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A longitudinal study of sexual minority stress and behavioral health in sexual minority adolescents

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A longitudinal study of sexual minority stress and behavioral health in sexual minority adolescents

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Content i

A LONGITUDINAL STUDY OF SEXUAL MINORITY STRESS AND BEHAVIORAL
HEALTH IN SEXUAL MINORITY ADOLESCENTS

BY

Claire Burgess

A Dissertation Presented to the

FACULTY OF THE GRADUATE SCHOOL

UNIVERSITY OF SOUTHERN CALIFORNIA

In Partial Fulfillment of the

Requirements for the Degree

DOCTOR OF PHILOSOPHY

(PSYCHOLOGY)

December 2017
ii

Acknowledgements
I owe a big thank you to many people who have supported me during graduate school.
My colleagues Joshua Rusow, Jeremy Gibbs, Ankur Srivastava, and Cary Klemmer: thank you
for working with me over the past three years on this project. This dissertation could not be
possible without your dedication in recruitment and retention. A tremendous thank you goes out
to you!
I also wanted to thank Dr. Jeremy Goldbach, my mentor, for giving light to this research
idea. I have much hope that this dissertation is the basis for something much bigger that extends
beyond our reach to positively impact adolescent health. I am especially grateful for the
opportunities Jeremy has given me to grow in new directions through presenting at public health
conferences, publishing in high-impact journals, and connecting with seminal scholars. Thank
you for believing in my work and showing me what a consistently patient, passionate, and
diplomatic mentor looks like.
Many thanks to my committee and to Dr. John Monterosso for his mentorship. This
project would not be possible without the voice of Dr. David Schwarz who suggested I apply for
the SPSSI “Grants-in-Aid” award, which funded this project. Thank you Drs. Frank Manis, John
McArdle, Dan Nation, and our collaborators at Children’s Hospital, Drs. Mary Rose Mamey and
Sheree Schrager for their feedback.
This dissertation is dedicated to my best friend and inspiration, Dr. Randy Bautista.
Randy is someone who I aspire to be more like each day. He navigates life with poise and
calmness. I am happy for the joy Randy has brought to my life and my dissertation over the
years. I will never forget the memories we made during graduate school.
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This dissertation is additionally dedicated to my mom and dad. I appreciate them for
continuing to support me and always being present for my needs, even from afar, over the past
seven years. Coming back to the Eastern time zone has never felt so good!
I am grateful to Liz, who has encouraged me to further my professional journey and
continue to apply myself to be the best I can be: thank you for your continued support and
awareness of my professional needs. I appreciate your support throughout my academic journey
and your keen, balanced perspective on life.
My internship colleagues and supervisors, particularly Demet, I owe a lot of gratitude to
for their patience, love, humility, and humor. I have had some of the best moments in therapy
working with them. My identity as a therapist has grown considerably over the past year and it
fills my heart with sadness to consider leaving a workplace that really feels like home.
My love lastly goes out to my cohort. We have been so close over the past seven years
and really supported each other at every step of our milestones in and out of school. I will never
forget our cohort’s experiences, from eating a buffet together our first year in assessment, to our
first outings together in Los Angeles, to our graduation celebration. Each cohort member has a
wonderful career ahead and together we represent the wisest, most admirable soon-to-be
psychologists on the planet. I have enjoyed growing old in graduate school – let’s grow older
together still!

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Table of Contents
Acknowledgements ......................................................................................................................... ii
List of Tables ...................................................................................................................................v
List of Figures ............................................................................................................................... vii
Dissertation Abstract ................................................................................................................... viii
Chapter One: Background and Significance ....................................................................................1
Chapter Two: Method ...................................................................................................................16
Chapter Three: Analytic Plan ........................................................................................................22
Chapter Four: Results ....................................................................................................................26
Chapter Five: Discussion and Conclusion .....................................................................................37
References ......................................................................................................................................53
Tables…………………………………………………………………………………………….63

Figures……………………………………………………………………………………………87
Appendices .....................................................................................................................................88
Appendix A: Sexual Minority Adolescent Stress Inventory ..............................................88
Appendix B: State Trait Anxiety Inventory (Short Form) .................................................90
Appendix C: Center for Epidemiologic Studies Depression Scale (Short Form) ..............91
Appendix D: Substance Use Measure................................................................................92
Appendix E: Adolescent Stress Questionnaire ..................................................................93
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List of Tables
Table 1: Means for Key Study Variables at Wave One………………………………………….63

Table 2: Sample Size, Means, and Ranges of Total SMASI Score………………………..…….64

Table 3: Intercorrelations of SMASI Over Time ………………………………………………..65

Table 4: Cronbach’s Alpha Reliability Coefficients of Key Study Variables…………….…….66

Table 5: Intercorrelations of STAI Over Time…………………………………………………..67

Table 6: Intercorrelations of CES-D Over Time…………………………………………….…..68

Table 7: Intercorrelations of Substance Use Over Time ………………………..………….……69

Table 8: Sample Size, Means, and Ranges of Outcomes ………………………………………..70

Table 9: Intercorrelations of SMASI and CES-D Over Time……………………………..……..71

Table 10: Intercorrelations of SMASI and STAI Over Time……………………………………72

Table 11: Intercorrelations of SMASI and Substance Use………………………………………73

Table 12: Intercorrelations of SMASI and ASQ Over Time…………………………………….74

Table 13: Intercorrelations of ASQ and CESD Over Time……………………………………...75

Table 14: Intercorrelations of ASQ and STAI Over Time………………………………………76

Table 15: Intercorrelations of ASQ and Substance Use Over Time……………………………..77

Table 16: Intercorrelations of ASQ and SMASI Over Time…………………………………….78

Table 17: SMASI Univariate LGCMs Model Fit Statistics……………………………………...79

Table 18: Depression Univariate LGCMs Model Fit Statistics…………………………………80

Table 19: Anxiety Univariate LGCMs Model Fit Statistics……………………………………..81

Table 20: Substance Use Univariate LGCMs Model Fit Statistics………………………………82
Table 21: Bivariate Latent Growth Curve for SMASI & CES-D……………………………………83
Table 22: CES-D & SMASI, ASQ Cross Lagged Power Analyses……………………………..84
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Table 23: STAI & SMASI, ASQ Cross Lagged Power Analyses……………………………….85
Table 24: SUDS & SMASI, ASQ Cross Lagged Power Analyses………………………………86

vii

List of Figures
Figure 1: SMASI Total Score Means by Time Point…………………………………………….87

viii

Dissertation Abstract
Sexual minority adolescents (SMA) are at a disproportionate risk for developing
behavioral health problems and typically experience an earlier age of onset than heterosexual
adolescents. Contributing to the Minority Stress Theory hypothesis, studies have linked minority
stressors to poor behavioral health outcomes, primarily through cross-sectional approaches using
limited operationalizations of minority stress (e.g., victimization stress). These three papers
explore the reliability, univariate latent growth curves, and bivariate latent growth curves of
sexual minority adolescent scores on 1) a novel measure of sexual minority stress and 2) anxiety,
depression, and substance use measures in a sample of 304 SMA referred either in person,
online, or by peers. Researchers found that the novel measure of sexual minority stress
demonstrated internal consistency across eight months of data collection. Additionally, the latent
growth trajectory showed that summary scores on the SMASI measure tended to increase, then
steadily decrease over time. Cross-sectionally, minority stress was linked with behavioral health
outcomes; however, it did not account for variance explained over and above general life stress.
Bivariate latent growth curve modeling did not indicate that minority stress preceded changes in
behavioral health outcomes. We provide a power simulation to guide future longitudinal studies
and make substantive recommendations for the continued refinement of investigating minority
stress in SMA based on these exploratory findings.

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Chapter One: Background and Significance
Sexual minority adolescents (SMA) are at disproportionate risk for behavioral health
problems (McLaughlin, Hatzenbuehler, Xuan, & Conron, 2012). SMA are more likely to exhibit
anxiety, depression, or substance use disorder at an earlier age than their heterosexual peers
(Fergusson, Horwood, Beautrais, 1999; Gilman, Cochran, Mays, Hughes, Ostrow, & Kessler,
2001). These adolescents are three-to-five times more likely to attempt suicide than their
heterosexual peers (Garofalo, Wolf, Kessel, Palfrey, & DuRant, 1998; Hatzenbuehler, 2011;
Marshal et al., 2011) and engage in risky substance use including tobacco, cocaine, alcohol and
marijuana at higher rates than their heterosexual counterparts (CDC, 2011; Garofalo et al., 1998;
Marshal, Friedman, Stall, & Thompson, 2009a).
Generally, these disparities are explained through the Minority Stress Theory (MST;
Meyer, 2003). Research on minority stress finds that unique, minority-related stressors impact
the behavioral health of sexual minority individuals (Marshal et al., 2009b; Rotheram-Borus,
Hunter, & Rosario, 1994a). Minority stressors occur from (a) external, objectively stressful
events (chronic or acute), (b) the expectation of such events and the vigilance around that
expectation, and (c) the internalization of pejorative societal attitudes. Minority stressors such as
internalized homophobia (Frost, Lehavot, & Meyer, 2015; Szymanski & Gupta, 2009), family
conflict or rejection (Duros & Gates, 2012; Savin-Williams & Dubé, 1998), peer harassment
(Bontempo & D’Augelli, 2002; Halderman, 2000; Herek, Gillis, Cogan, & Glunt, 1997; Herek,
2009; Ueno, 2005), and physical violence (Carvalho, Lewis, Derlega, Winstead, & Viggiano,
2011; Herek et al., 1997; Herek, 2009; Williams, Connolly, Pepler, & Craig, 2005) have received
wide attention in the literature. Meta-analyses (King, Semlyen, Tai, Killaspy, Osborn, Popelyuk,
& Nazareth, 2008; Friedman, Marshal, Guadamuz, Wei, Wong, Saewyc, & Stall, 2011; Marshal
2

et al., 2011b; Meyer, 2003) and prospective studies (Birkett et al., 2015; Burton, Marshal,
Chisolm, Sucato, & Friedman, 2013; Rosario, Schrimshaw, Hunter, & Gwadz, 2002) have
supported MST. However, MST as a theory has only been examined by looking at discrete
minority stressors, with much attention to gay-related victimization stress. To our knowledge, no
researcher has developed a measure that assesses multiple adolescent sexual minority stressors
concurrently (Goldbach, Tanner-Smith, Bagwell, & Dunlap, 2014; Morrison, Bishop, Morrison,
& Parker-Taneo, 2016; Rotheram-Borus, Rosario, Meyer-Bahlburg, Koopman, Dopkins, &
Davies, 1994b; Watson & Russell, 2014).
Although the studies described above cross-sectionally link sexual minority stress to
behavioral health, longitudinal research on SMA is limited (Elze, 2005; Meyer, 2003). Few
studies have sampled SMA longitudinally and those that have, focused predominantly on the
effect of sexual minority stress on internalizing symptoms (e.g., Burton, Marshal, Chisolm,
Sucato, & Friedman, 2013; Rosario, Schrimshaw, Hunter, & Gwadz 2002; Rosario, Schrimshaw,
& Hunter, 2005). The literature has only recently started to document outcomes such as
substance use longitudinally (Goldbach, Tanner-Smith, Bagwell, & Dunlap, 2014; Marshal,
Friedman, Stall, & Thompson, 2009). Moreover, the current works do not examine life stressors
as distinct entities from sexual minority stressors, which are theorized to have a unique impact on
the behavioral health of SMA (Meyer, 2003).
Researchers investigating the behavioral health patterns of SMA over time have
documented changes in anxiety, depression, and substance use (Birkett, Newcomb, & Mutanski,
2015; Burton, Marshal, Chisolm, Sucato, & Friedman, 2013; Dermody, Marshal, Burton, &
Chisolm, 2016; Marshal et al., 2013; Rosario, Schrimshaw, Hunter, & Gwadz, 2002). For
instance, Rosario et al. (2002) found that in a sample of SMY from the 1990’s, youth
3

demonstrated low-to-moderate effect size changes in depression and anxiety within one year’s
time. Determining the stability of SMA behavioral health will indicate whether, in future works,
researchers can quantify these changes along with other factors, such as minority and life stress
in an adolescent’s environment, to see if minority stress theory is supported over shorter periods
of time in SMA. Previous studies, such as those by Marshal et al. (2013) relied on older datasets
that used infrequent data collection schedules with one year or greater windows. Additionally,
sampling windows within studies with shorter collection times had assessments between two and
three times per year at most (see Birkett et al., 2015; Dermody et al., 2016; Rosario et al., 2002).
Timmons & Preacher (2015) found that when studying developmental phenomena over time,
more frequent, precise data collection over possible periods of change lead to the most accurate
examination of longitudinal processes. Building on these recommendations, the present study
examined anxiety, depression and substance use to update the literature and see if these
outcomes can be measured within an eight-month time frame and determine how stable they are.
Since behavioral health disparities are commonly found among SMA, we provide a literature
review below.
Depression in Sexual Minority Adolescents. In the general population, twenty percent
of adolescents have experienced clinical levels of depression by age 18 (Lewinsohn, Hops,
Roberts, Seeley, & Andrews, 1993; Lewinsohn, Rohde, & Seeley, 1998). Depression
disproportionately affects SMA compared to heterosexual adolescents (for a review of reported
rates of depression disparities and measurement concerns in the literature, see Russell, 2003).
Symptoms of depression include irritability, changes in sleep and appetite, withdrawal from
social activity, and thoughts of hopelessness or sadness. Depression in children and adolescents
typically lasts 7-9 months and has a 90% chance of remitting (Birmaher et al., 1996); however, it
4

has a 70% of recurring within 5 years and a persistence rate of 60-70% into adulthood. Despite
these findings, on average adolescents demonstrate low levels of depression during adolescence
(Brendgen, Wanner, Morin, & Vitaro, 2005) and longitudinal analyses find that they tend to
decrease during adolescence (Ge, Conger, & Elder, 2001).
The mortality associated with depression is particularly concerning, and it has been a
focus of much research on sexual minority adults (Cochran, Sullivan, & Mays, 2003; Mills et al.,
2004). Depression can interfere with academic success and disrupt social-emotional development
in adolescents. The impact of depression during adolescence is problematic because adolescents
who have experienced depression show wide ranging poorer physical health outcomes and
interpersonal functioning than those who have not experienced depression (Birmaher et al., 1996;
Jaycox et al., 2009).
Given risks associated with depression, researchers have examined how minority stress
may be involved as a mediating variable. Specifically, in several studies minority stress
researchers have examined depression and how it relates to victimization (e.g., Bontempo &
D’Augelli, 2002; Birkett, Newcomb, & Mustanski, 2015; Mustanski, Andrews, & Puckett, 2016;
Russell, Ryan, Toomey, Diaz, & Sanchez, 2011). Loss of role or diminished self-esteem may be
concerning to SMA, as they enter adolescence and are confronted with family, peer, and societal
norms, while dealing with developmental changes associated with their sexual identity. Possible
rejection from typical areas of social support at school or within the family may lead to a
negative social impact, which may contribute to depression in adolescents (Padilla, Crisp, &
Rew, 2010; Gibbs & Goldbach, 2015; Goldbach & Gibbs, 2014; Russell, Toomey, Ryan, &
Diaz, 2014).
5

