth self-reported social capital items such as those related to adult support are closely associated with one another and potentially protective against developing depression symptoms. Given that the CES-D is not a diagnostic tool of depression it is important to note that this study cannot conclude that social capital can or cannot lead to depression. Additionally, this study was able to reiterate previous findings that female youth do experience more depression symptoms compared to male youth, however this finding was not replicated in the context of social capital. Given that few studies have examined social capital in the context of youth mental health, this study
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attempted to expand beyond the limited ethnic populations studied with respect to social capital and depression symptoms. Findings from the current study may provide clinicians with increased knowledge about the importance of social capital among youth mental health. Intervention and Policy
This study’s findings have several implications for policy and intervention. The study adds to the literature and captures the importance of social capital among youth mental health given the risk of depression symptoms and risk for developing adult depression. This study provided support for future studies researching psychosocial factors affecting youth wellbeing and mental health, and may assist in designing feasible interventions, evaluation of interventions, and drafting policy to benefit this vulnerable population. The social capital and depression literature emphasize the importance of early intervention in addressing mental health problems to offset further depression symptoms which is known to affect several psychosocial aspects of life. Social capital has been found to benefit adolescent through the support generated through social capital (Vilhjalmsdottir, Gardarsdottir, Bernburg, & Sigfusdottir (2016). McPherson et al.'s (2013) meta-analysis of 55 studies on social capital and its relationship to mental health identified how support for positive communication, nurturance, and low levels of conflict between parent-adolescents can greatly benefit adolescent’s self-esteem and self-worth. Social capital in this context focused on family social capital, which may only be a small subset of social capital but may be feasible when designing interventions for adolescents experiencing emotional distress. More specifically, when mental health providers intervene with adolescents experiencing depression symptoms, providing the parents with information on the benefits of increasing social capital in the forms of family support (positive communication, nurturance, low conflict, etc.) may result
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in additional positive outcomes for the adolescent. Family social support may come in many forms such as evening meals with family, parental surveillance, and parent-child relationships as studied by Rothon, Goodwin, and Stansfeld (2012) and their findings supported that family social capital decreased the odds of poor mental health. Given that the health disparities gap continues to widen due in part to limited funding for mental health and the numerous barriers to treatment people encounter, informing parents, educators, and communities on the positive benefits of social capital may be feasible avenue in addressing mental health issues among one of the most vulnerable populations.
Social capital research may aide drafting policy to provide funding in support of interventions targeted at adolescent mental health. For example, research studies may continue to provide additional data and support for funding of programs and interventions such as the Triple P- Positive Parenting Program (Sanders, 2008) so that they may be available and accessible to families. The Triple P- Positive Parenting Program is an evidence-based program that includes aspects of cognitive behavioral and developmental theory, along with social learning to teach parents the skills to be able to manage family issues and behavioral problems (Thomas & Zimmer-Gembeck, 2007). For example, parent strategies in the program focus on developing positive relationships. Thomas and Zimmer-Gembeck (2007) conducted a meta-analysis evaluating and comparing behavioral outcomes of two disseminated parenting interventions- Parent Child Interaction Therapy and Triple P-Positive Parenting Program. The meta-analysis on these parenting programs provides support for the efficacy of the program which received substantial U.S. government funding for their implementation. Social capital research has emphasized the importance of positive relationships between parents and adolescents. The findings from this study encourage
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families, communities, and outside agencies to collaborate on building upon interventions to include elements that may increase social capital for the benefit of adolescent mental health. Continuous research on social capital may lead to programs that can target interventions for the most vulnerable adolescent populations, such as minorities and the socioeconomically disadvantaged.
Study Limitations
There are several limitations to the findings of this study. Findings of studies should always be interpreted with caution, this study’s youth is not representative of the U.S. youth population which limits interpretability to youth outside of Denver, Colorado. Additionally, another limitation in this study was with respect to the measurement of social capital. An established measure of social capital is lacking in the literature. However, this allows for flexibility of including questions related to the multifaceted components of social capital for this study. The coping measure was also not supported in this study. The results showed a positive relationship between coping and depression symptoms which indicates that the coping measure may not have been a true measure of coping in relation to depression symptoms. In addition, the survey questioned youth about the gender they identified with which was not limited to male and female as described in the methods section. However, analyses only considered responses for those who endorsed male and female responses given that there were very few who did not identify as male or female. Future studies should explore different gender groups as this is an area that is also lacking in social capital research. Another important consideration may be that the direction of the effects between social capital and depression is not well understood. For example, the literature suggests that depression symptoms could lead to behavioral withdraw which may reduce aspects of social
39


capital. Therefore, a cross-sectional study makes it difficult to understand whether social capital reduces depression or if depression reduces social capital (O’Conner, Hawkins, Toumbourou, Sanson, Letcher, & Olsson, 2011). These limitations can inform future studies examining social capital and how to improve upon what is currently known in the literature. Future Directions
A large portion of this study consisted of youth who were taking college credit courses, which may indicate a predisposition to supportive networks engaged in their success and wellbeing, although speculation, this may warrant future considerations. Future studies should consider studying vulnerable youth and identifying aspects of social capital that can be increased to protect against symptoms of depression. For example, trajectories of depression over youth in a large US sample found that strong connections with parents and peers predicted membership in a non-depressed trajectory group (Costellow, Swendsen,
Rose, & Dierker, 2008). Additionally, because this study was examining depression symptoms and not a diagnosis of depression, future directions may expand upon the literature with youth who have a depression diagnosis.
Future studies may incorporate aspects of acculturation in their analyses to explore ethnicity differences in the relationship between social capital and depression among youth. For example, further exploring the impact of acculturation on social capital and expanding to examine if acculturation/acculturation stress may explain depression symptoms. Concha, Sanchez, De la Rosa, and Villar (2013) conducted a longitudinal study to assess the effects of social capital on acculturation-related stress among recently immigrated Hispanic adults before and after immigration. They concluded that acculturative stress was negatively related to support from friends and positively from support from parents. Unfortunately,
40


studies including youth in respect to acculturation stress and depression within the social capital framework are also limited.
Although there were no significant ethnic group differences confirmed in this study it is important to continue making efforts towards expanding research studies to diverse populations. With respect to diverse samples, these should not be limited to ethnic diversity but gender as well which was not explored beyond male and females in the current study. Future studies may make efforts towards understanding social capital and mental health in the context of acculturation and work towards a more inclusive effort of gender and ethnicity to better understand the implications of social capital and create appropriately tailored interventions for vulnerable youth.
41


TABLES
Table 1
Demographic Characteristics (N= 200)________
Youth Age in years
Frequency Percentage Min Max Mean SD
14 21 17.38 1.68
Gender (F/M) 114/86 57%/43%
Ethnicity
White 71 35.50%
Hispanic 96 48.00%
Black 23 11.50%
Asian 32 16.00%
Other 2 1.00%
American Indian/Alaska 12 6.00%
Native Hawaiian or Pacific 5 2.50%
Participants Education
(1) 8th grade 6 3.0%
(2) 9th grade 1 .5%
(3) 10th grade 5 2.5%
(5) 11th grade 117 58.5%
(5) 12th grade 8 4.0%
(5) Some college 58 29.0%
Participant’s Education coded from 1-5, presented next to ethnicity. 39 participants indicated more than one ethnicity.
42


Table 2
Skewness and Kurtosis Statistics of Depression Score
N Skewness Kurtosis
Statistic Statistic Std. Error Statistic Std. Error
Depression 197 0.75 0.17 0.43 0.35
43


Table 3
Hierarchical Recession Analysis
Model Unstandardized Coefficients B Std. Error Standardized Coefficients Beta t SiR.
1 (Constant) 14.508 2.084 6.963 .000
Gender (female) 4.325 1.440 .215 3.004 .003
Race (Black or African American) 3.163 2.627 .102 1.204 .230
Race (Asian) 2.722 2.503 .100 1.087 .278
Hispanic -.550 1.916 -.028 -.287 .775
Race (Other) -10.469 7.049 .106 1.485 .139
Race (American Indian or Alaskan Native) .152 2.929 .004 .052 .959
Race (Native Hawaiian or other Pacific Islander) .672 4.684 .011 .143 .886
Race (White) .412 1.787 .020 .230 .818
2 (Constant) 12.970 2.372 5.469 .000
Gender (female) 4.178 1.441 .208 2.900 .004
Race (Black or African American) 3.554 2.638 .115 1.348 .179
Race (Asian) 2.624 2.499 .096 1.050 .295
Hispanic -.481 1.913 -.024 -.251 .802
Race (Other) -10.505 7.034 .106 1.494 .137
44


Table 3 cont’d
Race (American Indian or Alaskan Native) .193 2.923 .005 .066 .947
Race (Native Hawaiian or other Pacific Islander) .826 4.675 .013 .177 .860
Race (White) .283 1.786 .014 .159 .874
Coping .325 .241 .096 1.348 .179
3 (Constant) 26.295 3.173 8.287 .000
Gender (female) 4.655 1.332 .231 3.495 .001
Race (Black or African American) 3.457 2.434 .112 1.421 .157
Race (Asian) 1.604 2.312 .059 .694 .489
Hispanic .457 1.773 .023 .258 .797
Race (Other) 6.979 6.519 .070 1.071 .286
Race (American Indian or Alaskan Native) 1.030 2.697 1.001 1.011 .991
Race (Native Hawaiian or other Pacific Islander) -1.217 4.328 -.019 -.281 .779
Race (White) -.456 1.653 -.022 -.276 .783
Coping .467 .224 .138 2.087 .038
Social Capital (Summed Score) -.975 .168 -.389 -5.799 .000
4 (Constant) 22.144 4.484 4.938 .000
Gender (female) 11.128 5.126 .553 2.171 .031
45


Table 3 cont’d
Race (Black or African American) 3.569 2.431 .115 1.468 .144
Race (Asian) 1.581 2.308 .058 .685 .494
Hispanic .433 1.769 .022 .245 .807
Race (Other) 7.501 6.519 .076 1.151 .251
Race (American Indian or Alaskan Native) -.546 2.721 -.013 -.201 .841
Race (Native Hawaiian or other Pacific Islander) -.741 4.336 -.012 -.171 .864
Race (White) -.383 1.651 -.018 -.232 .817
Coping .482 .224 .142 2.155 .032
Social Capital (Summed Score) -.687 .287 -.274 -2.480 .014
Social Capital* Gender -.449 .344 -.362 -1.307 .193
5 (Constant) 30.931 7.177 4.310 .000
Gender (female) 11.641 5.357 .579 2.173 .031
Race (Black or African American) .905 9.497 .029 .095 .924
Race (Asian) -4.954 7.936 -.182 -.624 .533
Hispanic -10.601 7.220 -.534 -1.468 .144
Race (Other) 35.895 47.223 .362 .760 .448
Race (American Indian or Alaskan Native) 6.122 9.785 .147 .626 .532
46


Table 3 cont’d
Race (Native Hawaiian or other Pacific Islander) -46.160 30.641 -.731 -1.506 .134
Race (White) -8.596 5.948 -.413 -1.445 .150
coping .426 .229 .125 1.857 .065
Social Capital (Summed Score) -1.277 .488 -.509 -2.614 .010
Social Capital * Gender -.498 .357 -.402 -1.393 .165
Social Capital * Black .221 .669 .106 .331 .741
Social Capital * Asian .467 .550 .246 .849 .397
Social Capital *Hispanic .743 .486 .611 1.528 .128
Social Capital * Other -2.876 4.432 -.308 -.649 .517
Social Capital * American Indian or Alaskan Native -.385 .631 -.143 -.611 .542
Social Capital * Native Hawaiian or other Pacific Islander 3.521 2.383 .710 1.477 .141
Social Capital *White .573 .400 .410 1.434 .153
a. Dependent Variable: Depression (Summed Score)
47


