Mediation analyses are crucial for understanding the mechanisms through which interventions or theoretical constructs influence outcomes within nested structures, commonly found in education, psychology, and sociology. Recognizing the importance of these effects, designing and analyzing robust studies to detect them is essential. This dissertation…

Entity Resolution (ER) has been one of the bedrocks in the creation of information systems by ensuring ambiguous entities are identified and resolved by linking. One common design approach of traditional ER systems is to run in single-threaded mode, which makes the system prone to out-of-memory error when processing larger datasets. The Data…

Hierarchical data is often observed in education data. Analyzing such data with Multilevel Modeling becomes crucial to understanding the relationship at the individual and group levels. However, one of the most significant problems with this kind of data is small sample sizes and very low Intraclass Correlations. The multivariate Latent Covariate…

Multivariate count data is ubiquitous in many areas of research including the physical, biological, and social sciences. These data are traditionally modeled with the Dirichlet Multinomial distribution (DM). A new, more flexible Dirichlet-Tree Multinomial (DTM) model is gaining in popularity. Here, we consider Bayesian DTM regression models. Our…

This research study was conducted to determine if there was a difference in community college graduation rates based on aggregate faculty hires, aggregate faculty retention and community college size. The study employed a non-experimental quantitative research design with three independent variables (aggregate faculty new hires, aggregate faculty…

Meta-analytic Structural Equation Modeling (MASEM) is the combination of meta-analysis (MA) and structural equation modeling (SEM). With new MASEM methodologies developed over the past few years, there is an opportunity to compare the past approaches with the new ones. The purpose of this dissertation is two-fold. First, the parameter estimates,…

We consider Bayesian estimation of a hierarchical linear model (HLM) from small sample sizes. The continuous response Y and covariates C are partially observed and assumed missing at random. With C having linear effects, the HLM may be efficiently estimated by available methods. When C includes cluster-level covariates having interactive or other…

Over the last four decades, meta-analysis has proven to be a vital analysis strategy in educational research for synthesizing research findings from different studies. When synthesizing studies in a meta-analysis, it is common to assume that the true underlying effect varies from study to study, as studies will differ in design, participants,…

Cultural capital is a key term from the Social Reproduction Theory, and it is defined as the codes and practices that parents pass on to their children (Bourdieu, 1984). Previous empirical studies have confirmed the importance of cultural capital, and it is positively related to students' reading achievement (e.g., Kosutic, 2017; Noble &…

The main goal of learning analytics and early detection systems is to extract knowledge from student data to understand students' trends of activities towards success and risk and design intervention methods to improve learning performance and experience. However, many factors contribute to the challenge of designing and building effective…

This dissertation is composed of a study of estimation methods in classical and test theories and the elaboration and application of a cluster-robust variance estimator. Variance estimators derived from generalized estimating equations are known to be robust to most covariance structures and are therefore well suited for psychometric analysis of…

Clustering, a prevalent class of machine learning (ML) algorithms used in data mining and pattern-finding--has increasingly helped engineering education researchers and educators see and understand assessment patterns at scale. However, a challenge remains to make ML-enabled educational inferences that are useful and reliable for research or…

We study flexible Bayesian methods that are amenable to a wide range of learning problems involving complex high dimensional data structures, with minimal tuning. We consider parametric and semiparametric Bayesian models, that are applicable to both static and dynamic data, arising from a multitude of areas such as economics, finance and…

This dissertation consists of three chapters exploring the role that information plays in strategic situations. The first two chapters are devoted to modeling a decision maker's reasoning (knowledge, beliefs, and unawareness) about those of other decision makers in strategic environments. The third chapter, in turn, studies how information and…

School psychologists use intelligence and academic achievement test scores to understand individual differences in abilities that may be directed towards learning and the learning that has already occurred. To make ethical and effective decisions regarding individualized educational programs and interventions, test users must understand the…