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

Doctor of Philosophy, The Ohio State University, 2005, Psychology

Previous research shows psychological stressors to correspond to decreased functioning of NK and T lymphocyte, immune cells which may affect cancer progression. Using data from a longitudinal study of breast cancer patients, we tested whether individual differences in level and change in the psychological stress response correspond to individual differences in level and change in NK and T lymphocyte function. Latent curve analyses suggested an inverse relationship between levels of T lymphocyte blastogenesis and levels of perceived stress and distress. This finding may indicate that stress and distress are related to T lymphocyte blastogenesis through consistent individual differences. Results also suggested an inverse relationship between change in perceived stress and change in NK lymphocyte function over the 18-month follow-up period. These data are consistent with the hypothesis that stressors affect immune function through psychological reactions to stress.

Committee: Barbara Andersen (Advisor) Subjects: Psychology, Clinical

Master of Science (MS), Bowling Green State University, 2012, Applied Statistics (ASOR)

Latent Curve Analysis (LCA) is a statistical technique used for longitudinal studies that combines the methods of factor analysis with linear models. The purpose of LCA is to allow the modeler to combine common variables into a smaller number of random variables. The random variables are then regressed linearly to allow the modeler to express how the variables change over time. Due to the complexity of LCA, a researcher is forced to choose between a one growth curve model, or spend countless hours attempted to locate a better model. The purpose of this study is to create general methods that will allow future researchers the ability to create more adequate LCA models than the one growth curve model in a timelier manner. The data used for the thesis came from a longitudinal study dated 2000 to 2004. The study was a positive orientation survey that measured seven characteristics of positive orientation for 45 males and 81 female participants over three time periods. Using the program LISREL, a one growth curve LCA model was first analyzed by a group of researchers named Guido Alessandri, Gian Vittorio Caprara, and John Tisak. Using a variety approaches, a total four techniques were created in effort to solve the problem. Using a chi square test, two of the techniques resulted in models that were statistically significantly better than the one LCA model. These two models are discussed in detail within the paper. Though only two of the four techniques produced a better model than the one growth curve LCA model, each of the four methods could prove helpful for future researchers attempting to solve this problem.

Committee: Nancy Boudreau Dr (Advisor); John Tisak Dr (Committee Chair); Maria Rizzo Dr (Committee Chair) Subjects: Statistics