Table 4
Tolerance and VIF Statistics of Predictors of Depression
Predictors Collinearity Statistics Tolerance VIF
Gender 0.06 17.02
Race (Black) 0.04 22.62
Race (Asian) 0.05 20.31
Hispanic 0.03 31.68
Race (Other) 0.02 54.52
Race (American Indian or Alaska Native) 0.08 13.32
Race (Native Hawaiian or Other Pacific Islander) 0.02 56.50
Race (White) 0.05 19.58
Coping 0.91 1.09
Social Capital 0.11 9.11
SC*gender 0.05 19.95
SC*Black 0.04 24.80
SC*Asian 0.05 20.06
SC*Hispanic 0.03 38.40
SC* Other 0.02 54.02
SC* American Indian or Alaska Native 0.08 13.18
SC* Native Hawaiian or Other Pacific Islander 0.02 55.34
SC*White 0.05 19.62
48


FIGURES
Scatterplot
Dependent Variable: Depression (Summed Score)
Regression Standardized Predicted Value
Figure 1. Scatterplot of standardized predicted values against the standardized residuals in
49


Normal P-P Plot of Regression Standardized Residual Dependent Variable: Depression (Summed Score)
Figure 2. Normal probability plot of residuals in predicting depression
50


Frequency
Histogram
Dependent Variable: Depression (Summed Score)
Mean = -4.99E-16 Std. Dev. = 0.969 N = 197
Regression Standardized Residual
Figure 3. Histogram of depression
51


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APPENDIX A
Demographic Questionnaire
1. How old are you?
a. 14
b. 15
c. 16
d. 17
e. 18
f. 19
g- 20
h. 21
i. Other
j- Don't want to answer
If Other, please specify:_______
2. What grade are you in? (If you are currently on vacation between grades, please indicate the grade you will be in when you go back to school)
a. 6th
b. yth
c. 8th
d. 9*
e. 10th
f. 11th
g- 12th
h. Not currently in school
i. Other
j- Don’t want to answer
If Other, please specify:
Gender?
a. Female
b. Male
c. Transgendered, Male to Female
d. Transgendered, Female to Male
e. Other/don’t want to answer
Are you Hispanic or Latino?
a. Yes
b. No
c. Don’t know/Don’t want to answer
What is your race? (Check all that apply)
a. American Indian or Alaska Native
b. Asian
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c. Black or African American
d. Native Hawaiian or Other Pacific Islander
e. White
f. Other
g. Don’t want to answer
If Other, please specify:____
6. When you are at home or with your family, what language or languages do you usually speak? (Choose all that apply)
a. English
b. Spanish
c. Chinese language (Mandarin or Cantonese)
d. Other
e. Don’t want to answer
If Other, please specify:____
7. Were you bom in the US?
a. Yes
b. No
c. Don’t want to answer
8. How many years have you lived in the US? (Choose one)
a. 10 years or more
b. Between 5 and 9 years
c. Less than 5 years
d. Don’t want to answer
9. Were your parents bom in the US? (Choose one)
a. No
b. One but not both
c. Both
d. Don’t want to answer
10. What is the highest grade that your mother completed? (choose one)
a. Less than high school
b. High school graduate
c. Some college
d. College graduate or higher
e. I don’t know
f. I don’t want to answer
11. What is the highest grade that your father completed? (choose one)
a. Less than high school
b. High school graduate
c. Some college
d. College graduate or higher
e. I don’t know
f. I don’t want to answer
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12. During most of the time you have grown up, who have you lived with? (check the best answer)
a. Mother and Father
b. Mother and Stepfather
c. Father and Stepmother
d. Father only
e. Mother only
f. Guardian
g. Other
If you have lived most of the time with someone besides those listed, please tell us who_________
13. How many people currently live with you in your house (please do not count yourself—choose one answer)
a. 1
b. 2
c. 3
d. 4
e. >5
14. Of the list here, which best describes your living conditions? (choose one)
a. I or a family member own the residence where I live
b. I or a family member rents the residence where I live
c. I am living with friends who are not family
d. I live in transitional housing such as a hotel
e. I live in a group home such as a halfway house
f. I stay in shelters
g. I do not have a home
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APPENDIX B
Center for Epidemiologic Studies-Depression scale (CES-D)
For the following questions indicate how often were each of the following true during the last week. (Choose 'never or rarely', 'sometimes', 'a lot of the time', 'most of the time or all the time'.)
1. You were bothered by things that usually don't bother you.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
2. You didn't feel like eating, your appetite was poor.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
3. You felt that you couldn't shake off the blues, even with help from your family and friends.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
4. You felt that you were just as good as other people.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
5. You had trouble keeping your mind on what you were doing.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
6. You felt depressed.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
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7. You felt that you were too tired to do things.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
8. You felt hopeful about the future.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
9. You thought your life had been a failure.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
10. You felt fearful.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
11. You were happy.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
12. You talked less than usual.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
13. You felt lonely.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
14. People were unfriendly to you.
a. Never or rarely
b. sometimes
c. a lot of the time
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d. most of the time or all the time
15. You enjoyed life.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
16. You felt sad.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
17. You felt that people disliked you.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
(Choose 'never or rarely', 'sometimes', 'a lot of the time', 'most of the time or all the time'.)
1. You were bothered by things that usually don't bother you.
a. Never or rarely
b. sometimes
c. a lot of the time
d. most of the time or all the time
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APPENDIX C
Social Capital Questions
1. Adult Info- How often do the adults in your life give you information on how to do things?
a. Never
b. Rarely
c. Sometimes
d. Most of the time
e. All of the time
f. Not sure/Don’t want to answer
2. Adult Advice- How often do you ask the adults in your life for advice?
a. Never
b. Rarely
c. Sometimes
d. Most of the time
e. All of the time
f. Not sure/Don’t want to answer
3. Adult Wellbeing- How often do the adults in your life express interest and concern in your well-being?
a. Never
b. Rarely
c. Sometimes
d. Most of the time
e. All of the time
f. Not sure/Don’t want to answer
4. Adult Interest- How often do the adults in your life talk with you about things you are interested in?
a. Never
b. Rarely
c. Sometimes
d. Most of the time
e. All of the time
f. Not sure/Don’t want to answer
5. Adult Support- How often do the adults in your life provide you with love and support?
a. Never
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b. Rarely
c. Sometimes
d. Most of the time
e. All of the time
f. Not sure/Don’t want to answer
6. Adult Expectations- How often do the adults in your life make it clear to you what they expect of you?
a. Never
b. Rarely
c. Sometimes
d. Most of the time
e. All of the time
f. Not sure/Don’t want to answer
7. Volunteer- How often do you volunteer in your community?
a. Never
b. Rarely
c. Sometimes
d. Most of the time
e. All of the time
f. Not sure/Don’t want to answer
8. Clubs- How often do you take part in clubs or groups in school or out-of-school? (this could be like a crew, or group of friends that meets in an organized way).
a. Never
b. Rarely
c. Sometimes
d. Most of the time
e. All of the time
f. Not sure/Don’t want to answer
9. Sports- How often do you participate in sports in school or out-of-school?
a. Never
b. Rarely
c. Sometimes
d. Most of the time
e. All of the time
f. Not sure/Don’t want to answer
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10. Arts- How often do you participate in music, dance, theatre, or other arts in school or out-of-school?
a. Never
b. Rarely
c. Sometimes
d. Most of the time
e. All of the time
f. Not sure/Don’t want to answer
11. Religion- How often do you attend religious services like church service, a bible study, or a spiritual gathering?
a. Never
b. Rarely
c. Sometimes
d. Most of the time
e. All of the time
f. Not sure/Don’t want to answer
12. Support Neighborhood- Do you feel like your neighborhood and community are supportive places?
a. Not at all
b. Not very
c. Somewhat
d. Pretty supportive
e. Very supportive
f. Not sure/Don’t want to answer/Not applicable
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APPENDIX D
Coping Questions
Think about the times when you have felt stress or been in stressful situations in the past three months, did you do any of the following (Check all that apply)
1. Coping 1 Break an upsetting problem down into smaller parts
2. Coping 2 Sort out what can and cannot be changed
3. Coping 3 Make a plan of action and follow it to address a problem
4. Coping 4 Leave options open when things get stressful
5. Coping 5 Think about one problem at a time
6. Coping 6 Find solutions to your most difficult problems
7. Coping 7 Make unpleasant thoughts go away
8. Coping 8 Take your mind off unpleasant thoughts
9. Coping 9 Stop yourself from being upset by unpleasant thoughts
10. Coping 10 Keep from feeling sad
11. Coping 11 Get friends to help you with the things you need
12. Coping 12 Get emotional support from friends and family
13. Coping 13 Make new friends
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Full Text

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YOUTH AND DEPRESSION: IS SOCIAL CAPITAL A DETERMINING FACTOR? by TATTIANA ROMO B.A. Colorado Mesa University, 2010 M.A. University of Colorado Denver, 2015 A dissertation submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Clinical Health Psychology Program 2019

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ii © 201 9 TATTIANA ROMO ALL RIGHTS RESERVED

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iii This dissertation for the Doctor of Philosophy degree by Tattiana Romo h as been approved for the Clinical Health Psychology Program by Kevin Everhart, Chair Evelinn Borrayo, Advisor Kristin Kilbourn David Cordova Date: August 3 , 201 9

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iv Tattiana Romo (Ph.D., Clinical Health Psychology) Youth and Depression: Is Social Capital a Determining Factor? Dissertation directed by Evelinn Borrayo ABSTRACT Adolescence is a critical developmental period which consists of physical, cognitive, and social changes. These changes have the potential to lead to mental health risks such as depression . Although there are evidence based interventions targeting adolesc ent mental health, the prevalence of depression continues to rise, which has led researchers to s tudying alternative psychosocial factors that may inform the development of future interventions, such as exploring social capital. A h ierarchical m ultiple re gression was performed to test whether social capital, coping, gender, and ethnicity were significant predictors of depression. In terms of model fit, the final regression model created in predicting depression was statistically significant (F(10, 186) = 5 . 430 , p = .00 0 ). This indicated that the regression model with the social capital, coping, and gender, had an acceptable model fit. Examination 0. 975 ) had a significant negative pr edictive relationship with depression, which indicated that youth who reported higher social capital endorsed fewer depression sy mptoms. Additionally, gender = 4. 655 ) had a significant positive predictive relationship with depression symptoms , indicating females experienced more depression symptoms compared to males , indicating females experienced more depression symptoms compared to males . Coping, ethnicity, and interactions were not significant in the hierarchical multiple regression model. The form and content of this abstract are approved. I recommend its publication . Approved: Evelinn Borrayo

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v DEDICATION This dissertation is dedicated to my loving family. Sergio and Maria Romo, thank you for all the sacrifices you had to make to ensure t hat I could have a great future. I would not have made it here without your encouragement, support, and love. You have always believed in me and I am truly grateful for that support. John Heuton, I love you immensely. Y ou push me to become a better person every day. I appreciate the nights you would stay up and work with me. Thank you for helping me believe that I am smart, capable, and worthy of a PhD. Milly, Katherine, Alex, and Andrew, I hope that you can value and appreciate the importan ce of an educati on . I hope you set your mind s on becoming better educated than your parents and pass along the desire and determination to gain knowledge and wisdom. My PhD was hardly easy to achieve, and I wanted to quit several times, but it was important to surround my self with loving and supportive family and friends because sometimes all you have to hear is, "ya mero mija" "almost the re baby." Sergio and Deedee Romo, I hope I made you proud, a sister cannot ask for better loving supportive siblings . T his is for our children; I hope the next generation of children in our family believe that they can accomplish their dreams with hard work and determination . I may be the first female PhD in our family, but I will not be the last.

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vi ACKNOWLEDGEMENTS I would like to start off by acknowledging my advisors, Dr. Evelinn Borrayo and Dr. Kevin Everhart. Dr. Borrayo, thank you for giving me the opportunity to join your lab early in my graduate training. I appreciate you hanging on through my thesis and now m y dissertation. I am truly grateful for the patience and direction you have provided. Dr. Kevin Everhart, thank you for taking me under your wing. I appreciate you not giving up on my training when things got tough and for providing support to get me thro ugh the next phases of this career. Dr. David Cordova, thank you for joining my dissertation committee and provid ing yo ur knowledge and guidance throughout this study. Dr. Kristin Kilbourn, you have been a great mentor and support. Thank you for being such a positive clinical supervisor. I am truly grateful for the guidance you have all provided. Dr. Jo Vogeli, Dr. Lacey Clement, and Dr. Kaile Ross, you are such amazing women and I feel so proud to call you colleagues. I am so proud and always so interested in what you are doing with your lives. I hope that we remain lifelong friends.

