Doctor of Philosophy, The Ohio State University, 2024, Psychology

Current longitudinal research in developmental science has shown that during college, students generally experience a progressive decline in psychosocial adjustment. First-generation college students (FGCS) encounter unique adjustment difficulties that impact their academic success. Yet, no current research tracks these students' progress based on key factors (i.e., socioemotional competence, psychosocial adjustment, and perceived academic control) that are critical to academic success. To fill this research gap, this short-term longitudinal study is the first to examine how these variables affect academic outcomes among emerging adult college freshmen at two timepoints, as they take one of their first college courses during their first semester of college. Findings show that the associations between most of these study variables are statistically significant and indicative of unique differences between FGCS and continuing-generation college students (CGCS) as academic learners. Perceived academic control was the only key predictor of academic performance. Despite reporting higher levels of psychological distress than CGCS across both timepoints, FGCS only showed significant differences in depression levels by the end of their first semester of college. Academic enrichment program participation did not impact academic performance. These study findings are used to expand FGCS research and offer practitioners relevant insights to support these students' positive adjustment to college upon entry.

Committee: Stephen Petrill (Committee Chair); Kelly Purtell (Committee Member); Sarah Schoppe-Sullivan (Committee Member) Subjects: Developmental Psychology; Education; Educational Psychology; Higher Education; Higher Education Administration; Psychology

Doctor of Philosophy, The Ohio State University, 2015, Biostatistics

When conducting research synthesis, the collection of studies that will be combined often do not measure the same set of variables, which creates missing data. When the studies to combine are longitudinal, missing data can occur on either the observation-level (time-varying) or the subject-level (non-time-varying). Traditionally, the focus of missing data methods for longitudinal data has been on missing observation-level variables. In this dissertation, we focus on missing subject-level variables where few methods have been developed or compared. We compare two multiple imputation approaches that have been proposed for missing subject-level data in single longitudinal studies: a joint modeling approach and a sequential conditional modeling approach. Based on analytical and empirical results for the case when all variables are normally distributed, we find the joint modeling approach to be preferable to the sequential conditional approach except when the covariance structure of the repeated outcome for each individual has homogenous variance and exchangeable correlation. Specifically, the regression coefficient estimates from an analysis incorporating imputed values based on the sequential conditional method are attenuated and less efficient than those from the joint method. Based on this preference, we develop a new joint model for multiple imputation of missing subject-level variables that models subject- and observation-level variables with distributions in the exponential family. Our model is built within the generalized linear models framework and uses normally distributed latent variables to account for dependence on both the subject- and observation-levels. When compared via simulation, the performance of our model is similar to or better than existing approaches for imputing missing subject-level variables with normal, Bernoulli, Poisson, and multinomial distributions. We illustrate our method by applying it to combine two longitudinal studies on (open full item for complete abstract)

Committee: Rebbeca Andridge (Advisor); Eloise Kaizar (Advisor); Bo Lu (Committee Member) Subjects: Biostatistics; Statistics

Doctor of Philosophy (PhD), Ohio University, 2013, Communication Studies (Communication)

Prominent among the many methodological challenges communication research faces are the relative lack of longitudinal research conducted in the discipline and the threats to validity that arise from the complex instrumentation necessary for inquiry into human interaction. This dissertation presented planned missing data designs (PMDs) as solutions to these challenges because PMDs can make research less burdensome, cheaper, faster, and more valid. Three studies illustrate the use of PMDs in communication research. Study one was a controlled-enrollment PMD investigation of the relationship between students' public speaking anxiety and communication competence in a semester-long longitudinal study. By using the controlled-enrollment design, this study had five measurement waves, but each participant was measured at no more than three measurement waves. Results indicated that the controlled-enrollment design was effective at minimizing participant loss due to attrition and reducing the risk of testing effects due to repeated measurements. Study two was an efficiency-type PMD replication of Infante and Wigley's (1986) verbal aggressiveness scale validation study, in which each participant was presented with only 95 items from the 147 item survey instrument. Through the use of an efficiency design, this study was able to replicate the results of the original study with a dramatically reduced time burden on the participants, indicating that efficiency-type PMDs are an effective tool for scale shortening. Study three was an accelerated longitudinal PMD replication of Rubin, Graham, and Mignerey's (1990) longitudinal communication competence study, which measured change in students' communication competence over the course of a college career. Through the use of an accelerated longitudinal PMD, data collection was completed in just over one calendar year, far shorter than the three years the original study took to collect data. A flaw in participant retention proced (open full item for complete abstract)

Committee: Amy Chadwick PhD (Advisor) Subjects: Communication