Research on body image among adolescent girls has seen a substantial increase during the past two decades due to its direct connection to physical and mental health problems. Unfortunately, the majority of these studies have been pathology-focused with extensive research examining negative conceptualizations of body image. Moreover, there is a dearth of research examining how adolescents from diverse cultural backgrounds experience body image. This paper addresses these gaps in the literature by first identifying distinct profiles of both positive and negative body image experiences among Latina adolescents. The emergent body image profiles were then linked to various psychosocial outcomes, including disordered eating, social engagement, and self-esteem. LCA results showed preference for a two-class model that included a Positive Attitude and Hoping to Change class and a Negative Attitude and Hoping to Change class. The Negative Attitude and Hoping to Change class demonstrated generally poorer outcomes, as they reported greater levels of disordered eating and lower functioning with regard to self-esteem and social engagement.

This Monte Carlo simulation study examined the performance of the most commonly used fit indices in selecting the "correct" latent class model while varying factors such as: the true number of latent classes, the size of the latent classes (i.e., class prevalence), the nature of the latent classes, the number of indicators, and sample size. Specifically, the fit indices examined in this simulation study were the Akaike Information Criterion (AIC), the Consistent Akaike Information Criterion (CAIC), the Bayesian Information Criterion (BIC), the adjusted Bayesian Information Criterion (ABIC), the adjusted Lo-Mendell-Rubin likelihood ratio test (LMR-LRT), the parametric bootstrapped likelihood ratio test (BLRT), the approximate Bayes Factor (BF), and the correct model probability (cmP). No study to date has examined the performance of the BF and cmP in recovering the correct latent class model.

This simulation study also aimed to simultaneously examine and understand how sample size, the number of observed indicators, and class enumeration intersect in latent class analysis (LCA) models. In other words, when sampling observations from a larger population, is there a critical point where the size of the sample and the number of indicators cannot uncover all existing heterogeneity? That is, at what point is specificity of the emerging latent classes lost?

All data were generated and analyzed using Mplus latent variable software (Muthén & Muthén, 1998-2013). The specific data generation and analysis conditions in this dissertation were created based on a literature search of Education and Psychology related databases. Results from this study will help applied researchers using LCA models further understand which fit index to trust under various conditions when going through the class enumeration process in practice. Specifically, the ABIC and BLRT indices emerged as being the highest performing across a variety of conditions considered in this study. Results also highlight the practical importance of thoughtfully considering sample size and the number of indicators included when estimating and interpreting LCA models. Findings of this dissertation provide evidence for a relatively strong interplay between sample size, number of indicators, and class enumeration in LCA models.

This applied study presented method illustrations of the regression analytic models for moderated mediation hypotheses using data from a survey program. In spite of the theoretical and empirical importance of employing appropriate models for analyzing moderated mediation hypotheses, or conditional indirect effects in social and behavioral science research, there have been limited examples of formally testing those effects in the literature of applied psychology or education mainly due to the lack of clear methods for examining these effects.

Therefore formal methods that were initially introduced and organized by Preacher, Rucker, & Hayes (2007), Hayes (2011), and Hayes (2013) were illustrated with the use of commercially available statistical software packages of IBM SPSS Statistics Version 23 (IBM, 1989-2015) and Mplus Version 6.11 (Muthen and Muthen, 1998-2011) using data from The Positive Body Image Survey (Romo, Mireles-Rios, & Nylund-Gibson, 2012). The focus population of the survey was a group of adolescent girls with the intentions to identify behavioral factors that contribute to their resilience in spite of sociocultural pressures that tend to produce negative feeling about their bodies, and to examine whether positive body perceptions can lead to the practice of health-promoting lifestyle.

Results from this study will help applied researchers using models for moderated mediation hypotheses, or conditional indirect effects as to how social or behavioral phenomena in substantive areas of the study can be better understood. Results from this dissertation highlight the importance of framing research questions about positive body image development among Latino adolescent girls in terms of path analytic models with protective factors specified as a predictor variable, positive body image as a mediator variable, and engagement in health-promoting behaviors as an outcome variable. Findings of this study provide evidence that this mediation models can be affected by moderator variables of interest.

Moderation analyses with a latent class variable allows researchers to study relations among exogenous (predictor or covariate) and endogenous (distal outcome) variables across the latent classes. Extending the latent class model to include auxiliary variables (both a predictor and an outcome) creates a context where the latent class variable serves as a moderating variable, referred to as a mixture regression. This thesis provides a pedagogical introduction on how to specify and interpret the moderation model with a latent class variable with the (ML) three-step manual approach (Asparouhov & Muthén, 2014) using data from the Longitudinal Survey of American Life (LSAL). Specifically, the latent class variable (science attitude classes) is hypothesized to moderate the relation between a predictor (science ability) and outcome (interest in science issues), controlling for the demographic variables. This thesis will outline the steps of the ML three-step approach, appropriate tables and visualizations used, and accompanying Mplus syntax.