Anxiety in Sexual Minority Adolescents. Anxiety is characterized by excessive worry,
somatic complaints, and an avoidance of feared situations (Rapee, 2015). In children and
adolescents, the prevalence of anxiety is between 15-20% (Beesdo, Knappe, & Pine, 2011) and
tends to be persistent, increasing in symptoms, with higher rates among females than males;
however, prospective studies of adolescents are needed for refinement. McLaughlin and King
(2015) modeled anxiety trajectories across early adolescence and found that anxiety tended to
decline over a one-year period in their sample. Hale, Raaijmakers, Muris, van Hoof, and Meeus
(2008) examined anxiety symptoms in adolescents aged 12-16 over 5 years using latent growth
modeling. Participants showed a slight decrease in symptoms relating to various forms of anxiety
including panic, school anxiety, and separation anxiety.
Much research indicates the lifetime prevalence of an anxiety disorder is more
pronounced in sexual minority individuals (King et al., 2008; Mays & Cochran, 2001). Again,
victimization is a stressor found in several studies. For instance, early experiences of
victimization predicted more anxiety symptomatology later in life in one study (Katz-Wise,
Rosario, Calzo, Scherer, Sarda, & Austin, 2016). Cross-sectionally, Rosario et al. (2002) found
in gay and bisexual male youths that negative attitudes towards homosexuality was associated
with greater risk for developing anxiety.
Substance Use in Sexual Minority Adolescents. Adolescence is a time when many
individuals initiate substance use (YRBS; Kann et al., 1997). SMA are at increased risk for use
of cigarettes, alcohol, marijuana, cocaine, prescription medications, and ecstasy (Bontempo &
D’Augelli, 2002; Corliss et al., 2013; Marshal et al., 2008; Mereish, Goldbach, & Burgess,
2016). Although much of the literature is still nascent (Corliss et al., 2010; Heck, Livingston,
Flentje, Oost, Stewart, & Cochran, 2014), SMA are more likely to use multiple substances
6

concomitantly compared to their heterosexual peers, which can lead to poor health consequences
(Garofalo et al., 1998; Marshal et al., 2008). Substances such as “e-cigarettes,” synthetic
cannabinoids, and prescription drugs have drawn recent awareness due to their detrimental
impact on adolescent health (Cobb, Byron, Abrams & Shields, 2010; Warner, Chen, Makuc,
Anderson & Miniño, 2011). Substance use confers many poor outcomes, such as risky health
behaviors, engaging in violence, weapon carrying, having unsafe sporting practices, and having a
suicide plan (DuRant, Smith, Kreiter, & Krowchuk, 1999).
Researchers have sought to elucidate the relationship between substance use and minority
stress. Goldbach et al. (2014) documented the effects of psychological stress, including
internalized homophobia, from a minority stress perspective. There are links between substance
use and experiences of anxiety symptoms. Regarding minority stress experiences, some work
finds sexual orientation-based victimization was associated with increased alcohol use over time
only for sexual minority females, but not for young gay males (Newcomb, Heinz, & Mustanski,
2012). Associations between sexual orientation and substance use are stronger when adolescents
have been victimized (Bontempo & D’Augelli, 2002). Given these aspects of minority stress
related to substance use among SMA, we seek to examine the longitudinal stability to better
understand the clinical implications for minority stress models.
Longitudinal Studies of Sexual Minority Stress. To date, cross-sectional research
(Almeida, Johnson, Corliss, Molnar, & Azrael, 2009; Bontempo & D’Augelli, 2002; Herek &
Garnets, 2007; King, Semlyen, Tai, Killaspy, Osborn, Popelyuk, & Nazareth, 2008; Williams et
al., 2005) and meta-analyses (Friedman, Marshal, Guadamuz, Wei, Wong, Saewyc, & Stall,
2011; Goldbach et al., 2014; Marshal et al., 2011) have supported the link between minority
stress and poor behavioral health outcomes in SMA.
7

Only three studies, to our knowledge, prospectively examine minority stress theory and
behavioral health outcomes in SMA. Using data from adolescent medical clinics, Burton et al.
(2013) conducted a two-time point study of minority stress in 197 adolescents (29% sexual
minority; Mage = 17, age range: 14-19 years; 63% African American). They found that sexual
minority victimization mediated the effect of sexual minority status on symptoms of depression
and suicidality. In a study similar to the proposed research, Rosario et al. (2002) recruited 140
SMA (Mage = 18, age range: 14-21 years; 37% Latino) from SMA-affirming university and
community agencies in New York City. Researchers tested whether minority stress (defined as
gay-related life events, negative attitudes towards homosexuality, and discomfort with
homosexuality) related to emotional distress (defined as symptoms of anxiety, depression, or
conduct disorder) over three time points. Wichstrøm & Hegna (2003) examined youth over three
waves. These researchers studied predictors of depression and suicide attempts, assessing gender
non-conformity stress to predict clinical outcomes. Taken together, the three studies reviewed
above do not fully assess sexual minority stress as a multi-domain construct. Furthermore,
although researchers found some support for minority stress affecting mental health outcomes,
Rosario et al. (2002), like Burton et al. (2013), explicitly stated the need for further prospective
research due to the significant limitations in their study.
Prior longitudinal studies with SMA have shortcomings in the measurement of minority
stress. Despite the well-documented contribution of stress to psychopathology (Grant, Compas,
Stuhlmacher, Thurm, McMahon, & Halpert, 2003; Plunkett, Radmacher, & Moll-Phanara, 2000;
Williams & Mohammed, 2009), current studies that measure sexual minority stress present
limited operationalizations of gay-related stress (e.g., Rosario et al., 2002; Rotheram-Borus et al.,
1994). Minority stress as a whole or certain minority stress domains are absent in prior works
8

(e.g., Hatzenbuehler, McLaughlin, & Nolen-Hoeksema 2008; Hahm, Wong, Huang, Ozonoff, &
Lee, 2008; Demody et al., 2014) (Balsam, Molina, Beadnell, Simoni, & Walters, 2011; Goldbach
& Gibbs, 2014; Rosario et al., 2002). For instance in their study, Rosario et al. (2002) failed to
measure witnessing sexual minority-related victimization and other key components of minority
stress (see Rosario, Rabinowitz, & Schrimshaw, 2002; Russell, 2003). Witnessing sexual
minority victimization has been linked to deleterious outcomes including externalizing
symptoms (Goldbach et al., 2014; Williams et al., 2005) and internalizing symptoms (Shields et
al., 2012). Previous works of SMA have, additionally, not differentiated between the effects of
sexual minority stress and life stress on behavioral health outcomes such as substance use
(Goldbach et al., 2014; Rotheram-Borus Rosario, Meyer-Bahlburg, Koopman, Dopkins, &
Davie, 1994; Watson & Russell, 2014). One study looking at a sample of nearly 400 adults
examined whether minority stress impacted physical health over and above life stress across a
one year period using logistic regression and found support for their hypothesis (Frost, Lehavot,
& Meyer, 2015). However, less is known about the subject developmentally. Through measuring
life and minority stress within the same sample, researchers would begin to understand the ways
in which minority stress might uniquely impact the health of SMA (Frost, Lehavot, Meyer, 2015;
Meyer, 2003; Rotheram-Borus et al., 1994).
Research design and analyses provide another limitation of past research. Burton et al.
(2013) report testing a mediation hypothesis in which victimization mediates the relationship
between sexual minority status on symptoms of depression. In measuring the mediator and
outcome concurrently, researchers were unable to conduct a sound test of mediation because the
process variable, the mediator, was not sampled separately from the outcome (Cole & Maxwell,
2003). The benefit of using longitudinal statistics such as cross-lagged autoregression or
9

bivariate latent growth curve is that researchers can examine questions relating to coupling or
directionality in looking at minority stress and behavior health. These methods allow researchers
to determine which of two outcomes being examined precedes the other.
Prior Measures of Sexual Minority Stress
Prior longitudinal studies with SMA have shortcomings in how they measured minority
stress (Morrison et al., 2016). Despite the well-documented contribution of stress to
psychopathology (Grant, Compas, Stuhlmacher, Thurm, McMahon, & Halpert, 2003; Plunkett,
Radmacher, & Moll-Phanara, 2000; Williams & Mohammed, 2009), studies that measure sexual
minority stress present limited operationalizations of gay-related stress (e.g., Rosario et al., 2002;
Rotheram-Borus et al., 1994a). Moreover, various gay-related stress domains are absent in
previous works (Balsam, Molina, Beadnell, Simoni, & Waters, 2011; Goldbach & Gibbs, 2014;
Rosario et al., 2002). For instance, Rosario et al. (2002) failed to measure key sexual minority
stress variables, such as witnessing sexual minority-related victimization (see Russell, Ryan,
Toomey, Diaz, & Sanchez, 2011). This stressor, witnessing sexual minority victimization, has
been linked to deleterious outcomes including externalizing symptoms (Goldbach et al., 2014;
Williams et al., 2005) and internalizing symptoms (Mustanski, Andrews, & Puckett, 2016).
Previous works of SMA have, additionally, not differentiated between the effects of sexual
minority stress and life stress on substance use outcomes (Goldbach et al., 2014; Rotheram-
Borus et al., 1994b; Watson & Russell, 2014). Measurements of life and minority stress within
the same sample would help researchers understand the ways in which minority stress might
uniquely impact the health of SMA (Rotheram-Borus et al., 1994b).
Another limitation of past research concerns the research design and analyses employed.
Burton et al. (2013) reported testing a mediation hypothesis in which gay-related victimization
10

mediates the relationship between sexual minority status on symptoms of depression in a sample
of adolescents, aged 14-19. By measuring the mediator and outcome concurrently, the
researchers were unable to conduct a sound test of mediation because the process variable, the
mediator, was not sampled separately from the outcome (Cole & Maxwell, 2003). Like Burton et
al., who conducted longitudinal analyses over two time points on one measure of sexual minority
stress, Wichstrøm & Hegna (2003) examined adolescents from grades 7-12 over three waves, but
only assessed one aspect of sexual minority stress: gender non-conformity. A longitudinal
assessment of more than two-to-three time points would allow for a more precise understanding
of change over time (Ferrer & McArdle, 2003; Timmons & Preacher, 2015) and would delineate
potential trajectories underlying growth or decline in sexual minority stress. Additionally, past
works do not fully assess sexual minority stress as a multi-domain construct.
Similarly, Hatzenbuehler McLaughlin, and Nolen-Hoeksema (2008) examined
adolescents in two time points, seven months apart, but did not measure minority stress. Hahm,
Wong, Huang, Ozonoff, & Lee (2008), Demody et al., (2014), and others used the National
Longitudinal Study of Adolescent Health for their analyses, which did not collect data on sexual
minority stressors. Assessments that understand intersectional identities of gender, sexual
orientation, ethnicity, culture, and religious components have not been thoroughly addressed in
the literature (Russell & Fish, 2016).
However, the issue is with these studies and reliance on older datasets, is that they did not
include perceived reasons for victimization in their assessment or other measures of stress
relating to perceived sexual minority status. As a result, there is a dearth of research
incorporating measures of sexual minority stress in adolescence as a construct. Morrison et al.’s
(2016) recent review of the psychometric properties of instruments evaluating experienced
11

discrimination in 162 research articles found subpar reliability, validity, and factor structure of
the measures used. In their results they highlight several limitations, including poor content
validity, no reliability measurement in over 30% of articles that calculated scaled scores, no use
of subscales or dimensionality, and lacking criterion and construct validity. One measure
achieved a perfect score in these domains but is used to measure adult heterosexist experiences
(Balsam et al., 2013). Therefore, we highlight, as other meta-analyses have (see Goldbach et al.,
2014; Marshal et al., 2009b), the dearth of quality measures of sexual minority stress experiences
impacting adolescents at present. Adolescence is a key period in which to assess these stressors,
in that social, physical, emotional changes are all occurring, along with the development of
sexual orientation and identity (Marshal et al., 2008). Difficulties may encumber adolescents
who encounter situations of stigma and prejudice regarding their nascent sexual minority
identity.
The Sexual Minority Adolescent Stress Inventory (SMASI)
Because previous attempts to measure sexual minority stress in adolescence have fallen
short at capturing minority stress comprehensively, Goldbach, Schrager & Mamey (2017)
developed the Sexual Minority Adolescent Stress Inventory (SMASI). Their study identified 54
items covering 11 domains of minority stress, including family stress, racial/ethnic-intersection
stress, and negative disclosure stress. SMASI scores were significantly associated with all
outcomes (depression, suicidality, self-harm, externalizing and internalizing behaviors, substance
use) but only moderately associated with the Adolescent Stress Questionnaire (Byrne,
Davenport, & Mazanov, 2007; r = -.13 to .51). Analyses revealed significant associations of a
latent minority stress variable with both proximal and distal health outcomes beyond the
variation explained by general stress. To determine the utility of the SMASI as a longitudinal
12

measure, the present work examines latent growth trajectories, internal consistency, and factor
invariance of a newly validated SMASI.
Psychometric Measurement over Time. Longitudinal analyses may be limited by the
measures used, how the scales hold up, and the potential for outliers and heterogeneity, as a
study is only as good as the confidence in the study measures used at achieving a valid and
reliable measurement of the underlying construct (Kazdin, 2003). Reliability, in research, is a
priority. Reliability refers to the consistency of the measure. Reliability is important when
seeking to test the stability of an entity over time. The present study features a measure that has
strong internal consistency and has demonstrated test-retest reliability (Schrager, Goldbach, &
Mamey, 2017) but requires further psychometric examination.
In prior research, the psychometric features of the sexual minority stress measures were
questionable. Morrison et al. (2016) conducted a review of discrimination measures in the
literature. They found that the standard procedure for many authors seeking to examine some
form of sexual minority discrimination stress was to produce a measure for use in one study.
Morrison et al. found that only two scales of discrimination towards sexual minority individuals
were used in more than four published studies: Waldo’s (1999) Workplace Heterosexist
Experiences Questionnaire and Szymanski’s (2006) Heterosexist, Harassment, Rejection, and
Discrimination Scale. This practice leads to concern over the validity, reliability, and factor
structure of measures being used. Research on the psychometric properties of measures used on
phenomena over time is especially important, as you want to ensure you are consistently
measuring the same phenomena in an accurate way that reduces reporting error and error from
other sources.

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Significance of the Present Research
Research is needed to clarify the mechanisms, such as minority stress, explaining the
disproportionate occurrence of adverse health outcomes in SMA (Healthy People 2020 initiative;
US Department of Health and Human Services, 2012; IOM, 2011). The application of
longitudinal techniques would elucidate the temporal relationship between minority stress and
behavioral health outcomes in SMA (Elze, 2005; Marshal et al., 2009; Meyer, 2003; Rosario et
al., 2002).
The present research is the first that examines behavioral health outcomes of depression,
anxiety, and substance use in SMA and sexual minority stress, while accounting for life stress.
This research would indicate whether over time, minority stress, due to its unique, stigma-related
effects has a negative relationship to behavioral health that cannot be explained by the
occurrence of other factors, such as life stress.
Intervention efforts will be informed through assessments of minority stressors over time
and their unique contribution to behavioral health outcomes (Marshal et al., 2009). The proposed
study can contribute to the development of tailored treatments for SMA (Hatzenbuehler, 2009),
which have increased effectiveness due to their ability to target populations and subgroups based
on stress experiences (Hecht et al., 2003; Marsiglia, Ayers, Gance-Cleveland, Mettler, & Booth,
2012). Additionally, this type of study would contribute to understanding how frequently mental
health should be measured in relation to minority stress to provide for best practices for the
timing of interventions. Prior studies show that interventions that incorporate minority stress
models have the potential to improve the long-term health of SMA and lead to strength-based
approaches for behavioral health recovery (Alessi, 2014; King et al., 2008; Marshal et al., 2009).