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vii TABLE OF CONTENTS CHAPTER I. INTRODUCTION ................................ ................................ ................................ ..... 1 Adolescent and Youth Development ................................ ................................ ......... 1 Depression among Youth ................................ ................................ ........................... 3 Gender and Depressi on ................................ ................................ .............................. 8 Ethnicity and Depression ................................ ................................ ......................... 10 Social Capital Theory ................................ ................................ .............................. 1 3 Social Capital and Mental Health ................................ ................................ ............ 1 5 Social Capital Measurement Issues ................................ ................................ ......... 1 8 Summary and Proposed Study ................................ ................................ ................. 1 9 II. METHOD ................................ ................................ ................................ ................ 2 2 Participants ................................ ................................ ................................ ............. 2 2 Recruitment and Data Collection ................................ ................................ ........... 2 2 Measure s ................................ ................................ ................................ ................ 23 . 23 Data Cleaning ................................ ................................ ................................ .......... 26 Data Analysis ................................ ................................ ................................ ......... 26 III. RESULTS ................................ ................................ ................................ .............. 30 Hypothesis 1: Coping ................................ ................................ ............................. 30 Hypothesis 2 : Social Capital ................................ ................................ .................. 30 Hypothesis 3 : Social Capital and Gender ................................ .............................. 31 Hypothesis 4 : Social Capital and Ethnicity ................................ ........................... 3 1

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viii Multicollinearity ................................ ................................ ................................ ................ 3 2 IV. DISCUSSION AND CONCLUSIONS ................................ ................................ . 34 Intervention and Policy ................................ ................................ .......................... 37 Study Limitations ................................ ................................ ................................ ... 39 Future Directions ................................ ................................ ................................ ... 40 REFERENCES ................................ ................................ ................................ ................. 5 2 APPENDIX A. Demographic Questionnaire ................................ ................................ ............ 67 B. Center for Epidemiologic Studies Depression Scale (CES D) ......................... 70 C. Social Capital Questions ................................ ................................ .............. D. Coping Questions ................................................... .............................. ........... ...76

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ix LIST OF TABLES TABLE 1. Demographic Characteristic ... 4 2 2. Skewness and Kurtosis Statistics of Depression Score .............. .................... ........ .......... ... 4 3 3. Hierarchical Multiple Regression Results . 4 4 4. Tolerance and VIF Statistics of Predictors of Depression 4 8

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x LIST OF FIGURES FIGURE 1. Scatterplot of standardized predicted values. ........................... ......... ................................... 49 2. Normal probability plot of residuals in predicting depression.. ................... ...... ............ . ..... 5 0 3. ....................................................... .......... ..... ........... 5 1

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xi LIST OF ABBREVIATIONS COMIRB Colorado Multiple Institutional Review Board CES D Center for Epidemiologic Studies Depression scale DSM V Diagnostic Manual of Mental Disorders, Fifth Edition

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1 CHAPTER I INTRODUCTION Adolescent and Youth Development The World Health Organization (2011) defines youth as individuals between the ages of 10 and 24 years of age. For the purpose of this study, the World Health Organizations definition of youth will encompass the population of interest, however , an understanding of adolescent development is warranted to comprehend the challenges youth may encounter biologically, socially, and psychologically. Adolescent and youth terms will be used interchangeabl y when addressing the literature of this population. Adolescence is defined as the second decade of life, a period in which a child transitions into adulthood. Adolescence is an important developmental period which can be conceptualized into three stages: Early (10 13 years), middle (14 16 years), and late (17 19 years) (Kar, Choudhury, & Singh, 2015). Physical, cognitive, and emotional changes take place during this sensitive time period ( Seifert, & Hoffnung, 2000). Duri ng this time c hildren gain about 50% of their adult body weight, are capable of reproducing, and experience significant changes in their brains (McNeely & Blanchard, 2009). Adolescents may also become particularly concerned about their body image, as they reach puberty. Physical changes can vary throughout adolescence which may cause stress and anxiety, although very normal for this time period ( Seifert, & Hoffnung, 2000) . What may be stressful for adolescents are the competing stressors such as the pressu re they feel to develop sexual identities, relationships, and a strong desire to feel accepted (McNeely & Blanchard, 2009). Between the ages of 10 14 years old a sense of identity begins to develop and therefore an increased self focus begins to become m ore obvious. Relationships also change

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2 in that friendships appear to become more important than relationships with their parents (McNeely & Blanchard, 2009). Abstract thinking, reasoning, and decision making is a critical element of the changes occurring during this development period ( Seifert, & Hoffnung, 2000) . Adolescents continue to develop a more solid personal identity, increase their independence, become more concerned for others, increased ability for delayed gratification, and social networks typi cally expand with newly formed friendships (McNeely & Blanchard, 2009). Cognitive changes involve the ability to think in abstract terms, debating, challenging ideas or values, and questioning (which help shape their ability to judge and solve problems), g oal setting, and thinking about their future (McNeely & Blanchard, 2009). Advanced reasoning skills help adolescents think about hypotheticals and logical thought processes which are important to decision making. For example, abstract thinking may embrac e contemplating thoughts regarding faith, love, trusts, beliefs, spirituality, (McNeely & Blanchard, 2009). Finally, metacognition is an important cognitive development during this period which allows for adolescents to think about how they are feeling, wh at they are thinking, and pondering about how they are perceived by others. Increased cognitive development is followed by an increase in emotional competence (McNeely & Blanchard, 2009). Emotional and social competences are learned skills that help adole scents navigate their emotions and how they relate with others. Social competence development is also important to peer relationships which tend to take importance over relationship and time with family (McNeely & Blanchard, 2009). For example, peers gene rally influence values, attitudes, behaviors, identity, and relationships. Adolescents develop their ability to label their emotions, such as recognizing anxiety or sadness in relevant situations ( Seifert, &

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3 Hoffnung, 2000) . Difficulties may arise when l a ck of awareness of feelings inhibits (McNeely & Blanchard, 2009). With significant changes to emotional development, it is estimated that one in ten adolescents may experience a serious emotional disturbance that can affect their ability to function at home, school, or their community. Depression has been identified as one of the most common mental health disorders in adolescence (McNeely & Blanchard, 2009). Accordi ng to the Diagnostic Manual of Mental Disorders, Fifth Edition (DSM V) depression may include symptoms of sadness, lack of interest and pleasure in daily activities, difficulties with sleep, significant weight changes, lack of energy, inability to concentr ate, feelings of worthlessness and/or guilt, and recurrent thoughts of death or suicide (American Psychiatric Association, 2013). According to the Diagnostic Manual of Mental Disorders, Fifth Edition (DSM V) depression may include symptoms of sadness, lac k of interest and pleasure in daily activities, difficulties with sleep, significant weight changes, lack of energy, inability to concentrate, feelings of worthlessness and/or guilt, and recurrent thoughts of death or suicide (American Psychiatric Associa tion, 2013). It is important to note the difference between diagnosed depression in the literature and depression symptoms, studies have explored both given the benefits and challenges of measuring depression and depression symptoms. This study will focus on depression symptoms; however, the literature will include studies that have measured depression either way. Depression among Youth Experiencing a wide range of emotions is expected during adolescent development. However, mental health disorders charact erized by symptoms affecting emotions, cognitions, and behaviors can also have an impact on and interfere with daily functioning (National

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4 Institute of Mental Health, 2016). During adolescence, youth are preoccupied with having to build an understanding a round healthy relationships, exploring interests and identity, learning and developing important life skills, entering the workforce, and depression during this time can seriously affect further successful development. For example, long term socioec onomic standing, peer, familial, and romantic relationships may all be negatively influenced by depression experienced during adolescence (Clayborne, Varin, & Colman, 2019). According to the 2016 National Survey on Drug use and Health, 12.8% of the U.S. ad olescent population aged 12 to 17, an estimated 3.1 million, indicated that they experienced at least one major depressive episode. Additionally, approximately 9.0% of U.S. adolescents reported at least one major depressive episode with severe impairment, which is an estimated 2.2 million adolescents. Approximately 60% of adolescents who reported a major depressive episode indicated that they did not receive treatment for their depression. The U.S. Department of Health & Human Services (2018) advised that youth with depression can experience problems with their school work, relationships, and health if left untreated. Of concern is that the prevalence rates for youth who are experiencing mental health illness may be underrepresented given that youths who a re not seriously impaired may be excluded from meeting the criteria for depression which may result in lower prevalence rates reported (Novak, Colpe, Barker, & Gfroerer, 2010). As children transition into adolescence the risk for depression greatly increa ses. Limited data on the trends in prevalence of depression in adolescents and young adults warranted a study exploring the national trends in a 12 month prevalence of major depressive episodes ( Mojtabai, Olfson, & Han, 2016). Th e study examined cross sectional data from the U.S. population gather ed from the National Surveys on Drug Use and Health from 2005

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5 to 2014. Participants included adolescents aged 12 to 17 and adults 18 to 25 years. The results of this study indicated that during the 12 month prevalence of major depressive episodes in adole scents and young adults, there was an increase from 8.7% to 11.3% (2005 to 2014) in adolescents and from 8.8% to 9.6% in young adults. The increase was significant only in the age ranged f rom 12 to 20 years. Addressing mental health disorders such as depression in youth is important because once established such disorders become risk factors for adult psychopathology (Bluth, Campo, Rutch, & Gaylord, 2017). In addition, several studies h ave found that experiencing depression in early life is associated with poor outcomes such as being at risk of obesity, type 2 diabetes, and comorbid mental health disorders (i.e. anxiety and substance use) (Clayborne, Varin, & Colman, 2019). Goodman and Whitaker (2002) conducted a longitudinal study to determine whether depressed mood predicted the development of and perseverance of obesity in adolescents. Their sample consisted of 9 , 374 adolescents in grades 7 12 th grade that completed the National Longi tudinal Study of Adolescent Health. Baseline data were gathered followed by depressed mood and body mass index information at 1 year follow up. Goodman and Whitaker's (2002) findings supported their hypotheses and found that depressed adolescents were at an increased risk for development and continuance of obesity. Adolescent depression has not only demonstrated the health consequences, but studies have also provided support for psychosocial outcomes (Clayborne, Varin, & Colman, 2019). Psychosocial outc omes influenced by early life depression include lower educational attainment, unemployment, and lower perceived social support (Fergusson & Woodward, 2002; Galambos, Zeng, Sethilselvan, & Colman, 2002). Fergusson and

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6 Woodward (2002) conducted a longitudin al study of New Zealand children to examine to what extent adolescents (ages 14 16) with depression develop an increased risk of subsequent mental disorders (e.g. substance use), academic underachievement, and reduced life opportunities. The researchers f ound that youth with depression were significantly at risk of developing major depression, anxiety disorders, nicotine dependence, alcohol abuse or dependence, suicide attempt, educational underachievement, unemployment, and early parenthood. Results of as sociations were similar for males and females . However, Fergusson and Woodward (2002) highlight that the evidence of their study does not indicate that the psychosocial adverse outcomes are a consequence of early depression, but instead that they develop as a result of common social, familial, and personal factors (e.g. parental change, maternal educational underachievement, childhood sexual abuse, IQ, etc.). The authors emphasize the context of a young person's life history, social, and personal circumsta nces when looking at early depression. Clayborne, Varin, and Colman (2019) conducted a systematic review and meta analysis on youth depression and long term psychosocial outcomes. The study gathered articles published from 1980 through 2017 from five dat abases. The researchers wanted to combine the data investigating the relationship between youth depression and long term psychosocial outcomes; educational attainment, income, employment, pregnancy/parenthood, marital and relationship status, social suppo rt, and loneliness. The results from this meta analysis found that youth depression was indeed associated with outcomes such as failure to complete secondary school, unemployment, and pregnancy/parenthood. These studies highlight the importance of targeted mental healthcare early in youth development to reduce these adverse psychosocial outcomes. The authors highlight the complexity of the processes

