Lifespan psychological perspectives have long suggested the context in which individuals live having the potential to shape the course of development across the adult lifespan. Thus, it is imperative to examine the role of both the objective and subjective neighborhood context in mitigating the consequences of lifetime adversity on mental and physical health. To address the research questions, data was used from a sample of 362 individuals in midlife who were assessed on lifetime adversity, multiple outcomes of mental and physical health and aspects of the objective and subjective neighborhood. Results showed that reporting more lifetime adversity was associated with poorer mental and physical health. Aspects of the objective and subjective neighborhood, such as green spaces moderated these relationships. The discussion focuses on potential mechanisms underlying why objective and subjective indicators of the neighborhood are protective against lifetime adversity.

Statistical inference from mediation analysis applies to populations, however, researchers and clinicians may be interested in making inference to individual clients or small, localized groups of people. Person-oriented approaches focus on the differences between people, or latent groups of people, to ask how individuals differ across variables, and can help researchers avoid ecological fallacies when making inferences about individuals. Traditional variable-oriented mediation assumes the population undergoes a homogenous reaction to the mediating process. However, mediation is also described as an intra-individual process where each person passes from a predictor, through a mediator, to an outcome (Collins, Graham, & Flaherty, 1998). Configural frequency mediation is a person-oriented analysis of contingency tables that has not been well-studied or implemented since its introduction in the literature (von Eye, Mair, & Mun, 2010; von Eye, Mun, & Mair, 2009). The purpose of this study is to describe CFM and investigate its statistical properties while comparing it to traditional and casual inference mediation methods. The results of this study show that joint significance mediation tests results in better Type I error rates but limit the person-oriented interpretations of CFM. Although the estimator for logistic regression and causal mediation are different, they both perform well in terms of Type I error and power, although the causal estimator had higher bias than expected, which is discussed in the limitations section.

Previous research has shown functional mixed-effects models and traditional mixed-effects models perform similarly when recovering mean and individual trajectories (Fine, Suk, & Grimm, 2019). However, Fine et al. (2019) showed traditional mixed-effects models were able to more accurately recover the underlying mean curves compared to functional mixed-effects models. That project generated data following a parametric structure. This paper extended previous work and aimed to compare nonlinear mixed-effects models and functional mixed-effects models on their ability to recover underlying trajectories which were generated from an inherently nonparametric process. This paper introduces readers to nonlinear mixed-effects models and functional mixed-effects models. A simulation study is then presented where the mean and random effects structure of the simulated data were generated using B-splines. The accuracy of recovered curves was examined under various conditions including sample size, number of time points per curve, and measurement design. Results showed the functional mixed-effects models recovered the underlying mean curve more accurately than the nonlinear mixed-effects models. In general, the functional mixed-effects models recovered the underlying individual curves more accurately than the nonlinear mixed-effects models. Progesterone cycle data from Brumback and Rice (1998) were then analyzed to demonstrate the utility of both models. Both models were shown to perform similarly when analyzing the progesterone data.

To make meaningful comparisons on a construct of interest across groups or over time, measurement invariance needs to exist for at least a subset of the observed variables that define the construct. Often, chi-square difference tests are used to test for measurement invariance. However, these statistics are affected by sample size such that larger sample sizes are associated with a greater prevalence of significant tests. Thus, using other measures of non-invariance to aid in the decision process would be beneficial. For this dissertation project, I proposed four new effect size measures of measurement non-invariance and analyzed a Monte Carlo simulation study to evaluate their properties and behavior in addition to the properties and behavior of an already existing effect size measure of non-invariance. The effect size measures were evaluated based on bias, variability, and consistency. Additionally, the factors that affected the value of the effect size measures were analyzed. All studied effect sizes were consistent, but three were biased under certain conditions. Further work is needed to establish benchmarks for the unbiased effect sizes.