14

The Present Study
We sought to examine a novel measure of sexual minority stress that improves upon prior
measures limited in the narrowness of their definition of minority stress and inability to address
intersecting identities (Russell & Fish, 2016). In the present study, we predicted that the SMASI
will demonstrate internal consistency via Cronbach’s alphas of .7 or greater at each time point
(Cronbach, 1990; cf Tavakol & Dennick, 2011). This reliability statistic will mean that the
measure and factors within the measure are consistent with one another, meaning that the
questions all assess the same construct. We also predict the measure will demonstrate factorial
invariance over time. Demonstration of factorial, specifically measurement invariance, will
contribute to the validation of SMASI as a novel assessment tool (Dimitrov, 2010). Finally, we
hope to examine the stability of this construct using successive univariate latent growth curve
models to understand what the trajectory of sexual minority stress looks like in SMA over time.
Studies have commented on the need for further understanding of behavioral health
phenomena over time in adolescents (Beesdo, Knappe, & Pine, 2011), and specifically, SMA
(Corliss et al., 2010; Mustanksi, Andrews, & Puckett, 2016). More research is needed to
understand substance use, depression, and anxiety over time in SMA. Given the disproportionate
occurrence of mental health concerns in SMA, in this paper we investigated the longitudinal
course of three behavioral health outcomes: depression, anxiety, and substance use. We
hypothesized based on prior works that the measures would be reliable and that the anxiety
variable would demonstrate the most stability over time.This research is intended to build on past
works and inform the risk and resilience of sexual minority individuals through examining the
reliability, factor invariance, and the stability of depression, anxiety, and substance use (Poteat,
Aragon, Espelage, & Koenig, 2009).
15

While other research has examined the convergent validity, factor loadings, and
reliability of the measure cross-sectionally (Goldbach, Schrager, & Mamey, 2017; Schrager,
Goldbach, & Mamey, 2017), this study seeks to address longitudinal stability and developmental
trajectory that would make the SMASI measure viable for use of assessment in conjunction with
behavioral health outcomes over time. If this measure demonstrates stability, it could provide
researchers with an opportunity to explore and determine underlying mechanisms of change for
SMA-targeted interventions.
In the present study, we examined the following questions, in order:
1) Is minority stress related to behavioral health in SMA? If it is, is minority stress related
to behavioral health in SMA over time?
2) Is general life stress related to behavioral health in SMA?
3) Does minority stress account for some of the variance in behavioral health over and
above life stress cross-sectionally?
We predicted that sexual minority stress would relate to behavioral health in SMA over
time and that cross-sectionally this relationship would be present even when accounting for life
stress. We hypothesized further that bivariate latent growth curves would indicate a
unidirectional relationship from minority stress onto behavioral health, in support of the minority
stress hypothesis (King et al., 2008; Meyer, 2003). Last, to address the question of stability of
change between behavioral health outcomes and stress, we conducted a power-estimation study
using data from the correlational results to inform efficient practices in sampling methods for
future works.

16

Chapter Two: Method
Participants
Eligibility. Adolescents were eligible to participate in the study if they were between the
ages of 14 and 17, spoke English or Spanish (and could read study materials in either English or
Spanish), identified as either cisgender male or female, and stated their sexual orientation as gay,
lesbian, bisexual, or pansexual.
Recruitment and Participation. Participants were recruited through two different
approaches. First, 20 adolescents were recruited in-person at The LGBTQ Center of Long Beach
which provides resources and services for racially and ethnically-diverse LGBTQ adolescents. In
the second data collection approach, 1,495 adolescents were recruited online through
advertisements in social media that linked back to the study, targeting lesbian, gay, and
bisexually-identified adolescents (i.e., Facebook and Reddit forums). These recruitment methods
have been highlighted for accessing hard-to-reach populations in non-experimental research
(Topolovec-Vranic & Natarajan, 2016) and reviewed as a recruitment device elsewhere (see
Ramo & Prochaska, 2012). Individuals who clicked on advertisements for the study were taken
to two eligibility surveys via Fluid Survey and Qualtrics to determine if they were eligible to
participate (Qualtrics, 2017). This method allowed each individual to click through to the study
only one time and provided a two-step authentication process to assess the uniqueness of their IP
and GPS addresses.
There were 692 individuals who met inclusion criteria for the research and completed all
eligibility measures. Individuals were then examined for uniqueness and validity (for full
breakdown of inclusion numbers and criteria, see Schrager, Goldbach, & Mamey, 2017).
Researchers excluded 230 for creating contrived or duplicative identities/data and excluded 116
17

for internal validity concerns, resulting in 346 participants. The same validity checks stated
above were conducted at each time point during the longitudinal study. This resulted in the
removal of 42 additional individuals: 33 individuals were excluded due to contrived
identity/data and 9 individuals did not pass a validity check. The resulting data is based on 304
individuals.
The sample was ethnically diverse, with 12.5% African-American, 9.2% Asian, 37.0%
Caucasian, 8.6% multiple races, 24.8% Latino/Hispanic, 1.7% American Indian/Alaska Native or
Pacific Islander, and 1.0% other. At time point one, the mean age of the adolescents was 15.90
(SD = 0.99; range 14-17). Participants were 56.6% female and 96.7% participants were currently
enrolled in school.
Measures
Anxiety. The State-Trait Anxiety Inventory six-item measure (STAI; Marteau & Bekker,
1992; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983; Appendix B) was used to assess
anxiety symptoms from time points two to nine using a 1-4 scale per item (“not at all” to “very
much”) resulting in a summed score (see Appendix B for reverse scored items). The measure has
demonstrated sufficient internal consistency (α = .86 - .95) and test-retest reliability (r = .65 -
.75) (Spielberger et al., 1983; Tluczek, Henriques, & Brown, 2009).
Depression. Researchers utilized the Center for Epidemiologic Studies Depression
Scale, four-item short form (CES-D; Eaton, Smith, Ybarra, Muntaner, & Tien, 2004; Melchior,
Huba, Brown, & Reback, 1993; Radloff, 1977; Appendix C). This measure asked about feelings
of depression, loneliness, sadness, and crying spells within the past week. Adolescents rated the
items from 0 (“rarely or none of the time”) to 3 (“most or all of the days”). Items were then
summed for a total depression score. In prior research, the four-item measure correlated with the
18

twenty-item measure (r = .87; Melchior et al., 1993) and demonstrated strong internal
consistency (α = .91; Miller et al., 2008).
Recent Life Stress. Recent life stress was examined using a summed count of 56 binary
items that asked “which of the following have you experienced in the last month?” and assessed
whether life stressors occurred in the past 30 days using items from the Adolescent Stress
Questionnaire (Bryne, Davenport, & Mazanov, 2007; Appendix E). This scoring procedure
differed from the original scoring of the Adolescent Stress Questionnaire, which used a Likert-
type scale of subjective stressfulness of each item in ten separately scored domains. This study
used scoring similar to the count measure of recent sexual minority stress explained below,
which assessed only whether stressful events occurred and a summed count score of past 30 day
stressors (e.g., Brody et al., 2006 and Kim, Conger, Elder, & Lorenz, 2003; see Hammen, 2005
for a review of stress measurement). Recent life stressors were from 10 general domains: stress
at home (12 items), school performance (7 items), school attendance (3 items), romantic
relationships (5 items), peer pressure (7 items), teacher interaction (7 items), future uncertainty
(3 items), school/leisure conflict (5 items), financial pressure (4 items), and emerging adult
responsibility (3 items). In past research the likert-scored version of the measure has
demonstrated strong test-retest reliability and internal consistency (average α from each domain
= .74) (Bryne et al., 2007).
Recent Sexual Minority Stress. Recent sexual minority stress was measured through a
count of 54 binary items assessing past 30-day stressors on the Sexual Minority Stress Checklist
(total score SMASI; Schrager, Goldbach, & Mamey, 2017; Appendix A). Adolescents were
asked to “choose which applies,” and then stated whether an item “happened within the past 30
days” or “never happened (or happened more than 30 days ago).” Their responses endorsing the
19

“happened within the past 30 days” items were then summed for a total count score. This form
assessed 10 domains of sexual minority stress on all participants relating to adolescent sexual
minority stressors in the following domains: social marginalization (8 items), family rejection
(11 items), internalized homonegativity (7 items), identity management (3 items), homonegative
climate (4 items), intersectionality (3 items), negative disclosure experiences (5 items), religion
(5 items), negative expectancies (3 items), and homonegative communication (5 items) (See
Appendix A for items in each subdomain). The original measure has 11 domains and assesses
“work” stress for those currently employed; however, this domain is not included in the total
score and is therefore not reported here. We were interested in the total count score of past 30-
day SMASI due to theoretical relationships of broad multidimensional measures of stress onto
behavioral health (Balsam et al., 2011; Goldbach et al., 2014; Meyer, 2003; Morrison et al.,
2016). The 30-day SMASI total score has demonstrated criterion validity with measures of
depressive symptoms (r = .26, p < .001) and problem behaviors (r = .39, p < .001) and divergent
validity with adolescent life stress subscales (r = .17–.43), as reported elsewhere (see Goldbach,
Schrager, & Mamey, 2017).
Substance Use. Adolescents were asked to endorse whether they had used 18 different
substances over the past month from the Youth Risk Behavior Surveillance System (YRBS;
CDC, 2015; Appendix D). The questionnaire asked adolescents “have you used [substance] in
the past 30 days.” A summed score of the variables representing individual drugs used in the past
month were combined to form a total substance use score. This measure demonstrated test-retest
reliability in past samples (mean kappa statistic for alcohol use, 63.4%; for tobacco use, 68.8%;
Brener, Kann, McManus, Kinchen, Sundberg, & Ross, 2002).

20

Procedure
For the in-person study, researchers contacted 20 initial participants at “The Center” and
“Lifeworks” of Long Beach by asking adolescents during drop-in hours if they were interested in
a research study. Potentially eligible adolescents were taken to a private room at the recruitment
sites and were assessed to see if they met eligibility requirements. For the Internet-based study,
researchers posted advertisements on Facebook and Reddit (in a discussion sub-forum
“r/LGBTeens”) targeting the population of interest. This recruitment method expanded from
initially collecting data in urban cities in California (Los Angeles, San Francisco) to nationwide.
To assent to the study, adolescents were given an informational sheet about the study and
IRB requirements, and watched a video that read the assent document aloud. The adolescents
who participated online assented to the study over the internet. Since disclosure of adolescents’
identities might result in negative reactions from adolescents’ parents, the IRB waived parental
consent.
Adolescents then began the study, which consisted of demographic questions, the SMASI
measure, and the measure of life stress, followed by clinical outcome measures. Study
participation at time point one varied in length, from 14 minutes to several hours. Adolescents
were also asked to verify that they had a working email address where a digital gift card could be
sent upon completion of the study. Compensation for the study was in the form of a $25
Amazon.com gift card.
Respondent-driven sampling. Adolescents were asked if they would be willing to help
recruit three additional people to take the survey. Participants were given three “coupons” to use
to refer friends to the study. For each friend they referred, these “seed” participants received an
additional $10 USD in amazon.com gift cards. Friends of seed participants (up to three) then
21

called or accessed a unique link to the study to find out more about the study and determine their
eligibility for participation. Sixty three percent of participants were ultimately recruited for the
present study through respondent-driven sampling.
Longitudinal follow-up. At the end of the study at time point one, adolescents filled out
a survey where they gave their email address, so that their Amazon.com gift card could be sent to
as payment. At that time, adolescents indicated if they were interested in future research and
entered either their email address or phone number. Adolescents were told that they would be
emailed or telephoned for more information to start the follow-up process. Adolescents who
indicated interest in the follow-up study were contacted monthly for three months after their
initial participation to see if they would complete follow-up measures. Adolescents were given
the opportunity to assent to the longitudinal portion of the study at that time and were informed
that their data from the first time point of the study would be linked to the longitudinal study
using a code. Individuals were re-contacted every month for nine months to continue their
participation in the study.

22

Chapter Three: Analytic Plan
All analyses were conducted in SPSS Statistics Version 17 and R software, Version 3.1.0.
Analyses are on data from nine measurement points across eight months. Missing data were
accounted for using full-information maximum likelihood (“FIML”; Enders, 2001). This method
for handling missing data allows no data to be imputed and sets parameters to be estimated using
all available data, not just complete cases of data.
Preliminary Analyses. Before addressing our main study aim of examining the
relationship between recent sexual minority stress and behavioral health, we looked at attrition
and demographic variables to elucidate any a priori relationships that might impact key study
variables. We also examined descriptive statistics of the main study variables.
Cross Sectional Analyses. We looked cross-sectionally at whether sexual minority stress
might account for additional variance in behavioral health over and above life stress through
hierarchical regressions. We conducted hierarchical regressions at time point two to see if past
30-day minority stress contributed over and above past 30-day life stress on to mental health
outcomes. Next, we conducted three three-step linear hierarchical multiple regressions with
depression, anxiety and substance use as dependent variables. Age and gender were entered at
step one of the regression to account for their possible effects on the dependent variables. Life
stress was added at step two and minority stress was entered at step three, per our hypothesis that
minority stress would account for some of the variance in behavioral health over and above life
stress through examining the R
2
change at the final step.
Longitudinal Analyses. Before conducting longitudinal analyses, we wanted to
determine feasibility of statistical analyses. Using R software, we estimated power through
simulating data using variable correlational values generated from the present study. In
23

simulating these data we assumed a cross-lagged model structure, in which the stability of the x
(minority stress) and y (anxiety, depression, and substance use) variables assumed a 1
st
order
autoregressive structure. In this framework, the cross-lag between x and y represented the ability
of current values in x[t] to predict subsequent values of y [t+1] and for the current values of y [t] to
predict future values of x[t+1] over eight months. The average cross-correlation values were used
in the simulation and were constrained to be the same across each pair of time points. Concurrent
correlations between x and y (e.g. x[t]~y[t]) were allowed for exogenous variables only and were
based on the average correlation between x and y in the data. Using correlational values from the
present, we fit 1,000 simulations to each x and y pair and varied the number of participants from
30-100. We also varied the number of repeated measures occasions to 3, 4, 9, and 12 to represent
the number of waves of monthly data collection.
A series of univariate latent growth curve models (LGCMs) were used to model the total
SMASI score, CES-D score, STAI score, and substance use summary score over the course of
the study. For SMASI, the total SMASI score was used at each of the nine time points. We
followed steps from Burant (2016) to determine best model fit from the following models, in
order: a null, no-growth model, a linear model that fit intercepts to one and slopes to increasing
values at each time point, a non-linear, and a quadratic model. Additional models were evaluated
for fit without residual variances constrained to equality, meaning that variance in the error at
each time point was not set to be the same as other time points. We evaluated fit using the chi-
square (Χ²) statistic, comparative fit index (CFI), Tucker-Lewis index (TLI), Akaike information
criterion (AIC), Bayesian information criterion (BIC), and the root mean square error of
approximation (RMSEA) (per Hayduk, Cummings, Boadu, Pazderka-Robinson, & Boulianne,
2007; c.f., Cheung & Rensvold, 2002).
24

Next, bivariate latent growth curve models were used to determine whether a single
trajectory underlies growth in study variables across all timepoints and the relationship between
behavioral health and stress variables (Ferrer & McArdle, 2003). One bivariate latent growth
curve model was run with the SMASI and CES-D, given that power analyses supported
sufficient power only for the examination of SMASI with the CES-D over time. This model built
upon prior univariate latent change score models on sexual minority stress and depression. The
bivariate latent change score model examined how these two variables changed together
overtime and impacted each other. These models contain the following sources of influence:
proportional change for each variable (change related to amount of construct at previous time
point) via a proportional change parameter (β) and coupling change (how one variable influences
the other, and vice versa) via a coupling parameter (γ) (Ferrer & McArdle, 2003). The coupling
parameters allowed us to determine if one variable may “lead” (precede) or “lag” (come after)
the other. To examine the relationship between the SMASI and CES-D scores over time, we ran
four models. First, we ran a no coupling model in which both SMASI and CES-D coupling
parameters were fixed to zero. Second, we proceeded with a unidirectional model where the
SMASI coupling parameter was estimated for SMASI predicting changes in CES-D while the
parameter of CES-D predicting changes in SMASI was fixed to zero. Third, we examined a
unidirectional coupling parameter was estimated for CES-D predicting changes in SMASI while
the parameter of SMASI predicting changes in CES-D was fixed to 0. Fourth and finally, we
examined a bidirectional coupling model in which both coupling parameters between variables
were allowed to be estimated.
Example model equations for the first, no coupling model:
SMASI[t]n = μ0 SMASI + e
SMASI [t]n (1)
25

CES-D[t]n = μ0 CES-D + e
CES-D [t]n (2)
Example model equations for the second, unidirectional coupling model:
SMASI[t] n = μ 0 SMASI + β SMASI x SMASI [t-1] n + e SMASI [t] n (3)
CES-D[t] n = μ 0 CES-D + β CES-D x CES-D [t-1] n + γ SMASI x SMASI [t-1] n + e CES-D [t] n (4)
Example model equations for the third, unidirectional coupling model:
SMASI[t] n = μ 0 SMASI + β SMASI x SMASI [t-1] n + γ CES-D x CES-D [t-1] n + e SMASI [t] n (5)
CES-D[t] n = μ 0 CES-D + β CES-D x CES-D [t-1] n + e CES-D [t] n (6)
Example model equations for the forth, bidirectional coupling model:
SMASI[t] n = μ 0 SMASI + β SMASI x SMASI [t-1] n + γ CES-D x CES-D [t-1] n + e SMASI [t] n (7)
CES-D[t] n = μ 0 CES-D + β CES-D x CES-D [t-1] n + γ SMASI x SMASI [t-1] n + e CES-D [t] n (8)
We evaluated fit as per Hayduk, Cummings, Boadu, Pazderka-Robinson, and Boulianne
(2007) using the chi-square (Χ²) statistic, comparative fit index (CFI), Tucker-Lewis index
(TLI), Akaike information criterion (AIC), Bayesian information criterion (BIC), and the root
mean square error of approximation (RMSEA).