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7 relating depression and psychosocial outcomes. For example, given the noteworthy functional impairment cause d by depression, schoolwork may become too difficult to comprehend, which in turn can affect how an adolescent performs and therefore affect their educational attainment. Similarly, lack of educational attainment can influence employment opportunities (Lew insohn, Rohde, Seeley, Klein, & Gotlib , 2003). Depression may also a ffect school attendance as highlighted by a study conducted by Suris, Michaud, and Viner (2004), which can influence educational attainment and the formation and maintenance of relationshi ps because of the reduced time spent with peers. The consequences of early depression can be seen throughout the lifespan such as the negative impact on social impairment may affect the development of stable relationships and social networks which may also influence workplace advancement in their future (Burns, Fitzpatrick, Pinfold, & Priebe, 2007; functioning may be the result of socioeconomic consequences of depression as demonstrated by se veral studies (Bhoman, Hjern, et al., 2011; Berndt, Koran, Finkelstein, et al., 2000; Jonsson, Bohman, Hjern, von Knorring, Olsson, & von Knorring, 2010). Studies have shown the persistence of adolescent depression into adulthood, which may consist of recu rrent bouts of depression, can also be stressful for partners and in turn lead to relationship problems, dissolution, and/or divorce (Rehman, Gollan, & Mortimer, 2008). As extensively highlighted, depression experienced during an important developmental p eriod, adolescence, may have serious adverse consequences leading towards a future complicated by poor psychosocial outcomes compared to non depressed peers. Identifying and targeting at risk sub groups is crucial in the prevention and treatment of youth d epression, given that the research has identified associations with school failure, alcohol and substance use disorders,

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8 and suicide. Suicide being the third leading cause of death among adolescents ages 15 19 (Grose et al., 2014). Continual efforts towa rds understanding the effects of adolescent depression can inform future interventions. Unfortunately, recent studies have found that there have not been many significant changes in mental health treatment among youth (Curtin, Warner, & Hedegaard, 2016). With little change in mental health treatments, untreated depression continues to rise. Understanding what factors may be protective for youth mental health may be helpful in the development of future mental health treatments tailored for youth . Hayden and Mash (2014) identified interconnected and shared protective factors for psychopathology such as community factors (i.e., caring community relationships and positive role models), psychological factors (i.e. high self esteem and self efficacy, positive coping strategies, high distress tolerance, and resilience), and familiar factors (i.e. availability of resources, positive parenting, and spiritual beliefs). Treatment and interventions for depression have pre dominantly consisted of psychotherapeutic medications and evidenced based therapies (Mark, Levit, Buck, Coffey, & Vandivort Warren, 2007). Mark et al. (2007) highlights the need to examine other treatment strategies given the concerns regarding utility and validity of current treatments to address mental health illness. Gender and Depression Studies have identified inclinations in major depressive episodes differing among males and females. Mojtabai, Olfson, and Han (2016) found that their study aligned with previous findings on the larger increases in depressive symptoms experienced in female s compared to males. Additionally, Curtin, Warner, and Hedegaard (2016) found greater increases in suicide among female youth compared to males. The prevalence of major

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9 depressive episode was higher among female youth (19.4%) compared to males (6.4%) (Nati onal Institute of Mental Health, 2016). Psychosocial factors and biological changes due to puberty explain the higher vulnerability females have for depression (Lewis, Kremer, Douglas, Toumborou, Hameed, Patton, & Williams, 2015). However, the rise in fem ale depression despite biological vulnerability has given way for researchers to identify social, cultural, and historical factors that influence depressive symptoms (Lewis, Kremer, Douglas, Toumborou, Hameed, Patton, & Williams, 2015). For example, resea rchers hypothesize that female youth may be exposed to greater depression risk factors in the recent years due to cyberbullying and increased use of mobile phones (Mojtabai, Olfson, and Han, 2016). As previously discussed, adolescents become more self foc used given their increased metacognitive ability, particularly for females, this inwardly directed focus may lead to increases in rumination which has also been linked to depression and anxiety (Nolen Hoeksema, 2000 ). In addition, f emales tend to experience significant increases in stressors and stressful life events during adolescence, which in turn, may contribute to vul nerability to depressive symptoms (Hankin, Mermelstein, & Roesch, 2007). A recent study conducted by Malooly, Flannery, and Ohannessian (2017), surveyed 905 adolescents in the U.S. to understand gender and racial differences in depressive symptomatology and coping. The results indicated that female youth reported more depressive symptomology than males and were more likely to engage in coping strategies such as seeking emotional social support, instrumental social support, and venting emotions. As pr eviously highlighted, late adolescence is a critical period in which adolescents are at a higher risk, studies show a six fold surge of first episodes of clinical depression ( Hank in and Abramson, 2002 , Grose et al., 2014). The literature on gender

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10 differences in depression have been supported, furthermore, Hankin and Abramson (2002) add that there are also important e thnic differences in depression to consider . Ethnicity and Depression Although studies show that non Hispanic White youth have the highest suicide rates, the incidence has been growing for racial and ethnic minorities (Balis & Postolache, 2009). Hankin and Abramson (2002) found that there are also important ethnic differences in depression. The researchers note that while non Hispanic White adults have the highest rates by gender, rac e and ethnicity ( Sen, 2004 ). The literature on racial and ethnic differences in d epressive symptoms has been inconsistent (Maag & Ir vin, 2005). Malooly, Flanery, and Ohann the authors noted that when racial/ethnic differences have been examined, most of the studies included populations consisting of non Hispanic White and African American adolescents (Malooly, Flannery, & Ohannessian, 2017). According to the 2016 National Survey on Drug use and Health, the prevalence of major depressive episode was highest among adolescents reporting two or more ra ces (13.8%) compared to non Hispanic White adolescents. Even when controlling for family structure, socioeconomic status, and community ethnic composition, Hispanic and African American adolescents are still more likely to experience symptoms of depression compared to Caucasian adolescents (Wight et al., 2006). Mojtabai et al.'s (2016) study found that Hispanic and non Hispanic White adolescents had greater prevalence of depression compared to Black and Asian adolescents. However, their findings concluded that biracial adolescents had the highest rates of depression (Mojtabai et al., 2016). Studies have explored

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11 reasons for ethnic and racial differences in depression and have found that symptoms of depression may be linked with discrimination and/or victim ization which may also complicate detecting symptoms of depression among racial and ethnic minority populations (Stockdale, Lagomasino, Siddique, McGuire & Miranda, 2008). Studies have found that there may be a relationship among rates of increasing racial discrimination in the U.S. and mental health disparities (American Psychological Association, 2016). Racial identity development during this critical developmental period can greatly impact mental health of adolescents. Cross and colleagues proposed Blac k identity development models which focus on racial identity development (Cross Jr. & Strauss, 1998). Their theories led to further identity development models that were applicable to Latino/Latina, Asian American, disabled, feminist, and gay and lesbian ( Cross Jr. & Strauss, 1998). Generally, these earlier racial identity models described an early stage in which the individual has a discrimination encounter from which psychological well being may follow if the individual develops a positive racial or ethni c identity and may resolve the distress experienced from this encounter (Cross, 1995). Helms (1995) developed the Person of Color Racial Identity Model which discusses identity formation for Caribbean Black American and African American adolescents. Iden tity formation describes the conscious processes of examining one's feelings, thoughts, behaviors, and relating to others who may or may not share commonalities (Cross, 1995). In this model, identity formation is associated with perceived discrimination an d how it influences conflicted racial identity versus resolved racial identity. Conflicted racial identity is associated with increased psychological distress (Sanchez, Bentley Edwards, Matthews, & Granillo, 2016).

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12 Sanchez, Bentley Edwards, Matthews, and Granillos (2016) study examined the relationship among racial identity, perceived discrimination, and psychological concerns among 189 Caribbean Black American and African American adolescents and found that for all participants having a less mature racia l identity profile was related to perceived discrimination and psychological concerns. The researchers found that in this study Caribbean Black American adolescents presented with confusion and/or anxiety over awareness of the salience of race such as bei ng racially profiled for being Black or being ignored by their teacher in class which results in maintaining a preference to not associate with their race. Furthermore, Caribbean Black American adolescents identified as having conflicted racial identity m ay experience distress because they were mistaken as Black due to their phenotype. To cope with the effects of racism, dissonance and conformity to racial identity attitudes may be at play, however, research has found that they result in negative mental h ealth outcomes (Munford, 1994). Similarly, immigration and acculturation have also been identified as risk factors for depression. Acculturation is a process of psychological and cultural changes that involve accommodation between two groups, usually some form of longer term adaptation to living from the groups in contact (Berry, 1992). For example, acculturation may involve learning the language, sharing food, and adopting forms of dress and social interactions (Berry, 2005). Acculturation involves adapt ion in physical, psychological, financial, spiritual, social, language, and family adjustments which can be a very stressful process for individuals (Mui & Kang, 2006). The process of acculturation can pose socio psychological stress for immigrant adolesce nts. Acculturation stress is broadly defined as the stress individuals encounter from the acculturation process (Gelfand & Yee, 1991). Acculturation stress may

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13 result from having to negotiate in group and outgroup challenges (Patil, Porche, Shippen, Dallen back, & Fortuna, 2018). Developing a positive racial/ethnic identity in a dominant non Hispanic White culture carries many challenges for minority adolescents, which can unfortunately lead to mental health risks (Patil, Porche, Shippen, Dallenback, & Fortu na, 2018). Wakefield, David, & Hudley's (2007) review support the findings that a strong, positive ethnic identity is beneficial for adolescent mental health. They further encourage strategies parents, teachers, and school can take to support positive eth nic identity development in racial and ethnic minority students such as parents teaching their children how to cope with perceived discrimination and stereotypes and working collaboratively with schools implementing culturally competent parent education pr ograms (Hudley, & Taylor, peer support may help adolescents develop healthy and positive ethnic identities (Wakefield, David, & Hudley, 2007). Social Capital Theory Soci al capital theory is a complex concept originating from the works of Bourdieu, Putnam, and Coleman (McPherson, Kerr, Morgan, McGee, Cheater, McLean, and Egan, 2013). Bordieu and colleagues were the original researchers investigating and trying to understan the actual or potential resources which are linked to possession of a durable network of more or less institutionalized relationships of mutual acquaintance and recogniti level resource as a product of norms and social trust that facilitate coordination an

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14 individuals social networks and the norms of reciprocity and trustworthiness that arise from ) conceptualized social capital in relation to family relationships. Social capital is currently articulated and researched slightly differently from and measure ment. Although, the construct is conceptualized slightly differently depending on researcher's definition, the common thread appears to be that social capital takes into consideration the importance of social networks and relationship to bring about socia l, economic, and health changes among different groups, hierarchies, and societies (McPherson, Kerr, Morgan, McGee, Cheater, McLean, & Egan, 2013). Theoretically, social capital operates at multiple levels within the social structure including bonding, bri dging, and linking being commonly studied in the literature (Gittell & Vidal, 1998; Putnam, 2000; Stone & Hughes, 2002; Van Deth, 2003). Kawachi (1997) has conceptualized social capital as consisting of both structural and cognitive dimensions with severa l additional components such as bonding and bridging social capital. Bonding and bridging social capital have literature. Bonding social capital refers to relationships between individuals or groups sharing similar demographic characteristics. Bridging and/or linking social capital refers to relationships across different communities or individuals (e.g., neighbors and work colleagues) (Narayan, 1999). Linking is also included in the social c apital construct in that they are the social connections formed across power hierarchies, for example teachers and students. In addition, social capital consists of four analytic levels in relation to health: the macro level (countries, states regions, an d local municipalities), meso level (neighborhoods

