Doctor of Philosophy, The Ohio State University, 2023, Human Ecology: Human Development and Family Science

Clear associations exist between the intersections of socioeconomic factors, relationship processes, and relationship outcomes. Though romantic relationships are predictive of positive outcomes across multiple life domains (i.e., mental health, physical health, financial health, relational health), maintaining a satisfying romantic relationship can be challenging for partners of low-income statuses given systemically induced stressors. Not only is this population growing, but these families continue to navigate economic, health, and intervention disparities. Previous relationship intervention and prevention efforts have largely produced little-to-no sustainable gains for couples of lowincome statuses, despite the need and potential benefits of services for this population. Scholars posit that the ineffectiveness of these interventions is due, in part, to the lack of client-driven and tailored interventions, as previous initiatives were directly transferred from middle- and higher-income participants. Building a strong foundation of basic science is essential for working towards accessible, sustainable, and effective evidence-based interventions for couples of low income statuses. In addition, the area of relationship maintenance continues to be integral to relational satisfaction and commitment; however, this area is understudied in terms of how maintenance associates with relationship outcomes across different levels of socioeconomic factors. As such, the aims of this dissertation were two-fold: 1) Investigate the longitudinal associations between relationship maintenance behaviors, socioeconomic factors, and relationship satisfaction; 2) Explore latent profiles of dyadic maintenance behavior use and their associations with socioeconomic factors, relationship satisfaction, and commitment using actor and partner data. Data were drawn from the German Family Panel Analysis of Intimate Relationships and Family Dynamics (pairfam). For Aim 1, associ (open full item for complete abstract)

Committee: Suzanne Bartle-Haring PhD (Advisor); Keeley Pratt PhD (Committee Member); Ashley Landers PhD (Committee Member); Arya Ansari PhD (Committee Member) Subjects: Economics; Families and Family Life; Personal Relationships; Quantitative Psychology; Soil Sciences

PHD, Kent State University, 2021, College of Education, Health and Human Services / School of Lifespan Development and Educational Sciences

The purpose of this study was to investigate the relationship among self-efficacy, self advocacy, and multicultural counseling competency of school counselors currently practicing in the US. The research questions guided this study included (a) What are the direct and indirect influences of school counselor self-efficacy on multicultural counseling competence? (b) Is the relationship between self-efficacy and multicultural counseling competence mediated by self-advocacy for school counselors? The data were collected from 306 school counselors practicing in the US. Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were used in the data analysis in the study. The results suggested self-efficacy significantly predicted multicultural counseling competence among the US school counselors. The results of the hypothesized structural model also suggested that self-advocacy had a strong indirect effect on multicultural counseling competence mediated by self-efficacy. The results of the data analysis, discussions of the findings, implications of the current study, and limitations and future research directions are presented herein.

Committee: Jason McGlothlin DR (Committee Chair); Martin Jencius DR (Committee Member); Kelly Cichy DR (Committee Member) Subjects: Counseling Education; School Counseling

Doctor of Philosophy, The Ohio State University, 2019, Psychology

Multilevel structural equation modeling (MSEM) is an emerging statistical framework for the analysis of hierarchically structured data, such as data corresponding to students nested within classrooms or repeated measurements nested within individuals. The MSEM framework provides several advantages over the traditional multilevel modeling (MLM) and structural equation modeling (SEM) frameworks, including the ability to model multivariate responses, level-2 response variables, measurement error via factor models, and structural relations (e.g., regressions) among the random effects/latent variables. Although several formulations of the MSEM have been presented (see, e.g., Liang & Bentler, 2004; Rabe-Hesketh, Skrondal, & Pickles, 2004; Mehta & Neale, 2005), the framework of B. Muthen and Asparouhov (2008) as implemented in Mplus (L. K. Muthen & Muthen, 2017) has the advantage that the relationship between lower-level (i.e., level-1) latent variables can be modeled as randomly varying across upper-level (i.e., level-2) units. Unfortunately, maximum likelihood (ML) estimation of the parameters for such models, as implemented in Mplus, is computationally demanding due to the likelihood function having to be approximated, as the function cannot be computed in closed-form. Mplus numerically integrates over all of the random effects/latent variables using quadrature-based methods. This approach is not feasible for high-dimensional latent variable models, which reduces the potential models that can practically be fit. In this dissertation, I develop a more computationally efficient and accurate ML estimation routine for MSEMs with random slopes for latent variables. The method relies on a reformulation of the likelihood function so that some of the integrals can be computed analytically, reducing the dimension of numerical integration required. Specifically, only the random slopes for latent variables need to be numerically integrated, as the integrals corresponding to the ot (open full item for complete abstract)

Committee: Andrew Hayes Ph.D. (Advisor); Paul De Boeck Ph.D. (Committee Member); Jolynn Pek Ph.D. (Committee Member) Subjects: Education; Educational Tests and Measurements; Psychology; Quantitative Psychology; Statistics