26

Chapter Four: Results
Preliminary Analyses
After wave one of data collection, additional individuals were excluded due to a) the
length of their survey being too short to yield valid data (n = 7) or b) because we lost contact
with them (n = 1). Out of 80 eligible follow-up participants contacted, 62 completed the second
wave of research (79.54% attrition from all wave 1; 22.50% attrition from wave 1 follow-up
candidates). Forty-four adolescents completed the study after nine waves of monthly data
collection (87.28% attrition from wave 1-9; 45.00% attrition from follow-up sample).
We tested for differences between those who opted in and out of the survey using t-tests
and chi-square difference tests on key study variables and demographic variables at wave one.
Differences were not found for participant’s self-reported age, school enrollment, sexual
minority stress score or substance use score at wave one. There was a significant effect for
gender, Χ
2
(1)= - 5.17, p < .05, such that males were more likely to opt into the study than
females. Differences were also noted in terms of the life stress score (t(150) = -2.28, p < .05) and
the depression score (t(190) = -3.21, p < .01), with those opting in to the study having more
elevated symptomology on both measures at wave one. Differences in terms of the anxiety
outcome could not be determined due to the sequencing of when this measure was given for the
first time in wave two. Via independent samples t-tests, we found no significant differences by
gender on the main study variables (see Table 1 for means). We used a one-way ANOVA to
investigate the effect of ethnicity on the main study variables. For these analyses the assumption
of homogeneity of variance was violated; therefore, the Brown-Forsythe F-ratio is reported.
There was a significant effect of ethnicity on the SMASI score, F(7, 19.79) = 3.50, p < .05,
indicating different scores by ethnic group on wave one of the study measure. The Brown-
27

Forsythe F-ratio was non-significant for the effect of ethnicity on anxiety score at wave two,
F(5,6.7) = 0.38, p = .85; life stress at wave one, F(7, 22.80) = 1.86, p = .12; or depression score
at wave one, F(7=7, 18.94) = 18.94, p = .19.
Post hoc analyses on the SMASI variable by racial/ethnic differences were evaluated
using the Games-Howell test given the violation of the homogeneity of variances assumption.
The Games-Howell test revealed differences between American Indian/Alaskan Native (M =
2.06, SD = 1.44) and Non-Hispanic White (M = 11.02, SD = 7.46) participants, such that Non-
Hispanic White participants scored higher on average (p < .001). Latino/Hispanic (M = 8.97, SD
= 8.20) and American Indian/Alaskan Native (M = 2.06, SD = 1.44) participants demonstrated
differences, where Latino/Hispanic participants scored higher on average (p < .001). African
American (M = 14.79, SD = 11.51) participants scored higher than American Indian/Alaskan
Native participants (M = 2.06, SD = 1.44; p < .001). There were also differences between
multiracial (M = 7.19, SD = 5.30) and African American (M = 14.79, SD = 11.51) participants,
with African American participants scoring higher (p < .05). Asian (M = 9.29, SD = 11.03)
participants, additionally, scored higher than American Indian/Alaskan Native participants (M =
2.06, SD = 1.44; p < .05). Finally, there were differences between American Indian/Alaskan
Native and multiracial participants, such that multiracial participants scored higher on average (p
< .01). Other post hoc comparisons demonstrated no differences between racial/ethnic groups on
the SMASI variable.
SMASI Descriptives and Reliability. From the data in Table 2, the range of SMASI was
0 to 52 out of a possible score of 54 items endorsed. The mean of the SMASI measure was
highest on the second wave of data collection at 14.35 (SD = 9.96) and lowest on the last wave
(8.36). The mean SMASI count score was 10.10 at time point one and 8.36 at time point nine.
28

Pearson’s correlation coefficients demonstrated positive relationships between time points,
ranging from r(44) = .30, p < .05 between time points one and eight, to r(42) = .92, p < .001,
between time points seven and nine (see Table 3 for all SMASI correlations by time point). The
total score demonstrated reliabilities between ɑ = .81 - .89, which are considered evidence of
sufficient internal consistency (Nunnally, 1978).
Anxiety Descriptives and Reliability. At time point 2, the internal consistency of the
anxiety measure was sufficient (α = .78; see Table 4 for reliabilities by time point). The STAI
measure had scores within the full range of the measure, from 6-24 with a mean of 14.84 (SD =
4.77) at time point 2, the first time point anxiety was assessed. The average score was 14.21, at
time point 9. Pearson’s correlation coefficients demonstrated positive relationships to prior time
points and ranged from r(37) = .21, p = .18 between time points four and eight, to r(42) = .64, p
< .001, between time points three and four (see Table 5 for correlations).
Depression Descriptives and Reliability. At time point one, the measure demonstrated
strong internal consistency (α = .88; see Table 4 for full reliabilities across the time points). The
range of the CES-D was 0-12 out of a possible score of 12. The mean of the CES-D measure was
5.40 (SD = 3.72) at time point one. Pearson’s correlation coefficients revealed positive
relationships within the measure between time points, ranging from r(43) = .35, p < .05 between
time points one and six, to r(40) = .81, p < .001, between time points two and five (see Table 6
for correlation table).
Substance Use Descriptives and Reliability. The internal consistency was α = .59 at
time point one. While generally the measure demonstrated strong reliabilities above ɑ = .70,
there were exceptions to this at two time points: time point one and time point seven when ɑ =
.66 (see Table 4 for reliability data). On average participants endorsed using approximately one
29

substance. The mean substance use score was lowest at time point five, at 0.63 (SD = 1.35), and
highest at time point nine, at 1.26 (SD = 1.78). Pearson’s correlation coefficients demonstrated
positive relationships to prior time points and ranged from r(42) = .27, p = .08 between time
points two and four, to r(43) = .92, p < .001, between time points two and three. Two of the
twenty-eight correlations conducted on the substance use data over time were not significant (see
Table 7 for all substance use correlations by time point).
Regression Analyses
Before conducting two-step and three-step linear hierarchical regressions, we examined
collinearity statistics and found the variance inflation factor (VIF) to be within normal limits
(VIF = 1.00 - 1.56; Hair et al., 1998; Yoo et al., 2014) and no independent variables to be highly
correlated.
Three-step Hierarchical Regressions. At wave two with depression as the dependent
variable, hierarchical multiple regression revealed that at step one none of the demographic
variables of age and gender contributed significantly to the regression model (∆F(2, 52) = 2.77, p
= .07). They accounted for 9.6% of the variation in depression. Introducing the life stress
variable explained an additional 15.6% of variation in depression and this change in R² was
significant (∆F(1, 51) = 10.65, p < .01). Adding minority stress to the regression model
explained an additional 1.6% of the variation in depression and this change in R² was non-
significant (∆F(1, 50) = 1.12, p = .29). Together the five independent variables accounted for
26.9% of the variance in depression (F(1, 50) = 4.60, p < .01).
We carried out hierarchical analyses in the same way for anxiety as a dependent variable
at wave two, with stress predictors (life stress and minority stress) coming from wave two as
well. In these analyses, the demographic variables contribute significantly to the regression
30

model (∆F(2, 52) = 3.99, p < .05), and they accounted for 13.3% of the variation in anxiety.
Introducing the life stress variable explained an additional 20.5% of variation in anxiety. This
change in R² was significant (∆F(1, 51) = 15.80, p < .001). Adding minority stress to the
regression model explained an additional 3.1% of the variation in anxiety, a non-significant
change (∆F(1, 50) = 2.48, p = .12). Together the five independent variables accounted for 36.9%
of the variance in anxiety (F(1, 50) = 7.32, p < .001).
Similar analyses were repeated for substance use, in which none of the demographic
variables contributed significantly to the regression model (∆F(2, 51) = 1.55, p = .22) and they
accounted for 5.7% of the variation in substance use. Introducing the life stress variable, which
explained no additional variation in substance use (∆F(1, 50) = 0.01, p = .91). Adding minority
stress to the regression model explained an additional 0.1% of the variation in substance use and
this change in R² was non-significant (∆F(1, 49) = 0.03, p = .85). Together the five independent
variables accounted for 5.8% of the variance in substance use (F(1, 49) = 0.76, p = .56).
Longitudinal Analyses
Longitudinal Change in SMASI Total Score. A series of latent growth curve models
(LGCM) were used to longitudinally model changes over time of SMASI. We began with
applying a baseline model of “no change,” followed by a linear model, a quadratic model, and
lastly a freely estimating model to the data, which allowed the slopes to vary freely. The model
that demonstrated best fit to the data was the linear basis model without residual variance set
equal (see model fit in Table 17; comparison to baseline: ∆Χ
2
(11) = 101.85, p < .001). This
model had slope loadings specified to increase by one unit per month (per wave) over time. This
modelmodel had a latent mean at wave one of 10.58, which meant that participants had an
average score of 10.58 at time point one. The slope mean produced by the model was - 0.40,
31

which showed that scores tended to go down over time. The covariance between the intercept
and slope was not significant, meaning that there was no relationship between individuals’ initial
scores and direction of slope over time. The initial variance was 53.22, which indicated
significant variation around the average score at time one, and slope variance was 0.99,
indicating variation in the slope scores as well over time. Model fit indices were: chi-square
statistic = 140.06(df = 40), CFI = .79, TLI = .81, AIC = 4754.20, BIC = 4806.24, and RMSEA =
.09. The RMSEA indicates that the data is similar to the approximated covariance matrix, as .08
is a common standard of how well the model fits the data (Hayduk et al., 2007).
Longitudinal Change in Depression. A series of univariate latent growth curve models
(LGCM) were used to longitudinally model changes over time of the depression summary scores
across all waves. The model that best fit the data was the quadratic model. This model had slope
loadings specified to increase by one unit per month (per wave) over time linearly as well
quadratically (i.e., 0, 1, 2, 4, 9, 16, 25, 36, 49) onto different slopes. The model had a latent mean
at wave one of 5.33, which showed that individuals tended to start at an average score of 5.33.
The slope mean that the model produced was – 0.61 for the linear slope and 0.06 for the
quadratic slope. The covariances between linear slope, quadratic slope, and intercept were non-
significant (intercept and linear slope, p = 0.35; intercept and quadratic slope, p = .65; linear
slope and quad slope, p = .06). The variance of the linear slope was non-significant (p = .05),
while the initial level variance was 9.95 indicating significant variation around the initial mean
Model fit statistics (found in table 18) were: chi-square statistic = 48.56(df = 36), CFI = .96, TLI
= .96, AIC = 3409.72, BIC = 3476.02, and RMSEA = .03. Model fit in this univariate model was
strong, with CFI and TLI indicating that the fit of the model compared to a model where all of
32

the variables are uncorrelated is different, suggesting good model fit with values approaching
1.00 (Hayduk et al., 2007).
Longitudinal Change in Anxiety. The model with no errors that demonstrated best fit
was the linear basis model without residual variance set equal (see model fit in Table 19;
comparison to baseline model ∆Χ
2
(3) = 8.62, p < .05). This model had slope loadings specified
to increase by one unit per month (per wave) over time. The linear model had a latent mean at
wave one of 14.84, which meant that individuals started with a score of 14.84 on average. The
slope was not significant (p = .20), indicating there were not overall changes in scores across
time that would indicate a trajectory of growth or decline. The covariance between the intercept
and slope was significant (β = - 0.97, p < .05), meaning that there was a relationship where
participants who started high on the anxiety measure tended to demonstrate decreasing scores
with time, and vice versa. The initial variance was 15.68, the slope variance was non-significant
(p = .12), and the error variance was 8.74. These findings indicate variation between participants
in terms of starting level and error terms. Model fit indices were: chi-square statistic = 42.77(df =
31), CFI = .93, TLI = .94, AIC = 2061.62, BIC = 2090.08, and RMSEA = .08. Model fit for this
univariate model was also strong, with RMSEA at or below .08 and CFI and TLI approaching
1.00.
Longitudinal Change in Substance Use. A final series of LGCMs were also used to
longitudinally model changes over time in the substance use summary scores. We began with
applying a baseline model of “no change,” followed by a linear model, a quadratic model, and
lastly a freely estimating model to the data, which allowed the slopes to vary freely from waves
four to nine. The freely estimating models did not converge, meaning that we have available data
on three types of models that did converge with no errors. The model with no errors that
33

demonstrated best fit to the data was the quadratic model (see model fit indices in Table 20).
This model demonstrated improved fit beyond the baseline (∆Χ
2
(3) = 26.10, p < .001) and linear
models (∆Χ
2
(4) = 40.88, p < .001). This model had linear slope loadings specified to increase by
one unit per month (per wave) over time and quadratic loadings set to increase quadratically over
time (calculated as linear loadings squared). The model had a latent mean at wave one of 1.13,
indicating that individuals tended to start with a score of 1.13. The linear slope was -0.25 and the
quadratic slope was 0.04, indicating there were changes in scores across time that relate to both
these trajectory shapes – one that was positive for a quadratically-shaped curve and negative for
linear decrease over the time points. The covariances between the intercept and slopes were
significant, meaning that there was a relationship such that participants who started high on the
substance use measure tended to demonstrate decreasing scores with time (-0.94) linearly and
positive relationship to the quadratic slope (0.11). The linear slope and quadratic slope were
negatively associated (-0.06), showing the trajectories of the two tended to be heading in
different directions. The initial variance was 2.95, showing variation around the initial mean, and
the slope variance was 0.47 for the linear slope and 0.01 for the quadratic slope. This indicated
that the slopes of both the linear and quadratic shapes were upwards. Model fit statistics for the
quadratic model were: chi-square statistic = 107.07(df = 34), CFI = .76, TLI = .80, AIC =
1230.76, BIC = 1252.35, and RMSEA = .18. Model fit was not particularly strong for the
substance use univariate latent growth curves.
Bivariate Latent Growth Curves. Researchers used latent growth curve modeling
(LGCMs) to examine the SMASI and depression scores over time across all time points. We first
tested a no coupling model (see equations 1 and 2 from the analytic plan), followed by a
unidirectional coupling model (equations 3 and 4 from the analytic plan) in which changes in
34