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15 and blocks), micro level (social networks and social participants), and individual psychological level (trust and norm) (Macinko & Sartfield, 2001). Social Capital and Mental Health McPherson, Kerr, McGee, Morgan, Cheater, McLean, and Egan (2014) conducted an integrative systemic review exploring the association between social capital and mental health and behavior problems in children and adolescents. This review focused on the infl uence of family and community social capital on mental health and was the first to focus on children and adolescents. In addition, their article not only identifies the literature of the subject matter but also makes recommendations, discusses implication s for future research, and policy development. In this review, studies conducted between 1990 and 2012 which categorized indicators of family and community social capital, were included. FSC explored the role of family structure, quality of parent child relations, adult interest in the child, parents monitoring of the child, and extended family support and exchange. They also included CSC, which examined the social support networks, civic engagement in local institutions, trust and safety, religiosity, t he quality of the school, and the quality of the neighborhood. The researchers involved reviews in which children (5 10 years old) and adolescents (10 19 years old) were included. The review included studies in which they directly collected data from the young person in addition to data gathered from a parent, teacher, or professional reporting on the young person. The outcomes of interest were measured self esteem and self worth, internalizing behaviors (e.g. depression and anxiety), and externalizing b ehaviors (e.g. aggression, violence, conduct disorders, and disobedience). McPherson et al. (2013) identified 55 studies meeting their criteria for review. In 29 of the studies identified, ethnicity, race, or nationality of participants was not included, while 11

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16 studies reported that most of their sample was black. The remainder of studies included majority participants of American Indian, Dutch, Latino, Mainland Chinese, and Southeast Asian descent. With respect to self esteem and self worth, four stu dies were identified in which self esteem and self worth was explored in the context of FSC. There appears to be support for positive communication, nurturance, and low levels of conflict between parent adolescents resulted in better self esteem/worth (Bi rndorf et al. 2005; Yugo & Davidson, 2007; Ying & Han, 2008). Parental monitoring and control were found to have negative effects on self esteem/worth. CSC also provided positive outcomes in that those who reported positive relationships beyond the famil y, feeling safe at school, and being engaged with school reported higher levels of self esteem/worth. When examining the literature on internalizing behaviors (includes thoughts, feelings, emotions, and behaviors directed inward), McPherson and colleague s (2014) were interested in studies exploring depressive symptoms, anxiety and social anxiety, moods, emotions, and composite scores that measured the mentioned behaviors. The researchers found that positive parent child relationships were associated with lower levels of internalizing behaviors (Caughy et al., 2008; Springer et al., 2006; Ying & Han, 2008). Furthermore, when children and adolescents live in higher quality and wider networks, they tend to report fewer internalizing behaviors. It is import ant to note that although findings suggest that health, in impoverished communities the opposite was the case. These families benefitted most from having their prim ary caregiver knowing fewer of their neighbors. Young people who live in a two parent family home were also less likely to have internalizing/externalizing problems (Galboda Liyange et al., 2003; Wen, 2008).

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17 A longitudinal study of adolescents and their p arents from seven cities in mainland China investigated the relationship between social capital in the family and the community with family human capital and financial capital on depressive symptoms of urban Chinese adolescents (Wu, Xie, Chou, Palmer, Gall aher, & Anderson Johnson, 2010). Their findings suggested that higher community social capital was associated with lower level of adolescent depressive symptoms. In addition, they reported that family social capital significantly mediated the effects of all other contextual factors on adolescent depressive symptoms. Surprisingly, increased depressive symptoms were observed in higher family financial capital. Higher financial capital was found to negatively affect family social capital. In their study, f emale adolescents reported more depressive symptoms due to less available family social capital. Rothon, Goodwin, and Stansfeld (2012) examined the relationship between family social support, community social capital and mental health and educational outcomes. Family social support was defined as quality of parent child relationships, evening meals with family, and parental surveillance while community social capital included parental involvement at school, sociability, and involvement in activities outside of the home. This was a longitudinal study in which baseline measures of social capital were taken when adolescents were 13 14 years old. Mental health was measured at 14 15 years old using the General Health Questionnaire (GHQ) which screens for anxiety and depression. Educational achievement was measured at 15 16 years old using national examination (General Certificate of Education). Results from this study found that "good paternal and maternal" relationships, higher parental surv eillance, and frequent evening family meals were associated with lower odds of poor mental health. Although the findings on mental health

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18 are encouraging, these results focus on the associations between mental health and educational outcomes, and do not d iscuss health outcomes. Few studies have examined health outcomes in relation to social capital, ethnicity, and mental health. Not only do adolescents benefit from the direct effects of their social networks, but studies found that they can also indirectl y benefit from social networks their parents are involved in (McPherson et al., 2014). McPherson et al. (2014) concluded that interventions targeted at improving the parent child relationship such as the Triple P Positive Parenting Program (Sanders, 2008 ) can be utilized as a protective intervention against adolescent health risk behaviors. There is substantial support for increase of self worth, self esteem, and confidence achieved through positive youth experiences that alleviate the effects of health stressors (McMahon Felix, & Nagarajan, 2011). Social Capital Measurement Issues As previously mentioned, social capital theory is complex and consists of multiple domains, because of this Putnam (1993) created a measurement of social capital theory wh ich consisted of summing several indicators of social capital characteristics: the existence of community networks, civic engagement, civic identity, reciprocity, and trust (Kritsotakis, Gamarnikow, 2004; Putnam, 1993). However, there is still a need for more consistent measures of social capital that consider the multiple dimensions of the construct but in relation to specific mental health. The social capital construct and its dimensions might be best understood and measured if framed in the context of the specific mental health outcomes of interest. The Integrated Questionnaire for the Measurement of Social Capital (SC IQ) attempts to measure six dimensions of social capital (groups and networks, trust and solidarity,

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19 collective action and cooperation , information and communication, social cohesion and inclusion, and empowerment and political action ( Grootaert et al., 2004). Although this measure is an attempt to address the critical issues of the social capital construct, it is not appropriate to app ly to youth samples . In addition, social capital in the context of youth also remains limited ( Grootaert, Narayan, Woolcock, & Nyhan Jones, 2004 ; Harpham, Grant, & Thomas, 2002 ; Lochner, Kawachi, & Kennedy, 1999 ; Pronyk et al., 2008). In an attempt to contribute to the existing social capital measurement literature addressing sexual and reproductive health, Cordova et al. (2017) identified factors related to social capital such as condom self efficacy, civic engagement, and adult and communit y support as a composite measurement of social capital aimed at addressing social capital and sexual and reproductive health. However, that study tailored their social capital measure to include items of sexual and reproductive health, which is not applica ble in this study. Summary and Pro posed Study Youth encompasses a developmentally challenging period and one in which potential for engaging in risk related activities is heightened (Smylie, Medaglia, and Maicka yndale, 2006). The rise in prevalence of depression may indicate the limited efficacy of existing interventions and prevention efforts regardless of increasing research in the treatment of depression (Mojtabai et al., 2016). Furthermore, mental health issues during youth are also important due to how preventable mental health and health risk behaviors are (CDCP, 2010; Kessler et al., 2005). The literature emphasizes the importance of social capital within the family context, specifically positive parent child interactions. There is a dearth of r esearch on the role of social capital in depressive symptomatology among youth compared to the literature on adults (Crosby, Holtgrave, DiClemente, Wingood, & Gayle, 2003 ; Murayama et

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20 al., 2012 ; Viner et al., 2012). Therefore, not only understanding the r elationship between social capital and adolescent relationships with parents, adults, and peers is important, it is also crucial to understand the effect of different cultural and environmental contexts. Moreover, research on the influence of social capit al and depression is important because it is possible that findings on social capital may aid policy endeavors that support interventions aimed at addressing and improving social capital among youth, leading to improvements in the prevention of depression symptoms among at risk youth. This study focuses on the impact of social capital, coping, gender , and ethnicity on youth depression symptoms. The hypotheses of this study are based primarily on evidence that social capital has been found to be inversely related to depressive symptoms ou tcomes ( Rothon, Goodwin, & Stansfeld, 2012) . In addition, there is empirica l evidence that supports females tend to endorse more depression symptoms than males. A hierarchical multiple regression model will test the effects of social capital, coping, gender, ethnicity, and depression symptoms. Further, interactions between social capital and gender, as well as social capital and ethnicity for depression symptoms will be examined in the hierarchical regression model. Based on the supporting evidence, we expect to replicate these finding and hypothesize the following: (1) When controlli ng for gender and ethnicity, coping has a negative relationship with depression, such that y outh (14 21 years old) who endorse more coping ha ve less depression symptoms. (2) Wh en controlling for gender, ethnicity, and coping, s ocial capital and depression symptoms will be inversely related such that y outh (14 21 years old) in the sample

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21 who have higher social capital will have lower depression symptoms than youth who have lower social capital . (3) There will be a significant interaction effect of social capital and gender on depression symptoms of youth (14 21 years old), such that females with lower social capital will have more depression symptoms compared to males. (4) There will be a significant interaction effect of social capital and ethnicity on depression symptoms of youth (14 21 years old). There will be significant ethnic group differences in the relationship between social capital and depression symptoms, such th at youth who identify as Hispanic, African American, and Asian (14 21 years old) who have lower social capital will have more depressive symptoms compared to non Hispanic White youth (14 21 years old). To test these hypotheses, the following specific aims are planned: Aim 1: Using hierarchical multiple regression, e xamine the unique contributions of gender, ethnicity, coping, and social capital , on depression symptoms among youth (14 21 years old ) . Aim 2: Examine the interaction between gender and social capital on depression symptoms within a hierarchical regression model. Aim 3: Examine the interaction between ethnicity and social capital on depression symptoms within a hierarchical regression model.

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22 CHAPTER II METHOD Participants Participants con sidered for the study met the following criteria, 1) were between the ages of 14 21 years old, 2) resided in the Denver Metropolitan area, 3) female or male, 4) and were able to read and understand English. Participants were excluded if they are unable to provide informed consent. Table 1 presents demographic information for the 200 youth surveyed. The mean age of the participants was 17.4 years ( SD = 1.7 years, range = 14 21 years). Females comprised 57.2% of the participants surveyed. With respect to ethnicity/race, participants were permitted to select more than one ethnicity, therefore all responses were considered in the analyses. Of the ethnici ties selected, 9 6 (48.%) participants identified as Hispanic/Latino, followed by 71 ( 35 . 50 %) non Hispanic White, 3 2 (1 6 %) as Asian, 2 3 ( 1 1. 50 %) Black, and 2 ( 1 .0 0 %) Other, 5 (2.50%) as Native Hawaiian or Other Pacific Islander, 12 (6%) as American Indian or Alaska Native, respectively. The majority of youth reported being in 11th grade (58.5%), followed by some college (29.0%), 12th grade (4.0%), 8th grade (3.0%), and 10th g rade (2 .5%), respectively. No significant differences in the distribution of participants by gender across different racial/ethnic groups were found, 2 (19) = 20.065 ( p = .391) . Recruitment and Data Collection Data for the current study were collected when approval from the Institutional Review Board and the Colorado Multiple Institutional Review Board was obtained. In addition, approval was obtained by the Research Review Committees of the partnering collabora tors at the National Latina Institute for Reproductive Health, Florence Crittenton

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23 Services, and YTH (Youth, Tech, Health). Participants were recruited from community based agencies and educational institutions serving a large youth sample between February and March 2014. Participants were identified through these agencies and were asked to refer others in their social networks for the study. Participants were prescreened for the eligibility criteria to include the youth demographics described under parti cipants section. Three graduate research assistants recruited and collected data in person using paper surveys and iPads. Upon completion of the survey, participants were given a $15 cash incentive for completing the survey. Participants were asked to c omplete self report measures at one time point via a computer based survey using a tablet or paper form. The measures took approximately 20 30 minutes to complete. Participants had the consent read aloud to them or watched a video describing the consent process. A waiver of parental consent was signed based on Colorado law allowing those under 18 years old to seek information and services related to sexual and reproductive health. Participants who provided assent were enrolled in the study. Each partic ipant completed demographic, Center for Epidemiologic Studies Depression scale (CES D), social capital related questions concerning community support, adult support, civic engagement, and coping questions . Measures Demographics (age, race, ethnicity, grade level, gender, language, citizenship, highest level of education attained by parents, who they reside with, how many people currently live in their home, and a description of their living conditions) were collected to use as possible covariates in analyses. Participants completed the following measures at one time point. Predictor Variables