Data from 749 Mexican-origin families across a seven-year span was used to test a model of the processes that moderate and mediate the transmission of religious values from parent to child. There were four separate reports of parenting practices (mother-report, father-report, adolescent’s report on mother, and adolescents report on father) and models were tested separately based on each report. Results suggest the mother’s role was more influential than fathers in transmitting religious values to their child, across parent and adolescent-report. In addition, results revealed different, and opposing effects for mother’s self-report of parenting practices and adolescents report on mother’s parenting behavior. Adolescents’ perceptions of maternal acceptance and consistency increased the likelihood of adolescents maintaining their religious values across adolescence, whereas mothers’ self-reported parenting practices negatively predicted late adolescents’ religious values. Lastly, results of this study lend support for the differential role of mothers in fathers in the development of adolescents’ social competence, specifically in the context of their religious values and use of positive parenting practices. The findings highlight the unique contributions of each reports’ perceptions in studying the transmission of religious values in families, as well, as the distinct role of mothers and fathers in the development of adolescents’ social competence.

A record number of Latino students are enrolling in higher education in the U.S., but as a group Latinos are the least likely to complete a bachelor’s degree. Cultural factors theoretically contribute to Latino students’ success, including orientation toward ethnic heritage and mainstream cultures (i.e., dual cultural adaptation), feeling comfortable navigating two cultural contexts (i.e., biculturalism), and the degree of fit between students’ cultural backgrounds and the cultural landscapes of educational institutions (i.e., cultural congruity). In a two-part study, these cultural factors were examined in relation to the hypothalamic-pituitary-adrenal (HPA) axis stress response (indexed by salivary cortisol), a physiological mechanism that may underlie how psychosocial stress influences academic achievement and health. First, Latino students’ cortisol responses to stress were estimated in their daily lives prior to college using ecological momentary assessment (N = 206; 64.6% female; Mage = 18.10). Results from three-level growth models indicated that cortisol levels were lower following greater perceived stress than usual for students endorsing greater Latino cultural values (e.g., familism), compared to students endorsing average or below-average levels of these values. Second, cortisol and subjective responses to a standard public speaking stress task were examined in a subsample of these same students in their first semester of college (N = 84; 63.1% female). In an experimental design, viewing a brief video prior to the stress task conveying the university’s commitment to cultural diversity and inclusion (compared to a generic campus tour) reduced cortisol reactivity and negative affect for students with greater Latino cultural values, and also reduced post-task cortisol levels for students with greater mainstream U.S. cultural values (e.g., competition). These findings join the growing science of culture and biology interplay, while also informing initiatives to support first-year Latino students and the universities that serve them.

The goal of diagnostic assessment is to discriminate between groups. In many cases, a binary decision is made conditional on a cut score from a continuous scale. Psychometric methods can improve assessment by modeling a latent variable using item response theory (IRT), and IRT scores can subsequently be used to determine a cut score using receiver operating characteristic (ROC) curves. Psychometric methods provide reliable and interpretable scores, but the prediction of the diagnosis is not the primary product of the measurement process. In contrast, machine learning methods, such as regularization or binary recursive partitioning, can build a model from the assessment items to predict the probability of diagnosis. Machine learning predicts the diagnosis directly, but does not provide an inferential framework to explain why item responses are related to the diagnosis. It remains unclear whether psychometric and machine learning methods have comparable accuracy or if one method is preferable in some situations. In this study, Monte Carlo simulation methods were used to compare psychometric and machine learning methods on diagnostic classification accuracy. Results suggest that classification accuracy of psychometric models depends on the diagnostic-test correlation and prevalence of diagnosis. Also, machine learning methods that reduce prediction error have inflated specificity and very low sensitivity compared to the data-generating model, especially when prevalence is low. Finally, machine learning methods that use ROC curves to determine probability thresholds have comparable classification accuracy to the psychometric models as sample size, number of items, and number of item categories increase. Therefore, results suggest that machine learning models could provide a viable alternative for classification in diagnostic assessments. Strengths and limitations for each of the methods are discussed, and future directions are considered.