CES-D scores were predicted by previous SMASI scores. In the no coupling model the model fit
was: chi-square statistic = 356.99(df = 170), CFI = .79, TLI = .81, AIC = 8167.65, BIC =
8238.27, and RMSEA = .06. For the second, univariate model the model fit was: chi-square
statistic = 354.86(df = 169), CFI = .79, TLI = .81, AIC = 8167.52, BIC = 8241.86, and RMSEA
= .06. This model was not significantly different from our first, no coupling model (∆Χ
2
(1) =
2.13, p = 0.14; see Table 21 for all model fit statistics). Next, we tested a unidirectional model in
which CES-D scores predicted changes in SMASI (see equations 5 and 6 from the analytic plan).
This model demonstrated improved fit upon our baseline model (∆Χ
2
(1) = 46.79, p < .001). The
model fit statistics for this univariate coupling model were: chi-square statistic = 310.20(df =
169), CFI = .84, TLI = .85, AIC = 8122.86, BIC = 8197.20, and RMSEA = .05. We consider this
model to be the best representation of the dynamic relationship between minority stress and
depression. The dual change score model (see equations 7 and 8 from the analytic plan) was the
last model fit to the data. This model did not demonstrate significant improvement upon the
univariate coupling model (∆Χ
2
(1) = 0.52, p = 0.47) and had poorer AIC and BIC, which
together indicated that based on the number of parameters estimated, degrees of freedom, and
sample size that the third model had best fit. Model fit for the dual change score model was: chi-
square statistic = 309.68(df = 168), CFI = .84, TLI = .85, AIC = 8124.35, BIC = 8202.40, and
RMSEA = .05.
The univariate model had an initial mean of SMASI at 10.08, indicating that on average
people started at 10.08, with variation around the mean of 58.27. The average slope was -7.32,
indicating that on average scores went down over time. The slope variance was 144.72,
indicating variation in slope scores. For the CES-D variable, the initial mean was 5.33, which
was the average score at time one. The slope was 1.53, indicating that participants’ scores
35

declined with time at a rate of 1.53. The variance around the initial mean was 9.40 and around
the slope was 1.30, indicating significant differences between individuals in terms of starting
score and rate of decline over time (slope).
In this univariate model, proportional change for CES-D indicated deceleration in growth
(β = -0.40), indicating that in the latent modeling that CES-D scores were negatively impacted by
previous CES-D scores. Proportional change for SMASI showed deceleration in growth as well
at a rate of - 0.92. The coupling parameter (γ = 3.86) indicated changes in SMASI were
positively impacted by the previous level of the CES-D, indicating that the CES-D was the
leading determinant of change in the model. This parameter showed that CES-D increases at one
time predicted growth in SMASI at later time points.
Model equations were: ∆SMASI[t]n = 10.08 - 0.92 x SMASI [t-1]n + 3.86 x CES-D [t-1]n
∆CES-D[t]n = 5.33 - 0.40 x CES-D [t-1]n
Post-Hoc Power Estimation Study. Using R software, we estimated power through
simulating data based on effect sizes presented in our correlations. Though the cross-correlations
varied depending on the variables we were examining (e.g., relationship between SMASI and
CES-D compared to SMASI and substance use), they yielded generally small-to-moderate
effects for the relationship between x[t]~y [t+1] and for the y[t]~ x[t+1] cross-lag (see Tables 22-24
for power estimates). We found cross-lagged models resulted in sufficient power (>.78) to detect
a medium effect size in the cross-correlation between the CES-D and SMASI or ASQ assuming
9 samplings with 60 individuals. Power using the STAI over 9 monthly samplings with 60
individuals to detect a medium effect size was .69 for the SMASI and at .80 for the ASQ.
However, power was inadequate for examining substance use outcomes over time (.08 1- β,
ASQ; .24 1- β, SMASI).
36

For future works looking at the STAI and the SMASI, we found that power was sufficient
to detect cross-correlations with either 60 persons measured over 12 time points or 80 persons
measured over 9 time points at a medium effect size. For the CES-D, it demonstrated sufficient
power for a medium effect size if the study were to sample 30 people across 9 time points or
over 80 people across 4 time points. The substance use measure demonstrated less power, with
.47 = 1 - β over 12 time points sampling 100 people.

37

Chapter Five: Discussion and Conclusion
This study represents a novel examination of minority stress given the age range of the
sample, the study’s use of multi-domain measures of adolescent minority and life stress, and its
examination of behavioral health with minority stress over time. We determined the reliability
and stability of behavioral health trajectories using correlations and univariate growth curves to
better understand these outcomes in SMA over an eight-month period. Additionally, we
predicted that these outcomes would be relatively stable over time, with the anxiety summary
scores showing less change over a seven-month period. Using bivariate chain analyses we found
that over nine sampling points that the behavioral health outcome of depression was the leading
indicator for changes (increases) in minority stress.
Attrition and Retention
The present study had only 22.50% attrition from time point 1 to time point 2, and
45.00% attrition across time points 1-9. These numbers are in contrast to studies such as Project
MATCH (1997), which achieved a 8% attrition rate across time points. One factor that could
have led to less attrition between time points 1-9 is payment. Researchers in this study proposed
to the IRB that compensation be stepped (e.g., $15 for time point two, $20 dollars for time point
three, etc.); however, the IRB stated this approach may be coercive, leading at-risk SMA to
continue participating despite compromising circumstances. Future studies might consider
engaging SMA through social media or offering small incentives throughout the month to
promote increased retention (Mustanski, 2015). Balancing privacy concerns regarding
membership or affiliation with a sexual minority-affiliated study must be kept in mind when
considering methods of recruiting and retaining SMA participants.

38

Preliminary Analyses
Individuals who opted to be followed up demonstrated unique differences in scores on
study measures at time one. Males were likely to opt in to being re-contacted for follow-up
studies, whereas females were equally likely to opt in or out. Differences in life stress and
depression were also noted in individuals who opted to be re-contacted for follow-up research:
these participants tended to demonstrate higher scores on both variables. In this way, the present
study may be viewed as an informal intervention, in which these adolescents could self-select to
answer questions about stress and behavioral health, perhaps increasing their awareness and
understanding of how these domains impact their functioning over time.
We found different scores on the key study measure (SMASI) at time point one in terms
of ethnicity, indicating that individuals responded differentially by ethnic group membership.
Looking at the means for these measures revealed a trend where African American participants
and multi-ethnic participants reported the most stress at time one. On the STAI, African
American participants tended to score higher than participants of other ethnic backgrounds,
whereas on the CES-D, Native Hawaiian and other Pacific Islander participants tended to score
higher than individuals of other ethnic backgrounds. This finding makes sense with prior
literature showing a relationship between minority stress and intersectionality of micro
aggressions for LGBT people of color (Balsam et al., 2011). Intersectionality stress was one of
the foundational components of the present measure, distinguishing it from previous measures in
that people may feel marginalized depending on the how their various identities are situated in
each context (Brody et al., 2006). Future studies should expand upon the nascent findings here
that suggest differences by ethnicity in terms of sexual minority stress experiences (Balsam et al.,
2011; Goldbach & Gibbs, 2014).
39

Reliability
Overall behavioral health outcomes demonstrated strong reliability, consistent with prior
research, as evidenced by ɑ = .70 or higher (Cronbach, 1990). As an inventory of diverse
stressors, it was unclear whether the SMASI total count variable variable would demonstrate
sufficient reliability. This total count variable of minority stress did demonstrate sufficient
reliability, with reliabilities between α = .81 - .89.
In other studies of life and minority stress researchers have either decided to treat stress
counts as continuous variables and measure Cronbach’s alpha (e.g., Brody et al., 2006) or have
not provided reliability results on the count measures (e.g., Kim, Conger, Elder, & Lorenz,
2003). Based on prior work by Schrager, Goldbach, and Mamey (2017) assessing the reliability
and factor invariance of the domains and overall measure cross-sectionally by gender and age,
we had evidence supporting our findings here that support the reliability of the total score.
While most outcomes in the paper demonstrated strong reliability as did substance use,
on two occasions of measurement the substance use demonstrated poor reliabilities of less than ɑ
= 0.70. We found lower reliability on these occasions, most likely because the substance use
variable was a tally of different substances which participants used. These substances may be
related different than other multi-dimensional constructs we examined for anxiety and
depression .
SMASI Correlational Analyses
Correlations generally revealed that the SMASI total score was positively associated
across all time points. Time points seven and nine demonstrated the highest correlation, which
may be explained by the small sample completing the last few time points of data collection.
40

Our SMASI correlations revealed that our data aligned with past work. For instance, we
found that the correlation coefficient for anxiety was r = .53 (correlation coefficient between
time points 2 and 7 in the present study), similar to what Rosario et al. (2002) found over six
months, r = .54. Rosario et al. found a correlation of r = .35 for their depression score association
across six months, which is the same association that we found in the present data across five
months (from time point 1 to time point 6).
There was a tendency for earlier time points of the SMASI total score to be less
associated with later time points. This finding fits in that, generally over time, there will be
within individual differences in stress experiences reported compared to only after one month. It
might also be explained by bias in responding in close sequence to stressors that occurred more
remotely. One solution to diminish the temporal effects of sampling would be to use an in-vivo
sampling strategy, such as Experience Sampling Method (see Hatzenbuehler, Nolen-Hoeksema,
& Dovidio, 2008), although this does not resolve methodological concerns relating to the single
informant nature of the study.
SMASI Univariate Longitudinal Analyses
We used univariate latent growth curves to examine several aspects of the longitudinal
trajectory of sexual minority stress in our sample of adolescents. Using the SMASI summary
score over eight months (nine time points), we expected the baseline model to demonstrate poor
fit, which was supported by our data. The linear basis growth model was found to have better fit
to the data, as its shape, which goes up over time points two and three and then decreases
steadily, fit the data best. The overall shape of the SMASI summary score appears to be a
quadratic shape followed by a linear decline. It makes sense, therefore, that the linear and
quadratic models evidenced better fit to the data than a null model or freely estimating approach.
41

One hypothesis for why individuals reported increasing then decreasing SMASI scores
over time would be that as individuals become aware of minority stressors they have elevated
knowledge and heightened attention to minority stress experiences. This awareness, once
realized after several samplings, then decreases perhaps what can be described as regression to
the mean.
The best fit was demonstrated by a linear model that allowed the residual variances to not
be constrained to equal over time. If the variances were constrained to equal, this would suggest
that the error term was more controlled in the present study and not as dependent on extraneous
factors outside the researcher’s control, such as which participants accessed the survey and
when, and perhaps how much attention they paid to doing the survey at the time. Future studies
might estimate a piecewise model to find further improved fit (Timmons & Preacher, 2015).
LGCM analyses can reveal how large the latent mean is, whether individuals might
change over time, what the strength and form of change is, and whether differences in slope
occur between individuals or if an individual’s starting value is related to their directionality of
change. We found support for individuals changing over time, given our significant slope. Also,
the linear model with variances unconstrained indicated significant variation around the intercept
(starting point) and slope (change) in mean and variance across individuals. However, a person’s
starting score was not related to their slope over time.
The SMASI total score was evaluated through univariate latent growth curves as a
precursor to conducting bivariate longitudinal modeling. This variable may be used in
conjunction with other behavioral health outcomes, with the limitation that the trajectory of the
score is quite complex: adolescents encounter unique experiences at different points in their
sexual identity development and how this impacts their experience of minority stressors is
42

different for every person. For instance, some individuals accessing the study may not have
disclosed their identity to family or friends and may report stress around their disclosure,
whereas other individuals do not experience disclosure stress at the time of sampling (Goldbach,
Schrager, & Mamey, 2017). The impact of disclosure on other areas of minority stress, such as
victimization at school or family stress, merits examination in future work, along with age and
cohort effects. Due to the temporality of longitudinal data, individuals may not have consistently
completed each time point of the study in month increments, which may have created additional
variation in the data between time points. Additional limitations of using SMASI are discussed
below.
Behavioral Health Univariate Longitudinal Analyses
Anxiety Univariate Latent Growth Curves. Importantly, our work showed that certain
outcomes tended to be more stable over time. Anxiety demonstrated a stable trajectory over
seven months of data collection. Previous work has shown that anxiety diminishes slightly
through adolescence (Hale et al., 2008), which is what this study indicated over seven months.
Given its stability and non-significant slope in the latent growth model, it may be difficult in
future studies to predict that stressors may impact later changes in anxiety within a brief, seven-
month window. Longitudinal studies of behavioral health along with stress may necessitate the
use of power estimation methods before examining anxiety within a one year period. Variables
must demonstrate some statistical variability in order for statistics to pick up on changes in
anxiety and account for them. Additionally, it may be more difficult for interventionists to
structure temporally-sequenced, SMA-targeted interventions that address anxiety given its
stability.
43

Depression Univariate Latent Growth Curves. Depression univariate analyses
indicated that a quadratic slope best fit the data, demonstrating strong fit. This quadratic slope
yielded a declining trajectory across time. Ge et al. (2001) similarly found decreases in this
trajectory during adolescence. As Ge and colleagues stated, adolescence is a time of particular
importance for understanding depression, given the onset of puberty and social-emotional
changes that may leave adolescents vulnerable to depression.
The benefit of using a latent growth curve with the present sample is that it allows us to
estimate the changes in the slope, intercept and residual of the depression and anxiety scores over
time (Marshal et al., 2009; Mustanski et al., 2016). Given the quadratic curve of depression in
the present study, it may be easier for researchers to see changes in the short-term and use other
assessments to inform when changes in depression onset for SMA.
The present research, given the reliability of the substance use construct and poor fit as a
single factor to the data over time, we moved forward with looking at the longitudinal growth of
substance use outcomes over time. Other researchers (Duncan, Alpert, Duncan, & Hops, 1997;
Marshal et al., 2009) have used latent growth modeling to examine levels of alcohol use over
adolescence. In general, youth indicated some substance use at time point one and increased over
time. This finding has been replicated in other studies where, regardless of sexual orientation,
use increases through adolescence (Marshal et al., 2009). Marshal found that there were
individual differences around intercepts and slopes and that a random effects method like LGCM
would be an appropriate way to consider the longitudinal growth of substance use as an outcome.
However, the effects of the quadratic and linear slopes should be considered to understand the
complexities of SMA substance use. It appears from the item means and the slope trajectories
that substance use in the sample may be sporadic. Although complex, substance use trajectories
44

may be appropriate to examine in shorter increments given the strong disparities in SMA
behavioral health research (Marshal et al., 2008).
Timmons & Preacher (2015) posit that in linear growth, the timing and spacing of
observations are not as critical to understanding the underlying trajectory. However, if a more
complex trajectory underlies the data, then finite timing and spacing to observations become
necessary. Historically, the literature has documented an effect where smaller intervals of
measurement yield greater power at detecting effects in the data. Previous studies of behavioral
health and minority stress in adolescents have relied on six month-to-one year time frames (e.g.,
Birkett, Newcomb, & Mustanski, 2015; Frost et al., 2015; Mustanski & Liu, 2013), although
relevant exceptions do exist (e.g., using an adult sample, Hatzenbuehler, Dovidio, Nolen-
Hoeksema, & Phills, 2009).
The general recommendation that with non-linearly sloped “curvy” data is that
measurement occasions should be spaced around the period of greatest change (Timmons &
Preacher, 2015). Within the life of a SMA, adolescence is a time of great determination for
identity and, possibly, for increasingly confronting sexual minority stressors along with life
stressors (Marshal et al., 2008). When we evaluated the growth curves of these behavioral health
outcomes; however encountered difficulty in estimating all the parameters accurately for freely
estimated latent growth curves. Several factors may have contributed to errors in estimation and,
in one case, model convergence. First the small sample size and missingness of data may not
have allowed these models to converge error-free. Timmons & Preacher (2015) find that for
smaller samples power tends to be adequate for linear growth curves; however, “curvy” LGCMs
warrant increased observations around the points of change. The outcomes in the present study
demonstrated more-complex, quadratic trajectories, indicating the need for many sampling points
45

to increase power and accuracy in prediction of these trajectories (Timmons & Preacher, 2015).
Having a larger sample size who consistently completed the measure would improve power.
Relationship between Sexual Minority Stress and Behavioral Health
Regression Analyses. The present study found using three-step hierarchical regressions
that minority stress did not explain additional variance in behavioral health outcomes of
depression, anxiety, or substance use. These findings are distinguished from prior work that
theorizes minority stress may contribute uniquely over and above life stressors (Meyer, 2003). It
may be that life and minority stressors impact adolescents differentially and life stressors may
present more acute danger to adolescents. Null findings indicating that minority stress does not
contribute to deleterious behavioral health over and above life stress is a similar finding to Frost
et al.’s (2015) work. Frost and colleagues found that self-reported stigma was not related to
physical health over one year in a sample of sexual minority adults. Frost et al. (2015) theorized
that certain forms of minority stress, disclosure (being “out”) and internalized homophobia, are
unique, whereas other minority stressors (family or work stress) may not be as unique in their
impact on sexual minority persons. However, these findings still differ from studies such as
Herek et al.’s (1999), which found that hate crimes have a larger impact on victims than crimes
not motivated by hate, in support of Minority Stress Theory (Meyer, 2003).
SMASI and Depression Bivariate Longitudinal Analyses. Using bivariate LGCMs we
found strong fit for a unidirectional depression-to-minority stress change score model.
Depression scores unidirectionally impacted SMASI scores via a coupling parameter, indicating
CES-D scores related to later increases in SMASI scores. Proportional change was negative and
significant on both variables, indicating a downward trajectory within each variable from the
previous time point.
46