The characterization of cultures as independent-interdependent and individualistic-collectivistic has provided the foundation for a tremendous amount of research which has revealed the strong influence of culture on psychological processes. Recent years have witnessed an effort by researchers to develop finer-grained distinctions within these overarching cultural dimensions. In line with this work, and inspired by the theoretical model laid out by Campos and Kim (2017), which distinguished between Harmony Collectivism, more common among East Asian populations, and Convivial Collectivism, more common among Latin American populations, the present research was conducted to develop and validate the Harmony and Convivial Collectivism Scale (HCCS). A preliminary pilot study involved a survey in Mechanical Turk (N = 97) and several rounds of expert focus groups. Study 2 (N = 220) used exploratory factor analysis (EFA) with a large sample of undergraduates and found best fit for a three-factor, 13-item model (Harmony, Convivial-Recognition, Convivial-Display). Study 3 (N = 330) confirmed the 3-factor structure through confirmatory factor analysis (CFA) and demonstrated adequate validity and reliability. Additionally, Study 3 used the entire data set to examine cultural mean differences on the factors between Asian-Americans, Latino-Americans, and European-Americans.

Latent transition analysis (LTA) is a mixture modeling approach that is gaining popularity in social science, behavioral, and health research. LTA is a longitudinal method that can be used to investigate how individuals transition from one latent, or unobserved class, to another over time. Although LTA is gaining use in many disciplines, to date only two studies have examined the statistical power of this statistical approach. The present study aims to examine how sample size and model characteristics such as latent transition probabilities, model definition, item-response probabilities, and class size influence the statistical power of to detect effects in latent transition probabilities. Meta-analysis findings were used to guide conditions ultimately used in this Monte Carlo simulation study. All data were generated using Mplus (Muthén & Muthén, 1998-2014).

Results from this study revealed how larger sample sizes, larger transition probabilities and class sizes were more likely to have greater power. Results also highlighted the importance of a well-defined measurement model with high class separation and homogeneous classes and its influence on statistical power. Findings from this dissertation provide evidence on which conditions tend to have higher or lower power. Additionally, findings show how poor conditions can have model convergence issues and provide misleading results due to "artificially high" power values. This study also includes practical recommendations and suggestions for future directions.

For decades researchers have observed the relationship between college students’ social integration, and college persistence and other student success outcomes (Cabrera, Castaneda, Nora, & Hengstler, 1992; Tinto, 1993). With social media having become such an important component of college students’ social lives, many new possibilities to engage students through these media have emerged in recent years. Studies show both that colleges are increasingly utilizing these tools (NACAC, 2009), and that students who use them demonstrate higher levels of engagement (Valenzuela, Park, & Kee, 2009), which could ultimately lead to improved student success outcomes. Transfer students are a particularly vulnerable population, having consistently been shown to struggle more with social integration than their peers who enter institutions as freshmen (Bauer & Bauer, 1994; Rhine, Milligan, & Nelson, 2000; Laanan, 2007). They, therefore, might stand to benefit from the engagement opportunities offered by social media, which can be utilized before students ever set foot on campus, thereby easing the initial transition, and positioning students for both short- and long-term success. However, little information exists on the relationship between students’ participation in social media and student success. This study examines that relationship for transfer students admitted to a major public research university. Two sets of analyses look at whether students who participate in the university’s social media community are more likely to matriculate, and whether they exhibit better student success outcomes. The first set of analyses provide some evidence that social media networking significantly influences the matriculation decisions of students who choose to participate, β = .323, p < .001. While the second set of analyses reveal no significant differences for student success outcomes, both graduation rates and GPAs are slightly higher within the treatment group than the control group, a promising result that may indicate further study is warranted.

This study examines the interplay between school experiences, mental health, and suicidality among Black adolescents, analyzing secondary data from the CDC's 2021 Adolescent Behavior and Experiences Survey (ABES) (Rico et al., 2022). Employing Latent Class Analysis and statistical mixture modeling, three classes emerged: 'Low,' 'Medium,' and 'High Exposure to Racial Discrimination'—each with distinct patterns of negative experiences related to their educational journey. Key indicators included physical and emotional abuse during COVID-19 'school at home,' cognitive vulnerabilities, hopelessness, and mental health challenges. The findings highlight the severe impact of systemic inequities and pervasive racial tensions on the mental health and suicidality of Black adolescents, shedding light on the troubling realities of their developmental experiences. The study underscores the urgent need for culturally competent educational reforms and the establishment of anti-racist school climates that promote psychological safety and foster healthy cognitive development. School culture-changing initiatives are critical for accelerating educational attainment, providing equitable opportunities, and bridging historical divides. The paper concludes with a call to the education community to examine the critical consciousness and psychological safety of their practices and spaces. Black Adolescent Student Experiences are contextually complex adolescent student experiences, historically driven by societal “race feelings,” often presenting as inescapable and persistent hopelessness. To gain a more in-depth understanding of the growing vulnerability of Black adolescent students in America, there is a need for future research that utilizes larger datasets across disciplines to exponentially accelerate the cognitive development of Black adolescents through protective ‘no harm’ school experiences, fostering positive emotional experiences, ensuring psychological safety at school, thereby informing the radical reformation of the Black Adolescent Student Experience, immediately. This study concludes with a call to the education research community to lead the way in fortifying the psychological resilience of all adolescents, thereby enhancing their cognitive capabilities for educational attainment.Keywords: school experiences, mental health, suicidality, Black adolescents, ABES, CDC, Latent Class Analysis, racial discrimination, systemic inequities, educational reform, anti-racist education, cognitive development, interdisciplinary research, longitudinal studies, diversity.