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24 Social Capital Social Capital was initially measured using 11 items assessing perceptions of adult support, community support, civic engagement. The reliability of the measure of the terms of internal con sistency of the 11 question items. The 11 item measure of social capital However, when reducing the social capital variable down to 5 items including only addition, this change in the measure improved the regression model. Adult and Communi ty support. Civic engagement. Civic engagement included 5 items. An example question is, school or out of Coping Coping included 13 binomially scored (present or not present) items from which participants could indicate how many of the coping strategies they engage in. The question t here were 13 available responses to

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25 choose from (breaking up an upsetting problem down into smaller parts, think about one problem at a time, make new friends, keep from feeling sad, make unpleasant thoughts go The reliability of the mea sure of the the 13 items. The 13 item measure of coping ue was greater than the minimum Gender and Ethnicity To better understand the ethnic and gender differences between social capital and depression symptoms youth had to complete demographic questions. Partici pants were asked to indicate whether they were female, male, transgendered (male to female), transgendered following races that applied to them: American Indian or Alask a Native, Asian, Black or answer. Outcome variables Depression Depression symptoms were assessed using the 20 item Center for Epidemiologic Studies depression (CES D) Scale (Radloff, 1977). The scale asks to report on feelings and rangin

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26 ty is high in the general population The CES D has been shown to have good sensitivity and specificity, with high internal consistency (Lewinsohn, Seeley , Roberts & Allen, 1997). In addition, the measure has been used with different racial and ethnic groups (Roth et al., 2008). The total score of the CES D will be the datum point for analyses. This 20 item self report scale yields scores ranging from 0 to 60, with a score of 16 or above indicating a clinical level of depression. However, a cu toff score above 24 has been used to detect more accurately clinically cases among adolescents (Roberts, Lewinsohn, & Seeley, 1991; Radloff, 1991). Data Cleaning The full Ford Survey dataset included 200 youth surveys which were entered into IBM SPSS statistical software (IBM Corp., 2017). Assumptions for multiple regression were distinctly identify these items. Descriptive statistics inc luding mean, median, mode, standard deviation, and frequency distribution charts were analyzed using SPSS, and there were no identifiable outliers or data entry errors. Examination of frequency histograms was computed for observed variables to assess for normality and linearity. Continuous variables instructions. Bivariate associations among study variables were examined. D ata A nalysis Power analysis is conducted through GPo wer 3.1 software (Faul, 2007) , the sample size was determined using an alpha of .05, a desired power of . 90 , and an anticipated small to med ium effect size of (f 2 = . 15 ). Using these parameters, it was determined that a sample

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27 size of N = 92 was needed. This current study was able to satisfy this requirement because the final sample size was a total of 200 samples. A hierarchical multiple linear regression was conducted to address the research questions. Hierarchical multiple linear regression was tested the effects of social capital, coping, gender, and ethnicity on depression symptoms . First the d emographics of gender and ethnicity were entered given their ability to confound the relationship between the independent and dependent variable of depression symptoms. Second, coping was entered into the model , to provide additional control of coping on depression symptoms . T hird, the social capital variable was entered into the model to examine its relationship to depression symptoms when the demographics and coping variables are controlled. Fourth, the interaction between the two predictors was entered, specifically, main effects (i.e., social capital and gender) entered the regression equation. Finally, the interactions between social capital and ethnicities were entered into the model . The interaction examines whether a significant proportion of variance is accounted for by interaction terms after partialing the main effects of the predictors in the first couple steps of the analysis. Interaction effects usu ally tend to be difficult to detect with multiple regression, a more liberal Type I error level of .10 was set (McClelland & Judd,1993), and in order to explore potentially meaningful interactions. A significant moderator effect is determined with a signif icant change in R2 for the interaction term. The first assumption tested was linearity, or that the relationship between the independent variables and the dependent variable is linear. The assumption of linearity is best tested with scatterplot of the stan dard regression output of standardized predicted values against residuals . The scatterplot is shown in Figure 1. In the scatterplot, it can be observed

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28 that the data points are symmetrically distributed around a diagonal line in the horizontal line in the residuals versus predicted values plot. This means that the assumption of linearity was observed in the standard regression output in the prediction of the dependent variable of depression . Thus, the assumption of linearity was satisfied based on the investigation of the scatterplot. The second assumption tested is that the data needs to show homoscedasticity, which means that there should equal variance of all values of the independent vari ables around the regression line . Test of homoscedasticity was based on a visual inspection of the same scatterplot of the error terms (residuals) and the predicted values of the dependent variable of depression in Figure 1. The scatterplot of standardized predicted values against residuals should be a random pattern centered around the line of zero standard residual value to show homoscedasticity. The scatterplot of the regression in predicting depression showed random scatter. There was no observation of a megaphone structure of residuals in the scatterplots, which is a pattern of non homoscedasticity. Thus, the assumption of homoscedasticity was satisfied. The third assumption tested normality of the data or the error distribution of data . Normal probabi lity plot of the residual was used to test the assumption of normality of data. This is shown in Figure 2. Looking at the normal probability plot, the plot closely fall in the diagonal line indicating that regression model created in predicting depression showed normality of the data. In addition, histogram in Figure 3 showed that the distribution of the data of depression symptoms followed a bell shaped curved of the normal distribution pattern when social capital, gender, and ethnicity were predictors in the regression model.

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29 Skewness and kurtosis statistics of the data of depression symptoms were also computed for normality investigation. To determine whether the data follows a normal distribution, skewness statistics greater than three indicate strong n on normality and kurtosis statistics between 10 and 20 also indicate non normality (Kline, 2015). As can be seen in Table 1, the skewness (0.75) and kurtosis (0.43) statistic value of the dependent variable of depression were within the acceptable range en umerated by Kline (2015). Thus, the assumption of normality was satisfied based on the investigation of the normality graphs and skewness and kurtosis statistics.

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30 CHAPTER IV RESULTS Hierarchical multiple regression was conducted to determine whether social capital, coping, gender, and ethnicity are significant predictors of depression . Additionally , the results of the hierarchical multiple regression determined the significance of the combined in fluences of the social capital, gender, and race on depression. In addition, interaction terms were created in order to determine the impact of group differences of social capital, gender, and ethnicity on depression symptoms to address the following hypot heses . A level of significance of 0.05 was used in the hierarchical multiple regression analysis. The results of the hierarchical multiple regression are presented in Table 3. Hypothesis 1 To test hypothesis 1, hierarchical multiple regression was co nduct ed to control for gender and ethnicity and ex amine the relationship between coping and depres sion symptoms . In step 1, gender and ethnicity were entered, which accounted for 7 . 7 % of the variance (F ( 8,188) = 1 . 96 , p = .05 3 ). In step 1, although not specifically answering this research question, there was a significant finding for gender 4 .33, p = . 00). In step two, coping was entered, which accounted for 8 . 6 % of the variance (F( 9,187) = 1.96, p=.0 47 ). When controlling for 3 , p = .1 8 ). Unfortunately, the predicted hypothesis that there would be a negative relationship between coping and depression symptoms am ong youth was not significa nt. Hypothesis 2 The focus w as then on examining the relationship between social capital and depression symptoms when controlling for gender, ethnicity, and coping. It was hypothesized

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31 that social capital and depression symptoms would be inversely related such that youth (14 21 years old) in the sample who have higher social capital will have lower depression symptoms than youth who have lower social capital. In step 3 of the hierarchical multiple regression model, social capital was entered, which counted for 23% of the variance (F(10, 186) = 5.43, p<0.00). When controlling for gender, ethnicity, and coping, social capital and depression symptoms were inversely related such that youth (14 21 years old) in the sample who had higher social capital had lower depression symptoms than youth who had lower social capital ( B = .98, p = .00). Hypothesis 3 Interaction effects of social capital and gender on depression symptoms were examined in the hierarchical regression analysis to determine if there was a moderating gender effect on the relationship between social capital and depression symptoms. It was hy pothesized that female youth with lower social capital would have higher depression symptoms compared to males. In step 4, the interaction of social capital*gender was entered into the model, which accounted for 23% of the variance (F(11, 185) = 5.11, p<0.0 0). There was no significant interaction effect of social capital and gender on depression symptoms of youth (14 21 years old) (B = .45, p=.19). Hypothesis 4 Interaction ef fect s of social capital and ethnicity on depression symptoms were examined in the hie rarchical regression analysis to determine where there was a moderating ethnicity effect on the relationship between social capital and depression symptoms. It was hypothesized that t here w ould be a significant interaction effect of social capital and ethn icity on depression symptoms of youth (14 21 years old). There will be significant ethnic group

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32 differences in the relationship between social capital and depression symptoms, such that youth who identify as Hispanic, African American, and Asian (14 21 yea rs old) who experience lower social capital would have higher depressive symptoms compared to White youth (14 21 years old). In step 5, social capital*Black, social capital*Asian, social capital*Hispanic, social capital*other, social capital*American India n or Alaska Native, social capital*Native Hawaiian or other Pacific Islander, social capital*White, were entered into the hierarchical multiple regression model, which accounted for 26 % of the variance (F(18, 178)=3.44, p<0.00). There was no significant interaction effect of social capital and ethnicity on depression symptoms of youth (14 21 years old) : social capital*Black (B=.22, p=.74) , social capital*Asian (B= .47, p=.40), social capital*Hispanic (B= .74, p=.13), social capital*other (B= 2.88, p=.52) , social capital*American Indian or Alaska Native (B= .39, p=.54), social capital*Native Hawaiian or other Pacific Islander (B=3.52, p=..14), social capital*White (B=. 57 , p=.1 5 ). There are no significant ethnic ity group differences in the relationship bet ween social capital and depression symptoms. In the study, the % of variability accounted for showed much of an increase because it went up from 7 . 7 % to 26 %. Multicollinearity Investigation In terms of post estimation diagnosis for multicollinearity, collinearity statistic of tolerance and Variance Inflation Factor (VIF) for each independent variable were calculated to check for the presence of multicollinearity of the different independent variables in predicting depression. The VIF and tolerance values are summarized in Table 4. It should be noted that predictors that have tolerance values well above 0.2 and VIF values below 10 are not multicollinear in predicting a dependent variable. The VIF statistic s of several of the interaction terms in step 5 of the hierarchical regression model were not within the acceptable

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33 ranges , which indicated that these interaction term s were multicollinear in predicting depression symptoms. Therefore because of this issue, the preferred model for answering hypotheses questions should consider the step 3 model because of the following reasons : tolerance is considered a problem if it is less than .20, correlations between gender and interactions of gender were very high in th e Cronbach alphas of .90 and above, the correlations between ethnicity and interactions of ethnicities were also very high , this provided support for dropping those items from the model. Finally, the interaction terms were not statistically significant in the model which provided additional support from excluding steps 4 and 5 from the model. Although removing these multicollinear items created a slightly lower varianc e , the tolerance and variance of model step 3 was acceptable with a variance of 23%.

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34 CHAPTER V DISCUSSION AND CONCLUSIONS Youth are a vulnerable population given the developmentally challenging period encompassing physical, cognitive, and social changes they encounter. These sudden changes can potentially put them at risk for developing mental health concerns such as depressi on, anxiety, as well as health risk related behaviors (Smylie, Medaglia, & Maicka yndale, 2006). Current research supports that youth are at an increased risk for developing depression symptoms and being a young female or of the racial / ethnic group provid es additional challenges. Given the gravity of mental health risk for this population and a rise in prevalence of depression may indicate the limited efficacy of existing interventions and prevent ion efforts regardl ess of increasing research in the treatment of depression ( Mojtabai et al., 2016 ). An attempt to understand and emphasize the importance of social capital on depression among youth is a potential avenue for informing and evaluating future interventions f or this population. The current study explored the predictive relationships of social capital, gender, ethnicity, and depression symptoms using hierarchical multiple regression. The regression model was based on the literature emphasizing the effects of s ocial capital on depression symptoms among youth . Literature on gender and ethnicity differences also guided further exploration of interactions within a multiple regression model. Although, there is evidence depression, the literature was limited in establishing these findings among youth.