Earlier analyses examining the stability of depression and anxiety over time with
minority stress demonstrated that researchers may need more resources to recruit more persons
to measure anxiety over time in SMA, whereas depression was less stable over time and more
prone to fluctuation within this time span. Greater fluctuation and lower Pearson’s r values
between assessments makes it less costly for researchers to measure depression longitudinally in
relation to other outcomes. If a variable is relatively stable, demonstrating high correlations
between time points of assessment like anxiety, researchers may have to wait until longer periods
of time have elapsed (e.g., over one year) to ensure that the symptoms being reported have
enough variance to be accounted for by some other construct related to the variable being
measured, such as minority stress.
Power Estimation Study Recommendations. We examined 1
st
order cross-lagged
autoregressive models in a power estimation study, which allow for the examination of the
relationship of true scores of variables over time (Ferrer & McArdle, 2003). Our goal was to
guide future researchers wishing to examine stress and behavioral health in SMA over time. We
make recommendations on the basis of findings here and summarize other findings from the
literature, both on methodology and substantively from other longitudinal samples of SMA.
In the present study, we examined the stability of behavioral health and stress and used
our results from correlations in an empirical power estimation to demonstrate how much power
cross-lagged autoregressive analyses would have using a newly validated measure of minority
stress. We found that for these variables assessments of upwards of 9 months would be necessary
or assessments using as many as 60 people for anxiety and depression, but that measuring
substance use would require more observations.
47

Power in the present study using an autoregressive technique would have been inadequate
to examine anxiety and substance use variables in conjunction with sexual minority stress;
however power was sufficient for depression. The values indicating the stability of the clinical
outcomes of depression and anxiety over time also indicates that researchers may need more
resources to recruit more persons to measure anxiety over time in SMA, whereas depression is
less stable over time and more prone to fluctuation within a shorter time span (9 months). This
fluctuation and lower Pearson’s R values between assessments impacts researchers by making it
less costly for researchers to measure depression longitudinally along with other outcomes.
Statistically if a variable, such as substance use or anxiety in the present study, is relatively
stable, demonstrating high correlations between time points of assessment, then researchers are
incumbent to wait until a longer period of time has elapsed (over one year) to ensure that the
symptoms in a person may have enough change to be accounted for by some other related
variable being measured, such as minority stress.
We recommend that future studies consider the type of trajectory of behavioral health in
prior work before conducting longitudinal assessments. One of the benefits of conducting a
thorough literature review is that it will inform the type of relationship you expect to see and can
inform the timing of data collection time points (Timmons & Preacher, 2015).
Mediational analyses would be another way to examine the relationship between three or
more variables over time. Analyses looking at the relationship of three variables together should
account for added parameters in estimating the sample size needed. Burton et al. (2013)
demonstrated this effect with 197 participants over 3 time points in this type of study. However,
Burton and colleagues examined depression scores over time, which given its growth curve and
instability, may be more feasible than anxiety across two time points.
48

Limitations and Future Directions
There were several limitations in the present study. The first limitation is the sample from
which the data was drawn. The sample was recruited from an online pool of participants, who
were then seeds, reaching other hard-to-reach SMAs (Ramo & Prochaska, 2012; Topolovec-
Vranic & Natarajan, 2016). There were benefits to using this method for sampling, such as the
strong ethnic diversity within the sample. Future works may want to capitalize on online
methods using several forms of data validation to ensure that each participant accesses the survey
portal and submits their data to the study only one time. In the present study, this meant
screening hundreds of additional participants to achieve the resulting sample. This filtering
process was a disadvantage in terms of the time and money invested to yield a valid longitudinal
sample. Future works may benefit from paying for recruitment services such as Qualtrics or
devising strategies a priori about the stringency of standards for their data, given that online
recruitment devices yield large samples quickly (for a discussion, see Brandon, Long, Loraas,
Mueller-Phillips, & Vansant, 2014; Peer, Paolacci, Chandler, & Mueller, 2012).
Given that limited research has used longitudinal methods to examine the relationship
between minority stress and behavioral health, there is little precedent on how to accommodate
barriers to sampling SMA over time, such as challenges to recruitment and retention (Cotter,
Burke, Loeber, & Navratil, 2002). We attempted to prepare for barriers by establishing
relationships with local adolescent-based study sites and consulting the literature (Cotter et al.,
2002; Elze, 2003; 2005; Project MATCH, 1993; 1997) on how to optimize retention strategies
with these adolescents. Despite these efforts, recruitment efforts were limited by the sheer
number of adolescents entering gay-affirming agencies, which led us to recruit online instead.
49

As no nationally-representative data has been collected on minority specific stressors (or
perceived minority stressors) and behavioral health, new data must be collected to examine the
present research question. One limitation of the present work is the use of a sample recruited
from predominantly online means (e.g., Facebook, Reddit). The results found in this study may
not be generalizable to all adolescents, especially those who are not accessing targeting
advertisements and forums online at present. Additionally, select adolescents came from gay-
affirming organizations (i.e., community and school agencies). By using multiple channels of
recruitment, researchers gain greater generalizability than through recruiting at one institution.
The use of measures that are well-validated and have been used in past research with
similar samples (e.g., YRBS; CDC, 2011; 2015), along with demographic data allows
researchers to compare the outcomes in the present study to those of others SMA samples and
look at consistency in findings. One way the present study could be improved is through the use
of objective stress measures. Self-report data has situational and individual biases associated
with it and can be improved upon (Dohrenwend, 2006).
Research by Meyer (2003b) discussed the possible issue of under-reporting of minority
stress. Meyer commented on work by Contrada et al. (2000), who theorized that it benefits
individuals who are discriminated against to de-emphasize the discrimination they experience in
their environment: too much of it detected would lead to declines in life satisfaction. Therefore,
it may be that cognitively “healthy” individuals coping with discrimination use an appraisal style
such that they ignore discriminatory acts when they occur, which would then lead to an under-
reporting of discrimination events.
Latent growth modeling was a strength of the present study in that it allowed researchers
to investigate a single trajectory underlying the growth in each variable and between variables
50

across all time points. However, the variables used in the latent growth curve modeling indicated
a few ways the future longitudinal studies on SMA might be improved. For one, the relationship
between anxiety and sexual minority stress over time had non-significant coupling and slope
loadings, despite the data supporting a dual change score model. These findings may indicate
that a third variable is at play impacting both trajectories unaccounted for by the model.
Additionally, although this model demonstrated best fit to the data, fit here and in other analyses
has been poor compared to traditional standards of fit (Hu & Bentler, 1999).
Other studies using the SMASI could contribute to the literature by examining
longitudinal growth patterns of subsamples of the SMASI sample. It may be that some
adolescents have relatively low and stable growth trajectories on the SMASI measures, whereas
others demonstrate higher trajectories with varied slopes. Future work might investigate how
latent classes score over time on SMASI to better understand the diversity of trajectories here.
SMASI’s use as a novel instrument could benefit clinicians if researchers take steps to
continue to understand its relationship to health outcomes. Cluster analyses may be helpful to
demonstrate ties for certain items relating to clinical diagnoses. Shortening the measure in this
way could be helpful for understanding the epidemiological value of a novel measure of
adolescent stress. The present study had several limiting factors, including small sample size or a
count variable suggesting one factor estimated over time. However, other studies measuring life
and minority stress similarly and use their measures to examine stress over time (see Brody et al.,
2006; Kim, Conger, Elder, & Lorenz, 2003). Future research should take into account how
temporality of stress events may relate to factor loadings onto an overall factor and that factor
loadings for the past 30-day SMASI data converging onto a single factor may be lower than
lifetime estimates.
51

One direction recommended for future research is to evaluate the longitudinal latent
classes behind these behavioral health outcomes. Other researchers such as Mustanksi, Andrews,
and Puckett (2016) have investigated symptoms of posttraumatic stress and depression in a
sample of youth in Chicago. They found four classes of membership, such that females were
likely to be in a class of low, declining victimization. High starters who had experienced
decreasing symptoms were at increased risk for posttraumatic stress. These findings bring to
light the need for differential analyses that cluster by subgroups of gender or other subgroups to
better understand the epidemiological implications of the present findings. Additionally,
Mustanksi et al.’s (2016) results indicate the need for examination of behavioral health outcomes
in combination with stressors, such as victimization.
Conclusion
We examined the stability of sexual minority stress through correlations and a latent
growth curve, which showed that the shape of the average participant’s sexual minority stress
trajectory was generally linear, but increased over the first two months. This measure, given the
variation it demonstrated across eight months, is a reliable assessment of minority stress that
could be used in tandem with other behavioral health measures to guide intervention. Statistical
methods should be used that yield nuance in revealing the complicated trajectory of adolescent
sexual minority development to capture the complex phenomena of sexual minority stress.
This exploratory study examined the stability and course of sexual minority stress in a
sample of SMA and determined whether sexual minority stress may be related to behavioral
health outcomes cross-sectionally and over time. We utilized a novel measure of sexual minority
stress, SMASI, which addressed previous limitations in terms of intersectionality and breadth of
measurement of sexual minority stress as a construct (see Goldbach, Schrager, & Mamey, 2017).
52

In three papers we explored the reliability, univariate, and bivariate growth of sexual
minority adolescent scores on this novel measure of sexual minority stress and anxiety,
depression, and substance use measures in a sample of 304 SMA referred either in person,
online, or by peers. Latent growth curves showed that summary scores on the SMASI measure
tended to increase, then steadily decrease over time. Minority stress measured via SMASI did not
account for variance explained over and above general life stress. Bivariate latent growth curve
modeling did not indicate that minority stress preceded changes in either anxiety or depression
health outcomes. We provided a power simulation study to advance knowledge of how these
variables interact together over time. Limitations included sample size, which was limited by
eligibility, data validity concerns, and attrition.

53

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63

Tables
Table 1
Means for Key Study Variables at Wave One
Variable
Opted into Being
Recontacted
Mean(SD)
Gender

Mean(SD)
Age
Opt Out: 15.97(.99) Female: 15.85(1.05)
Opt In: 15.82(.98) Male: 15.97(.89)
Gender
Opt Out: .33(.47) NA
Opt In: .48(.50) NA
School Enrollment
Opt Out: .98(.14) Female: .97(.18)
Opt In: .96(.20) Male: .97(.17)
Depression
Opt Out: 4.40(3.87) Female: 5.64(3.83)
Opt In: 5.93(3.44) Male: 5.11(3.57)
Anxiety (wave two)
Opt Out: NA Female: 16.04(5.02)
Opt In: NA Male: 13.81(4.24)
Substance Use
Opt Out: .62(1.30) Female: .88(1.33)
Opt In: .92(1.05) Male: .79(.93)
Sexual Minority Stress
Opt Out: 10.44(12.24) Female: 10.53(10.96)
Opt In: 10.80(8.12) Male: 10.32(7.11)
Life Stress
Opt Out: 23.02(14.12) Female: 26.38(12.47)
Opt In: 26.67(9.40) Male: 25.74(9.52)

64

Table 2
Sample Size, Means, and Ranges of Total SMASI Score

Variable n Mean(SD) Potential Range Actual Range
SMASI wave 1 303 10.10(8.92) 0-54 0-52
SMASI wave 2 62 14.35(9.96) 0-54 0-35
SMASI wave 3 51 13.24(10.18) 0-54 0-34
SMASI wave 4 51

11.10(9.68) 0-54 0-36
SMASI wave 5 50 9.82(9.74) 0-54 0-37
SMASI wave 6 46 8.98(8.91) 0-54 0-37
SMASI wave 7 48 8.90(9.06) 0-54 0-37
SMASI wave 8 46 8.52(8.95) 0-54 0-33
SMASI wave 9 44 8.36(9.71) 0-54 0-37

65

Table 3
Intercorrelations of SMASI Over Time
Measure W1 W2 W3 W4 W5 W6 W7 W8 W9
1. SMASI
W1
(n = 303)
--

2. SMASI
W2
(n = 62)
.55*** --

3. SMASI
W3
(n = 51)
.56*** .67*** --

4. SMASI
W4
(n = 51)
.49*** .66*** .84*** --

5. SMASI
W5
(n = 50)
.53*** .57*** .75*** .90*** --

6. SMASI
W6
(n = 46)
.37* .72*** .82*** .82*** .79*** --

7. SMASI
W7
(n = 48)
.43** .67*** .79*** .71*** .67*** .86*** --

8. SMASI
W8
(n = 46)
.30* .57*** .81*** .61*** .61*** .75*** .84*** --
9. SMASI
W9
(n = 44)
.41** .66*** .76*** .58*** .64*** .76*** .92*** .90*** --
Note: *p < .05, **p < .01, ***p < .001. Two-tailed significance.

66

Table 4

Cronbach’s Alpha Reliability Coefficients of Key Study Variables
Measure
Wave 1

Wave 2

Wave 3

Wave 4

Wave 5

Wave 6

Wave 7

Wave 8

Wave 9

CES-D .88 .88 .88 .90 .91 .90 .86 .83 .86
SMASI .86 .83 .81 .84 .85 .85 .86 .85 .89
STAI NA .78 .74 .73 .79 .72 .81 .77 .78
Substance Use .59 .79 .76 .90 .74 .74 .66 .77 .73

67

Table 5
Intercorrelations of STAI Over Time
Measure W1 W2 W3 W4 W5 W6 W7 W8 W9
1. STAI
W1
(n = NA)
NA

2. STAI
W2
(n = 40)
NA --

3. STAI
W3
(n = 32)
NA .61*** --

4. STAI
W4
(n = 34)
NA .65*** .64*** --

5. STAI
W5
(n = 35)
NA .56*** .60*** .68*** --

6. STAI
W6
(n = 33)
NA .53*** .63*** .64*** .62*** --

7. STAI
W7
(n = 35)
NA .53*** .61*** .48** .45** .49** --

8. STAI
W8
(n = 29)
NA .57*** .33* .22 .40** .44** .21 --
9. STAI
W9
(n = 32)
NA .40* .37* .47** .61*** .62*** .45** .54** --
Note: *p < .05, **p < .01, ***p < .001. Two-tailed significance.

