abstract: Longitudinal recursive partitioning (LRP) is a tree-based method for longitudinal data. It takes a sample of individuals that were each measured repeatedly across time, and it splits them based on a set of covariates such that individuals with similar trajectories become grouped together into nodes. LRP does this by fitting a mixed-effects model to each node every time that it becomes partitioned and extracting the deviance, which is the measure of node purity. LRP is implemented using the classification and regression tree algorithm, which suffers from a variable selection bias and does not guarantee reaching a global optimum. Additionally, fitting mixed-effects models to each potential split only to extract the deviance and discard the rest of the information is a computationally intensive procedure. Therefore, in this dissertation, I address the high computational demand, variable selection bias, and local optimum solution. I propose three approximation methods that reduce the computational demand of LRP, and at the same time, allow for a straightforward extension to recursive partitioning algorithms that do not have a variable selection bias and can reach the global optimum solution. In the three proposed approximations, a mixed-effects model is fit to the full data, and the growth curve coefficients for each individual are extracted. Then, (1) a principal component analysis is fit to the set of coefficients and the principal component score is extracted for each individual, (2) a one-factor model is fit to the coefficients and the factor score is extracted, or (3) the coefficients are summed. The three methods result in each individual having a single score that represents the growth curve trajectory. Therefore, now that the outcome is a single score for each individual, any tree-based method may be used for partitioning the data and group the individuals together. Once the individuals are assigned to their final nodes, a mixed-effects model is fit to each terminal node with the individuals belonging to it. I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data. / Dissertation/Thesis / Doctoral Dissertation Psychology 2019

Recent research has identified important functional differences between Prescriptive morality (based in approach motivation) and Proscriptive morality (based in avoidance motivation). The purpose of the present research was to understand the consequences of these moralities applied at the group level for social categorization, especially in response to threat. I measured social categorization with a novel method in which participants categorized same-race and cross-race morphed faces. Social Justice (which is Prescriptive morality applied to the group) was associated with more inclusive social categorization under conditions of threat compared to a control condition. Social Order (which is Proscriptive morality applied to the group) was not associated with social categorization. The implications of this work for social categorization, politics, and our understanding of moral diversity are discussed.

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Self-control has been measured using a variety of methods including self-report measures, cognitive inhibition tasks, delay discounting and delay of gratification tasks, and persistence and willpower tasks. Although these are all theoretically linked to processes involved in self-control, recent evidence has shown that these diverse measurement techniques relate only minimally to one another. Assuming that self-control is a reflective construct, this would indicate that many of these tasks are poor indicators of self-control. The present research challenges the common assumption that self-control is a reflective construct and instead proposes that self-control is a formative construct. Conceptualizing self-control as a formative construct could reconcile some of the inconsistencies in the literature, in particular the fact that many indicators for self-control do not correlate highly. To examine the possibility of a formative model of self-control, this research examines 13 commonly used measures of self-control and investigates indicator intercorrelations, indicator relationships with the theoretical consequences of self-control, and performs a vanishing tetrad test (Bollen & Ting, 2000). Results show that in general, indicator intercorrelations are low and nonsignificant as well as indictor correlations with theorized construct consequences. The results of the vanishing tetrad test suggest a reflective interpretation of self-control, but concerns with uniformly low covariances between indicators limit the interpretation of this test.

Underachievement in postsecondary education has been a growing concern for educators and researchers. Higher education institutions have implemented early alert systems to identify students performing below standards. This early warning system has major limitations that confine it to an identification only approach. The current study aimed to investigate the psychometric properties of the Student Attitude Assessment Survey-R (SAAS-R) in a postsecondary sample. First, a confirmatory factor analysis validated the SAAS-R in a postsecondary sample. Predictive validity was then investigated by identifying students as underachieving or achieving based on responses to the SAAS-R and via a regression-based discrepancy model (ability vs. achievement). Then, the SAAS-R was compared to the discrepancy model to see whether the SAAS-R is an accurate assessment for identification of achievement. Tests of convergent validity included comparison of the SAAS-R subscales to well established similar constructs. Discriminant validity was checked by comparing the SAAS-R to a Social Desirability Scale. Finally, measurement invariance was explored to see if the SAAS-R would measure across groups. The SAAS-R demonstrated strong evidence of structural, convergent, and discriminant validity, yet limited evidence of predictive validity. Assessment of measurement invariance across self-identified traditional and non-traditional students yielded no evidence of measurement invariance. Initial psychometric properties support extension of the structural model of the SAAS-R to postsecondary students and the convergent validity utility of the SAAS-R subscales. However, more research is needed before the SAAS-R can be applied as an assessment of underachievement in postsecondary education.

Recent increases in opioid overdose rates have changed the role of first responders on the front lines of this national crisis. The present study used a semi-structured qualitative interview to investigate how the increase in opioids, opioid-related harm, and opioid-related death within Tennessee has affected the first responder population. Law enforcement officers, firefighters, and paramedics (N = 30) from rural-serving counties in Tennessee completed a semi-structured interview. Eight themes emerged from the interviews: (1) mental health symptoms, including posttraumatic stress disorder and secondary traumatic stress symptoms; (2) coping behaviors; (3) available resources; (4) barriers to accessing resources; (5) recommendations for what is needed; (6) hardest circumstances; (7) discrepant thoughts and feelings; (8) perception of role in reducing the impact of the epidemic. This study provides novel insights into the impact of the opioid epidemic on Tennessee first responders, and can inform future efforts to reduce adverse outcomes in these care providers.