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35 R esearch on the role of social capital in depressive symptomatology among youth compared to the literature on adults is lacking (Crosby, Holtgrave, DiClemente, Wingood, & Gayle, 2003 ; Murayama et al., 2012 ; Viner et al., 2012). In addition, measurement vari ation among the social capital literature warranted for additional expoloration. The chronbach alpha in this study indicated that reducing the 11 items of social capital to 5 yielded a better chronbachs alpha. Specifically, the items regarding adult suppo rt were better correlated with one another and indicative of what the study was attempting to examine. It is important to consider how adult support in terms of social capital may impact depression. For example, studies have highlighted positive parent chi ld interactions as a way of improving social capital which may also influence depression symptoms or mood ( McPherson, Kerr, McGee, Morgan, Cheater, McLean, & Egan , 2014) . In addition, s tudies vary greatly from conducting exploratory and confirmatory facto r analyses to reach a social capital latent variable and others that ask questions related to factors of s ocial capital which are then summed for analyses (Porter, 2008). For example, as previously noted the Cordova, Cole Minahan, Bull, & Borrayo (2017) i mplemented exploratory and confirmatory factor analysis to create a social capital measure aimed for their population and questions of interest. Social capital is a multifaceted psychosocial factor which can at times prove challenging dur ing research give n the lack of an agreed upon measurement of social capital. Therefore , it is important for researchers to continue studying the various components of social capital and its e ffects on youth health to inform future interventions and policies. In addition, the literature emphasized that when racial/ethnic differences have been examined, most of the studies included populations consisting of non Hispanic White and African American adolescents ( Malooly, Flannery, & Ohannessian, 2017). This study builds

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36 upon the current social capital literature among youth by studying the influences of social capital, gender, and ethnicity on depression symptoms among youth. Unfortunately, these findings were no t supported in this study. There were no sigificant findings in the relationship between ethnicity and depression nor the interaction of social capital and the various ethnicities. There may be several explanations to the non significant findings, t he li terature on racial and ethnic differences in d epressive symptoms has been inconsistent (Maag & Irvin, 2005). Malooly, Flanery, and Ohann study did not find significant differences based on race/ethnicity. Another issue may be that ethnic minoritie s may underreport symptoms of depression , given the long standing history with mental health stigma. Breland Noble, Bell, and Burriss (2011) discuss the mental health barriers that African American adolescents including negative perceptions of mental illne ss, fears of being mislabeled with a conduct disorder, and mistrust of researchers. The literature on underreporting among ethnic/racial minority youth is limited as well, however, it is often listed as concerns in various studies discussions ( Nestor, Che ek, & Liu, 2016 ; Cheng, Hitter, Adams, & Williams, 2016; Klaus, Mobilio, & King, 2009 ). The results suggest that youth self reported social capital items such as those related to adult support are closely associated with one another and potentially protective against developing depression symptoms. Given that the CES D is not a diagnostic tool of depression it is important to note that this study cannot conclude that social capital can or cann ot lead to depression. Additionally, this study was able to reiterate previous findings that female youth do experience more depression symptoms compared to male youth , however this finding was not replicated in the context of social capital. Given that f ew studies have examined social capital in the context of youth mental health, this study

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37 attempted to expand beyond the limited ethnic populations studied with respect to social capital and depression symptoms. Findings from the current study may provide clinicians with increased knowledge about the importance of social capital among youth mental health . Intervention and Policy This study findings have several implications for policy and intervention. The study adds to the literature and captures the importance of social capital among youth mental health given the risk of depression symptoms and risk for developing adult depression. This study provided support for future studies researching psychosocial factors affecting youth wellbeing and mental hea lth, and may assist in designing feasible interventions, evaluation of interventions, and drafting policy to benefit this vulnerable population. T he social capital and depression literature emphasize the importance of early intervention in addressing ment al health problems to offset further depression symptoms which is known to affect several psychosocial aspects of life. Social capital has been found to benefit adolescent through the support generated through social capital (Vilhjalmsdottir, Gardarsdottir , Bernburg, & Sigfusdottir (2016). McPherson et al.'s (2013) meta analysis of 55 studies on social capital and its relationship to mental health identified how support for positive communication, nurturance, and low levels of conflict between parent adoles cents can greatly benefit esteem and self worth. Social capital in this context focused on family social capital, which may only be a small subset of social capital but may be feasible when designing interventions for adolescents experie ncing emotional distress. More specifically, when mental health providers intervene with adolescents experiencing depression symptoms, providing the parents with information on the benefits of increasing social capital in the forms of family support (posit ive communication, nurturance, low conflict, etc.) may result

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38 in additional positive outcomes for the adolescent. Family social support may come in many forms such as evening meals with family, parental surveillance, and parent child relationships as studi ed by Rothon, Goodwin, and Stansfeld (2012) and their findings supported that family social capital decreased the odds of poor mental health. Given that the health disparities gap continues to widen due in part to limited funding for mental health and the numerous barriers to treatment people encounter, informing parents, educators, and communities on the positive benefits of social capital may be feasible avenue in addressing mental health issues among one of the most vulnerable populations. Social capital research may aide drafting policy to provide funding in support of interventions targeted at adolescent mental health. For example, research studies may continue to provide additional data and support for funding of programs and interventio ns such as the Triple P Positive Parenting Program (Sanders, 2008) so that they may be available and accessible to families. The Triple P Positive Parenting Program is an evidence based program that includes aspects of cognitive behavioral and developme ntal theory, along with social learning to teach parents the skills to be able to manage family issues and behavioral problems (Thomas & Zimmer Gembeck, 2007). For example, parent strategies in the program focus on developing positive relationships. Thoma s and Zimmer Gembeck (2007) conducted a meta analysis evaluating and comparing behavioral outcomes of two disseminated parenting interventions Parent Child Interaction Therapy and Triple P Positive Parenting Program. The meta analysis on these parenting programs provides support for the efficacy of the program which received substantial U.S. government funding for their implementation. Social capital research has emphasized the importance of positive relationships between parents and adolescents. The find ings from this study encourage

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39 families, communities, and outside agencies to collaborate on building upon interventions to include elements that may increase social capital for the benefit of adolescent mental health. Continuous research on social capital may lead to programs that can target interventions for the most vulnerable adolescent populations, such as minorities and the socioeconomically disadvantaged. Study Limitations There are several limitations to the findings of this study. Findings of stu dies should population which limits interpretability to youth outside of Denver, Colorado. Additionally, another limitation in this study was w ith respect to th e measurement of social capital. An established measure of social capital is lacking in the literature. However, this allows for flexibility of including questions related to the multifaceted components of social capital for this study. The coping measure was also not supported in this study. The results showed a positive relationship between coping and depression symptoms which indicates that the coping measure may not have been a true measure of coping in relation to depression symptoms. In addition, th e survey questioned youth about the gender they identified with which was not limited to male and female as described in the methods section. However, analyses only considered responses for those who endorsed male and female responses given that there wer e very few who did not identify as male or female . Future studies should explore different gender groups as this is an area that is also lacking in social capital research. An other important consideration may be that the direction of the effects between s ocial capital and depression is not well understood. For example, the literature suggests that depression symptoms could lead to behavioral withdraw which may reduce aspects of social

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40 capital. Therefore, a cross sectional study makes it difficult to under stand whether social capital reduces depression or if depression reduces social capital ( Toumbourou, Sanson, Letcher, & Olsson, 2011). These limitations can inform future studies examining social capital and how to improve upon what is c urrently known in the literature. Future Directions A large portion of this study consisted of youth who were taking college credit courses, which may indicate a predisposition to supportive networks engaged in their success and wellbeing, although specul ation, this may warrant future considerations. Future studies should consider studying vulnerable youth and identifying aspects of social capital that can be increased to protect against symptoms of depression. For example, trajectories of depression over youth in a large US sample found that strong connections with parents and Rose, & Dierker, 2008). Additionally, because this study was examining depression symptoms and not a diagnosis of depression, future directions may expand upon the literature with youth who have a depression diagnosis. Future studies may incorporate aspects of acculturation in their analyses to explore ethnicity differences in the rela tionship between social capital and depression among youth. For example, further exploring the impact of acculturation on social capital and expanding to examin e if acculturation/acculturation stress may explain depression symptoms. Concha, Sanchez, De l a Rosa, and Villar (2013) conducted a longitudinal study to assess the effects of social capital on acculturation related stress among recently immigrated Hispanic adults before and after immigration. They concluded that acculturative stress was negativel y related to support from friends and positively from support from parents. Unfortunately,

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41 studies including youth in respect to acculturation stress and depression within the social capital framework are also limited. Although there were no significant ethnic group differences confirmed in this study it is important to continue making efforts towards expanding research studies to diverse populations. With respect to diverse samples, these should not be limited to ethnic diversity but gender as well which was not explored beyond male and females in the current study. Future studies may make efforts towards understand ing social capital and mental health in the context of acculturation and work towards a more inclusive effo rt of gender and ethnicity to better understand the implications of social capital and create appropriately tailored interventions for vulnerable youth.

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42 TABLES Table 1 Demographic Characteristics (N= 200) Frequency Percentage Min Max Mean SD Youth Age in years 14 21 17.38 1.68 Gender (F/M) 114/86 57%/43% Ethnicity White 71 35 . 50 % Hispanic 9 6 48. 00 % Black 23 11 . 50 % Asian 3 2 1 6 . 00 % Other 2 1 .0 0 % American Indian/Alaska 12 6.00% Native Hawaiian or Pacific 5 2.50% Parti ci pants Education (1) 8 th grade 6 3.0% (2) 9 th grade 1 .5% (3) 10 th grade 5 2.5% (5) 11 th grade 117 58.5% (5) 12 th grade 8 4.0% (5) Some college 58 29.0% Education coded from 1 5, presented next to ethnicity . 39 participants indicated more than one ethnicity.

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43 Table 2 Skewness and Kurtosis Statistics of Depression Score N Skewness Kurtosis Statistic Statistic Std. Error Statistic Std. Error Depression 197 0.75 0.17 0.43 0.35

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44 Table 3 Hierarchical Regression Analysis Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 14.508 2.084 6.963 .000 Gender (female) 4.325 1.440 .215 3.004 .003 Race (Black or African American) 3.163 2.627 .102 1.204 .230 Race (Asian) 2.722 2.503 .100 1.087 .278 Hispanic .550 1.916 .028 .287 .775 Race (Other) 10.469 7.049 .106 1.485 .139 Race (American Indian or Alaskan Native) .152 2.929 .004 .052 .959 Race (Native Hawaiian or other Pacific Islander) .672 4.684 .011 .143 .886 Race (White) .412 1.787 .020 .230 .818 2 (Constant) 12.970 2.372 5.469 .000 Gender (female) 4.178 1.441 .208 2.900 .004 Race (Black or African American) 3.554 2.638 .115 1.348 .179 Race (Asian) 2.624 2.499 .096 1.050 .295 Hispanic .481 1.913 .024 .251 .802 Race (Other) 10.505 7.034 .106 1.494 .137

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45 Race (American Indian or Alaskan Native) .193 2.923 .005 .066 .947 Race (Native Hawaiian or other Pacific Islander) .826 4.675 .013 .177 .860 Race (White) .283 1.786 .014 .159 .874 Coping .325 .241 .096 1.348 .179 3 (Constant) 26.295 3.173 8.287 .000 Gender (female) 4.655 1.332 .231 3.495 .001 Race (Black or African American) 3.457 2.434 .112 1.421 .157 Race (Asian) 1.604 2.312 .059 .694 .489 Hispanic .457 1.773 .023 .258 .797 Race (Other) 6.979 6.519 .070 1.071 .286 Race (American Indian or Alaskan Native) 1.030 2.697 1.001 1.011 .991 Race (Native Hawaiian or other Pacific Islander) 1.217 4.328 .019 .281 .779 Race (White) .456 1.653 .022 .276 .783 Coping .467 .224 .138 2.087 .038 Social Capital (Summed Score) .975 .168 .389 5.799 .000 4 (Constant) 22.144 4.484 4.938 .000 Gender (female) 11.128 5.126 .553 2.171 .031