68

Table 6
Intercorrelations of CES-D Over Time
Measure W1 W2 W3 W4 W5 W6 W7 W8 W9
1. CES-D
W1
(n = 293)
--

2. CES-D
W2
(n = 56)
.77*** --

3. CES-D
W3
(n = 51)
.61*** .80*** --

4. CES-D
W4
(n = 51)
.55*** .76*** .75*** --

5. CES-D
W5
(n = 47)
.58*** .81*** .72*** .78*** --

6. CES-D
W6
(n = 46)
.35* .42** .56*** .52*** .70*** --

7. CES-D
W7
(n = 48)
.68*** .69*** .73*** .65*** .63*** .62*** --

8. CES-D
W8
(n = 44)
.66*** .69*** .69*** .66*** .39*** .65*** .74*** --
9. CES-D
W9
(n = 43)
.61*** .70*** .58*** .51** .68*** .58*** .67*** .65*** --
Note: *p < .05, **p < .01, ***p < .001. Two-tailed significance.
69

Table 7

Intercorrelations of Substance Use Over Time
Measure W1 W2 W3 W4 W5 W6 W7 W8 W9
1. SUDS
W1
(n = 303)
--

2. SUDS
W2
(n = 55)
.49*** --

3. SUDS
W3
(n = 49)
.42** .92*** --

4. SUDS
W4
(n = 50)
.71*** .27 .38* --

5. SUDS
W5
(n = 49)
.66*** .41** .46** .84*** --

6. SUDS
W6
(n = 45)
.60*** .51** .55*** .61*** .58*** --

7. SUDS
W7
(n = 47)
.59*** .37* .43** .60*** .77*** .49** --

8. SUDS
W8
(n = 45)
.57*** .47** .44** .56*** .64*** .58*** .83*** --
9. SUDS
W9
(n = 42)
.47** .34* .50** .60*** .72*** .47** .69*** .81*** --
Note: *p < .05, **p < .01, ***p < .001. Two-tailed significance.

70

Table 8

Sample Size, Means, Standard Deviations, and Ranges of Outcomes
Wave n

Mean(SD)
CES-D
Potential Actual

Mean(SD)
STAI
Potential Actual

Mean(SD)
SUDS
Potential Actual
wave 1 303 5.40(3.72) 0-12 0-12 NA NA NA 0.84 (1.72) 0-17 0-7
wave 2 62 5.05(4.04) 0-12 0-12 14.84(4.77) 6-24 6-24 1.09(1.85) 0-18 0-9
wave 3 51 4.65(3.72) 0-12 0-12 15.16(4.48) 6-24 7-24 1.12(1.69) 0-18 0-9
wave 4 51

5.04(3.87) 0-12 0-12 14.33(4.89) 6-24 6-24 0.72(1.39) 0-18 0-8
wave 5 50 4.00(3.94) 0-12 0-12 14.38(4.56) 6-24 6-24 0.63(1.35) 0-18 0-8
wave 6 46 4.48(3.71) 0-12 0-12 14.60(4.26) 6-24 6-23 0.91(1.58) 0-18 0-6
wave 7 48 4.71(3.49) 0-12 0-12 13.85(4.30) 6-24 6-24 0.89(1.37) 0-18 0-6
wave 8 46 4.59(3.18) 0-12 0-11 14.57(4.34) 6-24 6-24 1.24(1.82) 0-18 0-8
wave 9 44 4.72(3.77) 0-12 0-11 14.21(3.78) 6-24 8-24 1.26(1.78) 0-18 0-7

71

Table 9
Intercorrelations of SMASI and CES-D Over Time
Measure
CES-D
W1
CES-D
W2
CES-D
W3
CES-D
W4
CES-D
W5
CES-D
W6
CES-D
W7
CES-D
W8
CES-D
W9
1. SMASI
W1
(n = NA)
.27***

2. SMASI
W2
(n = 40)
.29* .34*

3. SMASI
W3
(n = 32)
.16 .27 .23

4. SMASI
W4
(n = 34)
.33* .42** .40** .31*

5. SMASI
W5
(n = 35)
.21 .45** .37* .35* .44**

6. SMASI
W6
(n = 33)
.15 .21 .28 .18 .25 .28

7. SMASI
W7
(n = 35)
.16 .16 .07 .21 .21 .22 .27

8. SMASI
W8
(n = 29)
.04 .15 .25 .12 .27 .42** .37* .23
9. SMASI
W9
(n = 32)
.04 .17 .13 .08 .31* .36* .24 .21 .27
Note: *p < .05, **p < .01, ***p < .001. Two-tailed significance.
72

Table 10

Intercorrelations of SMASI and STAI Over Time
Measure
STAI
W1
STAI
W2
STAI
W3
STAI
W4
STAI
W5
STAI
W6
STAI
W7
STAI
W8
STAI
W9
1. SMASI
W1
(n = NA)
NA

2. SMASI
W2
(n = 40)
NA .41*

3. SMASI
W3
(n = 32)
NA .41* .07

4. SMASI
W4
(n = 34)
NA .18 .23 .08

5. SMASI
W5
(n = 35)
NA .28 .21 .20 .19

6. SMASI
W6
(n = 33)
NA .17 .18 .16 .09 .11

7. SMASI
W7
(n = 35)
NA .09 .05 -.11 -.01 -.09 .15

8. SMASI
W8
(n = 29)
NA .15 .16 .09 .16 .09 .23 .17
9. SMASI
W9
(n = 32)
NA .11 -.06 -.10 .05 -.03 .15 .20 .20
Note: *p < .05, **p < .01, ***p < .001. Two-tailed significance.
73

Table 11

Intercorrelations of SMASI and Substance Use Over Time
Measure
SUDS
W1
SUDS
W2
SUDS
W3
SUDS
W4
SUDS
W5
SUDS
W6
SUDS
W7
SUDS
W8
SUDS
W9
1. SMASI
W1
(n = 303)
.11

2. SMASI
W2
(n = 61)
.13 .03

3. SMASI
W3
(n = 50)
- .05 -.08 -.08

4. SMASI
W4
(n = 50)
.12 .12 .09 .09

5. SMASI
W5
(n = 49)
.06 .02 .03 .04 16

6. SMASI
W6
(n = 45)
.02 -.10 -.08 -.04 .01 .20

7. SMASI
W7
(n = 47)
.02 .11 .10 -.03 .12 .25 .08

8. SMASI
W8
(n = 45)
.04 -.02 -.07 -.13 .01 .11 .20 .01
9. SMASI
W9
(n = 43)
.01 .17 .18 .12 .16 .11 .21 .23 .23
Note: *p < .05, **p < .01, ***p < .001. Two-tailed significance.
74

Table 12

Intercorrelations of SMASI and ASQ Over Time
Measure
ASQ
W1
ASQ
W2
ASQ
W3
ASQ
W4
ASQ
W5
ASQ
W6
ASQ
W7
ASQ
W8
ASQ
W9
1. SMASI
W1
(n = 303)
.44***

2. SMASI
W2
(n = 61)
.43** .56***

3. SMASI
W3
(n = 50)
.43** .56*** .57***

4. SMASI
W4
(n = 50)
.29 .54*** .51*** .48***

5. SMASI
W5
(n = 49)
.29* .46** .51*** .50*** .64***

6. SMASI
W6
(n = 45)
.40** .47** .49** .39* .47** .55***

7. SMASI
W7
(n = 47)
.18 .31* .47** .07 .17 .45** .46**

8. SMASI
W8
(n = 45)
.18 .36* .47** .18 .30* .36* .47** .50***
9. SMASI
W9
(n = 43)
.15 .29 .42** .05 .17 .39* .40** .52** .54***
Note: *p < .05, **p < .01, ***p < .001. Two-tailed significance.
75

Table 13

Intercorrelations of ASQ and CESD Over Time
Measure
CES-D
W1
CES-D
W2
CES-D
W3
CES-D
W4
CES-D
W5
CES-D
W6
CES-D
W7
CES-D
W8
CES-D
W9
1. ASQ W1
(n = NA)
.43***

2. ASQ W2
(n = 40)
.29* 47***

3. ASQ W3
(n = 32)
.26 .44** .34*

4. ASQ W4
(n = 34)
.37** .62*** .51*** .62***

5. ASQ W5
(n = 35)
.23 .47** .47** .44** .47**

6. ASQ W6
(n = 33)
.25 .24 .20 .31* .37* .45**

7. ASQ W7
(n = 35)
.19 .25 .19 .22 .27 .41** .40**

8. ASQ W8
(n = 29)
.22 .40** .27 .36* .51*** .44** .21 .37*
9. ASQ W9
(n = 32)
.29 .29 .17 .29 .49** .49** .39* .45** .46**
Note: *p < .05, **p < .01, ***p < .001. Two-tailed significance.
76

Table 14

Intercorrelations of ASQ and STAI Over Time
Measure
STAI
W1
STAI
W2
STAI
W3
STAI
W4
STAI
W5
STAI
W6
STAI
W7
STAI
W8
STAI
W9
1. ASQ W1
(n = NA)
NA

2. ASQ W2
(n = 40)
NA

.54***

3. ASQ W3
(n = 32)
NA .37* .27

4. ASQ W4
(n = 34)
NA .56* .50*** 52***

5. ASQ W5
(n = 35)
NA .45** .35* .44** .41**

6. ASQ W6
(n = 33)
NA .32* .22 .29 .27 42**

7. ASQ W7
(n = 35)
NA .24 .23 .19 .23 .34* .43**

8. ASQ W8
(n = 29)
NA .42** .33* .31* .31* .36* .30 .36*
9. ASQ W9
(n = 32)
NA .31 .16 .14 .17 .40* .53*** .57*** .40*
Note: *p < .05, **p < .01, ***p < .001. Two-tailed significance.

77

Table 15

Intercorrelations of ASQ and Substance Use Over Time
Measure
SUDS
W1
SUDS
W2
SUDS
W3
SUDS
W4
SUDS
W5
SUDS
W6
SUDS
W7
SUDS
W8
SUDS
W9
1. ASQ
W1
(n = 303)
.09

2. ASQ
W2
(n = 58)
.18 .04

3. ASQ
W3
(n = 50)
.09 .05 .06

4. ASQ
W4
(n = 50)
.07 .00 .03 .10

5. ASQ
W5
(n = 49)
.17 -.04 -.01 .05 .05

6. ASQ
W6
(n = 45)
.13 -.04 -.02 .13 .14 .09

7. ASQ
W7
(n = 47)
.05 -.13 -.12 .01 -.04 .15 .-.05

8. ASQ
W8
(n = 45)
.23 .05 .06 .10 .09 .15 .09 .09
9. ASQ
W9
(n = 43)
.05 .06 .04 .05 .11 -.05 .05 .13 .19
Note: *p < .05, **p < .01, ***p < .001. Two-tailed significance.

78

Table 16

Intercorrelations of ASQ and SMASI Over Time
Measure
SMAS
I W1
SMAS
I W2
SMAS
I W3
SMAS
I W4
SMAS
I W5
SMAS
I W6
SMAS
I W7
SMAS
I W8
SMAS
I W9
1. ASQ
W1
(n = NA)
.44***

2. ASQ
W2
(n = 40)
.36** .56***

3. ASQ
W3
(n = 32)
.40** .32* .57***

4. ASQ
W4
(n = 34)
.24 .27 .36* .48***

5. ASQ
W5
(n = 35)
.38** .31* .46** .58*** .64***

6. ASQ
W6
(n = 33)
.23 .43** .56*** .49** .44** 55***

7. ASQ
W7
(n = 35)
.25 .43** .54*** .41** .30* .52*** .46**

8. ASQ
W8
(n = 29)
.43 .51*** .61*** .49** .47** .47** .46** .50***
9. ASQ
W9
(n = 32)
.22 .34* .53* .33* .32* .40* .45** .42** .54***
Note: *p < .05, **p < .01, ***p < .001. Two-tailed significance.

79

Table 17
SMASI Univariate LGCMs Model Fit Statistics
Model Χ
2
df RMSEA (90%
CI)
AIC BIC CFI TLI ∆Χ
2

from
baseline
∆df
from
baseline
1. Baseline 241.92 51 .11(.10-.13) 4834.06 4845.21 .60 .72 NA NA
2. Linear Basis 189.24 48 .10(.08-.11) 4787.38 4809.69 .71 .78 52.68 3
2a. Linear Basis
NR
140.06 40 .09(.08-.11) 4754.20 4806.24 .79 .81 101.86 11
3. Quadratic 168.89 44 .10(.08-.11) 4775.03 4812.20 .74 .79 73.03 7
3a. Quadratic
NR*
111.30 36 .08(.07-.10) 4733.44 4800.35 .84 .84 130.62 15
4. Freely Est. 177.43 41 .11 (.09-.12) 4789.57 4837.89 .72 .75 64.49

10
4a. Freely Est.
NR
177.43 41 .11 (.09-.12) 4789.57 4837.89 .72 .75 64.49

10
Note: “NR” indicates no residual variances were constrained to equality in the model.
*Lavaan reported one error in estimating the R
2
in wave nine of model 3a.

80

Table 18

Depression Univariate LGCMs Model Fit Statistics
Note: * denotes Lavaan indicated errors in model estimation.
Model Χ
2
df RMSEA (90%
CI)
AIC BIC CFI TLI ∆Χ
2

from
baseline
∆df from
baseline
1. Baseline 103.36 51 .06(.04-.08) 3434.52 3445.57 .85 .89 NA NA
2. Linear Basis 85.15 48 .05(.03-.07) 3422.31 3444.41 .89 .92 18.21 3
2. Linear Basis
NR
66.08 40 .05(.03-.07) 3419.25 3470.82 .92 .93 37.28 11
3. Quadratic 60.24 44 .04(.01-.06) 3405.40 3442.24 .95 .96 43.12 7
3. Quadratic NR 48.56 36 .03(.01-.06) 3409.72 3476.02 .96 .96 54.8 15
4. Freely Est.* 77.58 41 .06(.04-.07) 3428.74 3476.63 .89 .91 25.78 10
4. Freely Est.
NR*
77.58 41 .06(.04-.07) 3428.74 3476.63 .89 .91 25.78 10
81

Table 19

Anxiety Univariate LGCMs Model Fit Statistics

Model Χ
2
df RMSEA (90%
CI)
AIC BIC CFI TLI ∆Χ
2

from
baseline
∆df from
baseline
1. Baseline 60.29 41 .08(.03-.13) 2059.13 2065.70 .89 .93 NA NA
2. Linear Basis 51.67 38 .07(.00-.12) 2056.52 2069.63 .92 .94 8.62 3
2. Linear Basis
NR
42.77 31 .08(.00-.13) 2061.62 2090.08 .93 .94 17.52 10
3. Quadratic* 47.68 34 08(.00-.13) 2060.52 2082.42 .92 .94 12.61 7
3. Quadratic
NR*
37.99 27 08(.00-.13) 2064.83 2102.06 .94 .94 22.3 14
4. Freely Est.* 35.56 32 .04(.00-.10) 2052.40 2078.68 .98 .98 24.73 9
4. Freely Est.
NR*
35.56 32 .04(.00-.10) 2052.40 2078.68 .98 .98 24.73

9
Note: * denotes Lavaan indicated errors in model estimation.

82

Table 20
Substance Univariate LGCMs Use Model Fit Statistics

Model Χ
2
df RMSEA (90%
CI)
AIC BIC CFI TLI ∆Χ
2

from
baseline
∆df
from
baseline
1. Baseline 174.04 41 .23(.19-.26) 1283.74 1290.21 .56 .70 NA NA
2. Linear Basis 147.95 38 .21(.18-.25) 1263.64 1276.59 .63 .73 26.09 3
2a. Linear Basis
NR
132.89 31 .23(.19-.27) 1262.58 1290.65 .66 .69 41.15 10
3. Quadratic 107.07 34 .18(.15-.22) 1230.76 1252.35 .76 .80 66.97 7
3a. Quadratic
NR*
83.31 27 .18(.14-.23) 1221.00 1257.70 .81 .80 90.73 14
4. Freely Est.*
4a. Freely Est.
NR*

Note: * denotes Lavaan indicated errors in model estimation. Models information not available
on models that did not converge.