Research has consistently shown that gay/lesbian/bisexual (GLB) or sexual minority youth are at an increased risk for adverse outcomes resulting from the stress caused by continual exposure to negative events (e.g., victimization, discrimination). The present study used a nationally representative sample of adolescents to test mechanisms that may be responsible for the differences in offending behaviors among sexual minority and heterosexual adolescents. Specifically, this study tested whether bisexual adolescents received less maternal support than did heterosexual adolescents because of their sexual orientation, thus increasing the likelihood that they run away from home. This study then examined whether the greater likelihood that bisexual adolescents running away would lead to them committing a significantly higher variety of income-based offenses, but not a significantly higher variety of aggression-based offenses. This study tested the hypothesized mediation model using two separate indicators of sexual orientation measured at two different time points, modeled outcomes in two ways, as well as estimated the models separately for boys and girls. Structural equation modeling was used to test the hypothesized direct and indirect relations. Results showed support for maternal support and running away mediating the relations between sexual orientation and offending behaviors for the model predicting the likelihood of committing either an aggressive or an income offense, but only for girls who identified as bisexual in early adulthood. Results did not support these relations for the other models, suggesting that bisexual females have unique needs when it comes to prevention and intervention. Results also highlight the need for a greater understanding of sexual orientation measurement methodology.

Understanding how adherence affects outcomes is crucial when developing and assigning interventions. However, interventions are often evaluated by conducting randomized experiments and estimating intent-to-treat effects, which ignore actual treatment received. Dose-response effects can supplement intent-to-treat effects when participants are offered the full dose but many only receive a partial dose due to nonadherence. Using these data, we can estimate the magnitude of the treatment effect at different levels of adherence, which serve as a proxy for different levels of treatment. In this dissertation, I conducted Monte Carlo simulations to evaluate when linear dose-response effects can be accurately and precisely estimated in randomized experiments comparing a no-treatment control condition to a treatment condition with partial adherence. Specifically, I evaluated the performance of confounder adjustment and instrumental variable methods when their assumptions were met (Study 1) and when their assumptions were violated (Study 2). In Study 1, the confounder adjustment and instrumental variable methods provided unbiased estimates of the dose-response effect across sample sizes (200, 500, 2,000) and adherence distributions (uniform, right skewed, left skewed). The adherence distribution affected power for the instrumental variable method. In Study 2, the confounder adjustment method provided unbiased or minimally biased estimates of the dose-response effect under no or weak (but not moderate or strong) unobserved confounding. The instrumental variable method provided extremely biased estimates of the dose-response effect under violations of the exclusion restriction (no direct effect of treatment assignment on the outcome), though less severe violations of the exclusion restriction should be investigated.

Time-to-event analysis or equivalently, survival analysis deals with two variables simultaneously: when (time information) an event occurs and whether an event occurrence is observed or not during the observation period (censoring information). In behavioral and social sciences, the event of interest usually does not lead to a terminal state such as death. Other outcomes after the event can be collected and thus, the survival variable can be considered as a predictor as well as an outcome in a study. One example of a case where the survival variable serves as a predictor as well as an outcome is a survival-mediator model. In a single survival-mediator model an independent variable, X predicts a survival variable, M which in turn, predicts a continuous outcome, Y. The survival-mediator model consists of two regression equations: X predicting M (M-regression), and M and X simultaneously predicting Y (Y-regression). To estimate the regression coefficients of the survival-mediator model, Cox regression is used for the M-regression. Ordinary least squares regression is used for the Y-regression using complete case analysis assuming censored data in M are missing completely at random so that the Y-regression is unbiased. In this dissertation research, different measures for the indirect effect were proposed and a simulation study was conducted to compare performance of different indirect effect test methods. Bias-corrected bootstrapping produced high Type I error rates as well as low parameter coverage rates in some conditions. In contrast, the Sobel test produced low Type I error rates as well as high parameter coverage rates in some conditions. The bootstrap of the natural indirect effect produced low Type I error and low statistical power when the censoring proportion was non-zero. Percentile bootstrapping, distribution of the product and the joint-significance test showed best performance. Statistical analysis of the survival-mediator model is discussed. Two indirect effect measures, the ab-product and the natural indirect effect are compared and discussed. Limitations and future directions of the simulation study are discussed. Last, interpretation of the survival-mediator model for a made-up empirical data set is provided to clarify the meaning of the quantities in the survival-mediator model.