Doctor of Philosophy, The Ohio State University, 2018, Psychology

Conditional process models are commonly used in many areas of psychology research as well as research in other academic fields (e.g., marketing, communication, and education). Conditional process models combine mediation analysis and moderation analysis. Mediation analysis, sometimes called process analysis, investigates if an independent variable influences an outcome variable through a specific intermediary variable, sometimes called a mediator. Moderation analysis investigates if the relationship between two variables depends on another. Conditional process models are very popular because they allow us to better understand how the processes we are interested in might vary depending on characteristics of different individuals, situations, and other moderating variables. Methodological developments in conditional process analysis have primarily focused on the analysis of data collected using between-subjects experimental designs or cross-sectional designs. However, another very common design is the two-instance repeated-measures design. A two-instance repeated-measures design is one where each subject is measured twice; once in each of two instances. In the analysis discussed in this dissertation, the factor that differentiates the two repeated measurements is the independent variable of interest. Research on how to statistically test mediation, moderation, and conditional process models in these designs has been minimal. Judd, Kenny, and McClelland (2001) introduced a piecewise method for testing for mediation, reminiscent of the Baron and Kenny causal steps approach for between-participant designs. Montoya and Hayes (2017) took this piecewise approach and translated it to a path-analytic approach, allowing for a quantification of the indirect effect, more sophisticated methods of inference, and the extension to multiple mediator models. Moderation analysis in these designs has been described by Judd, McClelland, and Smith (1996), Judd et al. (2001), and Montoya (open full item for complete abstract)

Committee: Andrew Hayes (Advisor); Jolynn Pek (Committee Member); Paul De Boeck (Committee Member) Subjects: Applied Mathematics; Behavioral Sciences; Biostatistics; Experimental Psychology; Psychology; Quantitative Psychology; Statistics

Doctor of Philosophy, Case Western Reserve University, 2016, Epidemiology and Biostatistics

This is a study to evaluate cross-sectional and longitudinal genetic associations among Metabolic Syndrome (MetS) risk factors and a select set of candidate genes involved in inflammatory, vasoconstrictive, and coagulation processes at the vascular epithelium in the Women's Interagency HIV Study (WIHS) Cohort. We conducted a candidate gene association analysis of multiple clinical measures for each component trait of MetS in a group of HIV-positive and -negative women of the WIHS. Thirty-two candidate genes were selected based on their pro-inflammatory, pro-vasoconstrictive, and pro-coagulative functions and expression in the vascular endothelium. The association was modeled with mixed effects model with both random slope and random intercepts. We have identified genetic variants in CKD14 and NFKB1 with longitudinal effects on MetS risk factors in African Americans and Hispanics. We provide evidence that the genetic architecture of MetS includes genes previously implicated in inflammation (NFKB1) and vessel repair (CDK14) and that HIV may mediate the magnitude of the some genetic associations.

Committee: Xiaofeng Zhu Ph.D. (Committee Chair); Robert Elston Ph.D. (Committee Member); Nathan Morris Ph.D. (Committee Member); Nora Nock Ph.D. (Committee Member); Barbara Gripshover M.D. (Committee Member); Bradely Aoiuzerat Ph.D. (Committee Member) Subjects: Epidemiology; Genetics

Doctor of Philosophy (PhD), Ohio University, 2006, Educational Research and Evaluation (Education)

In longitudinal studies, both repeated measures (RM) and hierarchical linear model (HLM) can be applied. Yet, it is not clearly determined which of RM and HLM should be applied in the balanced design of a longitudinal study. The purpose of this study was to explore the interrelation of HLM and RM in their theoretical development and to compare the two approaches to the evaluation of fixed effects in the balanced design of longitudinal data analysis. This was done through a Monte Carlo (MC) study of the empirical power of HLM and RM in three fixed effect tests: Two-group treatment effect, time effect and treatment-by-time interaction. In this research, RM included traditional RM (TRM) and updated multivariate RM (UMRM). Two covariance structures examined under UMRM were UN and AR(1). Specifically, this paper compared the power of HLM, AR(1), UN and TRM in the three fixed effect tests within three factors: Effect sizes, sample sizes and G matrices. The results indicated that HLM, AR(1) and UN had similar power patterns but different from TRM. TRM was significantly more powerful than the other three in the treatment and time tests, but significantly less powerful than the three in the interaction test. Among HLM, AR(1) and UN, UN power seemed to rank the highest, AR(1) the second, and HLM the lowest, in all three tests under defined conditions. Nevertheless, the pairwise power differences of HLM, AR(1) and UN are not all significant. Additionally, findings from Type I error rates, bootstrap bias, standard errors, confidence intervals and model fit statistics (AIC and BIC) were examined for the four models. Limitations, conclusions, recommendations for future study were also provided.

Committee: Gordon Brooks (Advisor) Subjects: Statistics