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46 Race (Black or African American) 3.569 2.431 .115 1.468 .144 Race (Asian) 1.581 2.308 .058 .685 .494 Hispanic .433 1.769 .022 .245 .807 Race (Other) 7.501 6.519 .076 1.151 .251 Race (American Indian or Alaskan Native) .546 2.721 .013 .201 .841 Race (Native Hawaiian or other Pacific Islander) .741 4.336 .012 .171 .864 Race (White) .383 1.651 .018 .232 .817 Coping .482 .224 .142 2.155 .032 Social Capital (Summed Score) .687 .287 .274 2.480 .014 Social Capital*Gender .449 .344 .362 1.307 .193 5 (Constant) 30.931 7.177 4.310 .000 Gender (female) 11.641 5.357 .579 2.173 .031 Race (Black or African American) .905 9.497 .029 .095 .924 Race (Asian) 4.954 7.936 .182 .624 .533 Hispanic 10.601 7.220 .534 1.468 .144 Race (Other) 35.895 47.223 .362 .760 .448 Race (American Indian or Alaskan Native) 6.122 9.785 .147 .626 .532

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47 Race (Native Hawaiian or other Pacific Islander) 46.160 30.641 .731 1.506 .134 Race (White) 8.596 5.948 .413 1.445 .150 coping .426 .229 .125 1.857 .065 Social Capital (Summed Score) 1.277 .488 .509 2.614 .010 Social Capital *Gender .498 .357 .402 1.393 .165 Social Capital * Black .221 .669 .106 .331 .741 Social Capital * Asian .467 .550 .246 .849 .397 Social Capital *Hispanic .743 .486 .611 1.528 .128 Social Capital * Other 2.876 4.432 .308 .649 .517 Social Capital * American Indian or Alaskan Native .385 .631 .143 .611 .542 Social Capital * Native Hawaiian or other Pacific Islander 3.521 2.383 .710 1.477 .141 Social Capital *White .573 .400 .410 1.434 .153 a. Dependent Variable: Depression (Summed Score)

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48 Table 4 Tolerance and VIF Statistics of Predictors of Depression Predictors Collinearity Statistics Tolerance VIF Gender 0. 06 1 7 . 02 Race (Black) 0. 04 22.62 Race ( Asian ) 0. 05 20.31 Hispanic 0. 03 31.68 Race (Other) 0. 02 54.52 Race ( American Indian or Alaska Native ) 0. 08 13.32 Race ( Native Hawaiian or Other Pacific Islander) 0. 02 56.50 Race ( White ) 0. 05 19.58 Coping 0. 91 1.09 Social Capital 0. 11 9.11 SC*gender 0.05 19.95 SC* Black 0.04 24.80 SC*Asian 0.05 20.06 SC*Hispanic 0.03 38.40 SC*Other 0.02 54.02 SC* American Indian or Alaska Native 0.08 13.18 SC* Native Hawaiian or Other Pacific Islander 0.02 55.34 SC*White 0.05 19.62

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49 FIGURES Figure 1. Scatterplot of standardized predicted values against the standardized residuals in

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50 Figure 2. Normal probability plot of residuals in predicting depression

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51 Figure 3. Histogram of depression

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63 Stronski, S. M., Ireland, M., Michaud, P., Narring, F., & Resnick, M. D. (2000). Protective correlates of stages in adolescent substance use: A swiss national study. Journal of Adolescent Health, 26 (6), 420 427. Tabachnick, B. G., & Fidell, L. S. (2012 ). Using Multivariate Statistics. Pearson Education, Limited. Retrieved from http://books.google.com/books?id=ucj1ygAACAAJ Tabachnick, B.G., & Fidell, L.S. (2013). Using multivariate statistics (Sixth ed.). Boston:Pearson Education. Thomas, R., & Zimme r Gembeck, M. J. (2007). Behavioral outcomes of parent child interaction therapy and triple P Positive parenting program: A review and meta analysis. Journal of Abnormal Child Psychology, 35 (3), 475 495. Tolma, E.L., Oman, R.F., Vesely, S.K., Aspy, C.B., Beebe, L., & Fluhr, J. (2011). Parental youth assets and sexual activity: differences by race/ethnicity. American Journal of Health Behavior, 35 (5), 513 524. Tinsley, H. E. , & Tinsley, D. J. (1987) . Uses of factor analysis in counseling psychology research . Journal of Counseling Psychology , 34 , 414 424. Thompson, L.K., Sugg, M.M. & Runkle, J.R. (2018). Adolescents in crisis: A geographic exploration of help seeking behavior using data from crisis text line. Social Science & Medicine, 215, 69 79. U.S. Department of Health & Human Services. Adolescent de velopment explained (2018). Washington, DC. Retrieved from https://www.jhsph.edu/research/centers and institutes/center for adolescent health/_docs/TTYE Guide.pdf Van Deth, J.W. (2003). Measuring social capital: orthodoxies and continued controversies. International Journal of Social Research Methodology, 6 (1), 79 92. Vilhjalmsdottir, A., Gardarsdottir, R. B., Bernburg, J. G., & Sigfusdottir, I. D. (2016). Neighborhood income inequality, social capital and emotional distress among adolescents: A popul ation based study. Journal of Adolescence, 51 , 92 102. Viner, R. M., Ozer, E. M., Denny, S., Marmot, M., Resnick, M., Fatusi, A., & Currie, C. (2012). Adolescence and the social determinants of health. The Lancet, 379, 1641 1652. Vyncke, V., De Clercq , B., Stevens, V., Costongs, C., Barbareschi, G., Jónsson, S. H., . . . Maes, L. (2013). Does neighborhood social capital aid in levelling the social gradient in the health and well being of children and adolescents? A literature review. BMC Public Healt h, 13 (1), 65 65. Wen, M. (2008). Family structure and children's health and behavior: Data from the 1999 national survey of america's families. Journal of Family Issues, 29 (11), 1492 1519.

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65 A PPENDIX A Demographic Questionnaire 1. How old are you? a. 14 b. 15 c. 16 d. 17 e. 18 f. 19 g. 20 h. 21 i. Other j. Don't want to answer If Other, please specify:______ 2. What grade are you i n? (If you are currently on vacation between grades, pl ease i ndi cate the grade you will be in when you go back to school) a. 6 th b. 7 th c. 8 th d. 9 th e. 10 th f. 11 th g. 12 th h. Not currently in school i. Other j. If Other, please specify:______ 3. Gender? a. Female b. Male c. Transgendered, Male to Female d. Transgendered, Female to Male e. 4. Are you Hispanic or Latino? a. Yes b. No c. 5. What is your race? (Check all that apply) a. American Indian or Alaska Native b. Asian

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66 c. Black or African American d. Native Hawaiian or Other Pacific Islander e. White f. Other g. If Other, please specify:_ _____ 6. When you are at home or with your family , what l a nguage or l anguages do you usually speak? (Choose all that apply) a. English b. Spanish c. Chinese language (Mandarin or Cantonese) d. Other e. If Other, please specify: ______ 7. Were you born in the US? a. Yes b. No c. 8. How many years have you lived in the US? (Choose one) a. 10 years or more b. Between 5 and 9 years c. Less than 5 years d. 9. Were your parents born in the US? (Choose one) a. No b. One but not both c. Both d. 10. What is the highest grade that your mother completed? (choose one) a. Less than high school b. High school graduate c. Some college d. College graduate or higher e. f. 11. What is the highest grade that your father completed? (choose one) a. Less than high school b. High school graduate c. Some college d. College graduate or higher e. f.

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67 12. During most of the time you have grown up, who have you l ived with? (check the best answer) a. Mother and Father b. Mother and Stepfather c. Father and Stepmother d. Father only e. Mother only f. Guardian g. Other If you have lived most of the time with someone besides those listed, please tell us who ______ 13. How many people currently live with you in your house (please do not count yourself -choose one answer) a. 1 b. 2 c. 3 d. 4 e. >5 14. Of the list here, which best describes your living conditions? (choose one) a. I or a family member own the residence where I live b. I or a family member rents the residence where I live c. I am living with friends who are not family d. I live in transitional housing such as a hotel e. I live in a group home such as a halfway house f. I stay in shelters g. I do not have a home

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68 APPENDIX B Center for Epidemiologic Studies Depression scale (CES D) For the following questions i nd i cate how often were each of the following true during the last week. (Choose 'never or rarely', 'sometimes', 'a l ot of the time', 'most of the time or all the time'.) 1. You were bothered by things that usually don't bother you. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time 2. You didn't feel l ike eating, your appetite was poor. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time 3. You felt that you couldn't shake off the blues, even with help from your family and friends. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time 4. You felt that you were just as good as other people. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time 5. You had trouble keeping your mind on what you were doing. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time 6. You felt depressed. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time

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69 7. You felt that you were too tired to do things. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time 8. You felt hopeful about the future. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time 9. You thought your life had been a failure. a. Never or rarely b. sometimes c. a lo t of the time d. most of the time or all the time 10. You felt fearful. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time 11. You were happy. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time 12. You talked less than usual. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time 13. You felt lonely. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time 14. People were unfriendly to you. a. Never or rarely b. sometimes c. a lot of the time

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70 d. most of the time or all the time 15. You enjoyed life. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time 16. You felt sad. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time 17. You felt that people disliked you. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time (Choose 'never or rarely', 'sometimes', 'a l ot of the time', 'most of the time or all the time'.) 1. You were bothered by things that usually don't bother you. a. Never or rarely b. sometimes c. a lot of the time d. most of the time or all the time

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71 A PPENDIX C Social Capital Questions 1. Adult Info How often do the adults in your life give you information on how to do things? a. Never b. Rarely c. Sometimes d. Most of the time e. All of the time f. 2. Adult Advice How often do you ask the adults in your life for advice? a. Never b. Rarely c. Sometimes d. Most of the time e. All of the time f. 3. Adult Wellbeing How often do the adults in your life express interest and concern in your well being? a. Never b. Rarely c. Sometimes d. Most of the time e. All of the time f. 4. Adult Interest How often do the adults in your life talk with you about things you are interested in? a. Never b. Rarely c. Sometimes d. Most of the time e. All of the time f. 5. Adult Support How often do the adults in your life provide you with love and support? a. Never

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72 b. Rarely c. Sometimes d. Most of the time e. All of the time f. 6. Adult Expectations How often do the a dults in your life make it clear to you what they expect of you? a. Never b. Rarely c. Sometimes d. Most of the time e. All of the time f. 7. Volunteer How often do you volunteer in your community? a. Never b. Rarely c. Sometimes d. Most of the time e. All of the time f. 8. Clubs How often do you take part in clubs or groups in school or out of school? (this could be like a crew, or group of friends that meets in an organized way). a. Never b. Rarely c. Sometimes d. Most of the time e. All of the time f. 9. Sports How often do you participate in sports in school or out of school? a. Never b. Rarely c. Sometimes d. Most of the time e. All of the time f.

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73 10. Arts How often do you participate in music, dance, theatre, or other arts in school or out of school? a. Never b. Rarely c. Sometimes d. Most of the time e. All of the time f. 11. Religion How often do you attend religious services like church service, a bible study, or a spiritu al gathering? a. Never b. Rarely c. Sometimes d. Most of the time e. All of the time f. 12. Support Neighborhood Do you feel like your neighborhood and community are supportive places? a. Not at all b. Not very c. Somewhat d. Pretty supportive e. Very supportive f.

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74 APPENDIX D Coping Questions Think about the times when you have felt stress or been in stressful situations in the past three months, did you do any of the following (Check all that apply) 1. Coping 1 Break an upsetting problem down into smaller parts 2. Coping 2 Sort out what can and cannot be changed 3. Coping 3 Make a plan of action and follow it to address a problem 4. Coping 4 Leave options open when things get stressful 5. Copin g 5 Think about one problem at a time 6. Coping 6 Find solutions to your most difficult problems 7. Coping 7 Make unpleasant thoughts go away 8. Coping 8 Take your mind off unpleasant thoughts 9. Coping 9 Stop yourself from being upset by unpleasant though ts 10. Coping 10 Keep from feeling sad 11. Coping 11 Get friends to help you with the things you need 12. Coping 12 Get emotional support from friends and family 13. Coping 13 Make new friends