83

Table 21
Bivariate Latent Growth Curve for SMASI & CES-D
Parameters &
Fit Indices
Baseline
No-Coupling
SMASI CES-D
SMASI ->
∆CES-D
SMASI CES-D
CES-D ->
ΔSMASI
SMASI CES-D
Dual Change

SMASI CES-D
Dynamic Coefficients
Proportion β

-0.04
NS
0.58 -0.08
NS
-0.99 -0.92 -0.40 -0.84 -0.51
Loading α

1(=) 1(=) 1(=) 1(=) 1(=) 1(=) 1(=) 1(=)
Coupling γ

0 (=) 0(=) 0.14
NS
0(=) 0(=) 3.86 0.02
NS
3.65
Latent Means, Variances, & Correlations
Initial Mean 10.62 5.37 10.60 5.40 10.08 5.33 10.09 5.35
Slope Mean -0.06
NS
2.25 0.34
NS
2.43 -7.32 1.53 -7.18 1.74
Initial
Variance
58.63 9.67

59.20 9.44 58.27 9.40 58.41 9.44
Slope
Variance
1.04 2.83 1.51 8.22 144.72 1.30 127.67 2.02
Error
Variance
25.35 4.45 25.13 4.44 21.03 4.64 21.00 4.63
Ρ0,1 0.15
NS
0.84 0.28
NS
0.82 0.24
NS
0.91 0.21
NS
0.91
ΡS0,C0 0.36 0.36 0.32 0.32
ΡS0,C1 0.26
NS
-0.07
NS
0.16
NS
0.09
NS

ΡS1,C0 -0.29
NS
-0.20
NS
-0.76 -0.76
ΡS1,C1 0.12
NS
-0.23
NS
-0.75 -0.85
ΡeS,eC 0.13 0.14 0.17 0.18
Goodness of Fit
Parameters 19 20 20 21
X
2
/df 356.99/170 354.86/169 310.20/169 309.68/168
AIC 8167.65 8167.52 8122.86 8124.35
BIC 8238.27 8241.86 8197.20 8202.40
CFI .79 .79 .84 .84
TLI .81 .81 .85 .85
RMSEA
(90%)
.06 (.05-.07) .06 (.05-.07) .05 (.04-.06) .05 (.04-.06)

84

Table 22

CES-D & SMASI, ASQ Cross Lagged Power Analyses
Measure Sample
Size
Power
3 Waves
Power
4 Waves
Power
9 Waves
Power
12 Waves
SMASI
30
0.31 0.47 0.82 0.89
ASQ
30
0.25 0.32 0.55 0.62
SMASI
40
0.38 0.54 0.89 0.96
ASQ
40
0.24 0.33 0.67 0.74
SMASI
60
0.51 0.68 0.97 0.99
ASQ
60
0.32 0.45 0.78 0.90
SMASI
80
0.63 0.78 0.99 0.99
ASQ
80
0.37 0.51 0.87 0.95
SMASI
100
0.69 0.89 0.99 0.99
ASQ
100
0.44 0.61 0.94 0.98

85

Table 23

STAI & SMASI, ASQ Cross Lagged Power Analyses
Measure Sample
Size
Power
3 Waves
Power
4 Waves
Power
9 Waves
Power
12 Waves
SMASI
30
0.21 0.24 0.46 0.55
ASQ
30
0.22 0.28 0.54 0.63
SMASI
40
0.22 0.26 0.53 0.68
ASQ
40
0.26 0.36 0.63 0.79
SMASI
60
0.23 0.38 0.69 0.84
ASQ
60
0.30 0.45 0.80 0.88
SMASI
80
0.32 0.44 0.81 0.94
ASQ
80
0.40 0.55 0.90 0.96
SMASI
100
0.38 0.51 0.88 0.97
ASQ
100
0.45 0.61 0.94 0.98
*Note: assuming medium effect size (Cohen, 1992)

86

Table 24

SUDS & SMASI, ASQ Cross Lagged Power Analyses
Measure Sample
Size
Power
3 Waves
Power
4 Waves
Power
9 Waves
Power
12 Waves
SMASI
30
0.09 0.12 0.21 0.21
ASQ
30
0.08 0.08 0.09 0.11
SMASI
40
0.10 0.12 0.19 0.24
ASQ
40
0.07 0.08 0.08 0.09
SMASI
60
0.12 0.13 0.24 0.31
ASQ
60
0.07 0.07 0.08 0.13
SMASI
80
0.13 0.16 0.32 0.37
ASQ
80
0.06 0.07 0.09 0.09
SMASI
100
0.14 0.17 0.36 0.47
ASQ
100
0.06 0.09 0.09 0.13
*Note: assuming medium effect size (Cohen, 1992)

87

Figures

Figure 1. SMASI Total Score Means by Time Point

88

Appendices
Appendix A: Sexual Minority Adolescent Stress Inventory

Factor 2: Social Marginalization
1. Item 6_4: Other youth refuse to do school activities with me because I am LGBTQ.
2. Item 8_5 : I have seen other LGBTQ youth treated badly in the neighborhood where I
live.
3. Item 8_6 : I have felt unsafe or threatened in the neighborhood where I live because I am
LGBTQ.
4. Item 8_7 : I have had to move or change where I live because I am LGBTQ.
5. Item 8_8 : I have felt isolated or alone in the neighborhood where I live because I am
LGBTQ.
6. Item 8_9 : Other people in the neighborhood where I live make fun of me for being
LGBTQ.
7. Item 9_1 : I have been physically assaulted in the neighborhood where I live because I
am LGBTQ.
8. Item 9_6 : Other youth refuse to hang out with me because I am LGBTQ.

Factor 3: Family Rejection
1. Item 2_5: I have to lie to my family about being LGBTQ.
2. Item 3_1: If I come out, it will cause problems within my family.
3. Item 4_5: My family does not want to talk to me about being LGBTQ.
4. Item 4_6: Someone who lives with me has told me they disapprove of me being LGBTQ.
5. Item 4_7: I feel as though I am a disappointment to my family because I am LGBTQ.
6. Item 4_8: My family has told me that being LGBTQ is just a phase.
7. Item 4_9: My parents are uncomfortable with LGBTQ people.
8. Item 5_1: My mother (or female caregiver) does not accept me as LGBTQ.
9. Item 5_2: My father (or male caregiver) does not accept me as LGBTQ.
10. Item 5_5: My parents are sad that I am LGBTQ.
11. Item 5_7: My family tries to make me straight.

Factor 4: Internalized Homonegativity
1. Item 3_6: There are times when I do not want to be LGBTQ.
2. Item 3_7: If I could, I would become straight.
3. Item 3_8: I hate being LGBTQ.
4. Item 3_9: I think it is wrong for me to be LGBTQ.
5. Item 4_1: I hope that being LGBTQ is just a phase for me.
6. Item 4_2: I think negatively about other LGBTQ people who act “too gay”.
7. Item 4_3: I am uncomfortable with being LGBTQ.

Factor 5: Identity Management
1. Item 1_4: I am questioning how to label my sexual orientation.
2. Item 1_5: I am having trouble accepting that I am LGBTQ.
3. Item 1_6: I feel pressured to label myself as gay or lesbian.

89

Factor 6: Homonegative Climate
1. Item 5_8: I felt unsafe or threatened in school because I am LGBTQ.
2. Item 6_5: I have seen other LGBTQ youth treated badly at my school.
3. Item 6_6: It's hard to be an LGBTQ person at my school.
4. Item 6_7: Other students make fun of me for being LGBTQ.
Factor 7: Intersectionality
1. Item 9_7: Other people who are in my racial/ethnic community judge me for being
LGBTQ.
2. Item 10_2: I feel as though I don't fit in my racial/ethnic community because I am
LGBTQ.
3. Item 10_3: As an LGBTQ person in my racial/ethnic community, I feel like I am a
minority within a minority.
Factor 8: Negative Disclosure Experiences
1. Item 2_3: A family member told other family members that I am LGBTQ without my
permission.
2. Item 2_4: A family member told me not to tell other family members that I am LGBTQ.
3. Item 3_2: A family member asked me if I was gay or lesbian before I wanted to talk
about it.
4. Item 3_3: I was forced to come out to someone because I got "caught".
5. Item 3_4: I was "outed" by someone other than my family without my permission.

Factor 9: Religion
1. Item 11_6: My family is part of a religion that has homophobic beliefs.
2. Item 11_8: I would not be accepted as an LGBTQ person in my family's religion.
3. Item 12_1: I believe it is wrong for me to be LGBTQ because of my religion.
4. Item 12_2: A religious leader has encouraged me to reconsider my sexual orientation.
5. Item 12_3: A religious leader tried to change my sexual orientation.
Factor 10: Negative Expectancies
1. Item 1_7: I am concerned that if I am LGBTQ, I will have a worse life than if I were
straight.
2. Item 2_6: I think I will lose friends if I come out as LGBTQ.
3. Item 2_8: I expect people to reject me when they find out that I am LGBTQ.
Factor 11: Homonegative Communication
1. Item 4_4: I have heard a family member make negative comments about LGBTQ people.
2. Item 9_3: My friends make jokes about LGBTQ people.
3. Item 9_9: I have heard negative comments from others in my racial/ethnic community
about being LGBTQ.
4. Item 11_1: I hear other LGBTQ people use words like "fag" or "dyke."
5. Item 11_7: I have heard negative messages about being LGBTQ from religious people.

90

Appendix B: State Trait Anxiety Inventory (Short Form)

1. I feel calm. (R)
2. I am tense.
3. I feel upset.
4. I am relaxed. (R)
5. I feel content. (R)
6. I am worried.

91

Appendix C: Center for Epidemiologic Studies Depression Scale (Short Form)

1. I felt depressed.
2. I felt lonely.
3. I had crying spells.
4. I felt sad.

92

Appendix D: Substance Use Measure

1. Have you: Drank one or more drinks of alcohol in the past 30 days?
2. Have you: Used tobacco products (including cigarettes, chew/dip, electronic cigarettes,
hooka, cigars and other tobacco products) in the past 30 days?
3. Have you: Used inhalants (paint, whippets, aerosols) to get high in the past 30 days?
4. Have you: Used cocaine in the past 30 days?
5. Have you: Used marijuana or hashish in the past 30 days?
6. Have you: Used synthetic marijuana (K2, spice, moon-rocks) in the past 30 days?
7. Have you: Used poppers in the past 30 days?
8. Have you: Used bath salts to get high in the past 30 days?
9. Have you: Used heroin in the past 30 days?
10. Have you: Used MDMA (ecstasy, Molly, X, E, XTC) in the past 30 days?
11. Have you: Used daztrex in the past 30 days?
12. Have you: Used GHB (G) in the past 30 days?
13. Have you: Used ketamine (Special K, Cat, K) in the past 30 days?
14. Have you: Used LSD or mushrooms in the past 30 days?
15. Have you: Used methamphetamine (meth, tina, speed, crystal meth) in the past 30 days?
16. Have you: Used prescription pain relievers (such as Vicodin or OxyContin) without a
doctor’s orders in the past 30 days?
17. Have you: Used prescription tranquilizers (e.g. Xanax or valium) without a doctor’s
orders in the past 30 days?
18. Have you: Used prescription stimulants (e.g. Ritalin, Adderall) without a doctor’s orders
in the past 30 days?

93

Appendix E: Adolescent Stress Questionnaire

1) Arguments at home no yes
2) Disagreements between your parents no yes
3) Disagreements between you and your mother no yes
4) Disagreements between you and your father no yes
5) Lack of understanding by your parents no yes
6) Abiding by petty rules at home no yes
7) Living at home no yes
8) Not being taken seriously by your parents no yes
9) Little or no control over your life no yes
10) Lack of trust from adults no yes
11) Parents expecting too much from you no yes
12) Parents hassling you about the way you look no yes
13) Having to study things you do not understand no yes
14) Teachers expecting too much from you no yes
15) Difficulty with some subjects no yes
16) Keeping up with schoolwork no yes
17) Having to study things you are not interested in no yes
18) Having to concentrate too long during school hours no yes
19) Pressure of study no yes
20) Getting up early in the morning to go to school no yes
21) Compulsory school attendance no yes
94

22) Going to school no yes
23) Getting along with your boy/girl-friend no yes
24) Breaking up with your boy/girl-friend no yes
25) Making the relationship with your boy/girl-friend work no yes
26) Not having enough time for your boy/girl-friend no yes
27) Being ignored or rejected by the person you want to go out with no yes
28) Pressure to fit in with peers no yes
29) Being hassled for not fitting in no yes
30) Peers hassling you about the way you look no yes
31) Being judged by your friends no yes
32) Disagreements between you and your peers no yes
33) Satisfaction with how you look no yes
34) Changes in your physical appearance with growing up no yes
35) Lack of respect from teachers no yes
36) Not being listened to by teachers no yes
37) Getting along with your teachers no yes
38) Disagreements between you and your teachers no yes
39) Teachers hassling you about the way you look no yes
40) Abiding by petty rules at school no yes
41) Not getting enough timely feedback on schoolwork no yes
42) Concern about your future no yes
43) Having to make decisions about future work or education no yes
95

44) Putting pressure on yourself to meet your future goals no yes
45) Not getting enough time for leisure no yes
46) Not enough time for activities outside of school hours no yes
47) Not having enough time for fun no yes
48) Having too much homework no yes
49) Lack of freedom no yes
50) Not enough money to buy the things you need no yes
51) Not enough money to buy the things you want no yes
52) Pressure to make more money no yes
53)
Having to take on new financial responsibilities with growing
older
no yes
54) Having to take on new family responsibilities with growing older no yes
55) Employers expecting too much of you no yes
56) Work interfering with school and social activities no yes

Abstract (if available)

Abstract Sexual minority adolescents (SMA) are at a disproportionate risk for developing behavioral health problems and typically experience an earlier age of onset than heterosexual adolescents. Contributing to the Minority Stress Theory hypothesis, studies have linked minority stressors to poor behavioral health outcomes, primarily through cross-sectional approaches using limited operationalizations of minority stress (e.g., victimization stress). These papers explore the reliability, univariate latent growth curves, and bivariate latent growth curves of sexual minority adolescent scores on 1) a novel measure of sexual minority stress and 2) anxiety, depression, and substance use measures in a sample of 304 SMA referred either in person, online, or by peers. Researchers found that the novel measure of sexual minority stress demonstrated internal consistency across eight months of data collection. Additionally, the latent growth trajectory showed that summary scores on the SMASI measure tended to increase, then steadily decrease over time. Cross-sectionally, minority stress was linked with behavioral health outcomes

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Asset Metadata

Creator Burgess, Claire (author)

Core Title A longitudinal study of sexual minority stress and behavioral health in sexual minority adolescents

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Publication Date 10/03/2017

Defense Date 07/27/2017

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag adolescence,Adolescents,anxiety,Depression,latent growth curves,LGBT,measure,Mental Health,minority stress,minority stress hypothesis,minority stress theory,OAI-PMH Harvest,sexual minority stress,substance use,youth

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Monterosso, John (committee chair), Goldbach, Jeremy (committee member), Manis, Frank (committee member), Nation, Daniel (committee member), Schwartz, David (committee member)

Creator Email claireburgess10@gmail.com,s15m15e15e15@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-441519

Unique identifier UC11264011

Identifier etd-BurgessCla-5814.pdf (filename),usctheses-c40-441519 (legacy record id)

Legacy Identifier etd-BurgessCla-5814.pdf

Dmrecord 441519

Document Type Dissertation

Rights Burgess, Claire

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA