Meta-Analyzing Structural Equation Models with Study-Level Moderators: Explaining the Systematic Heterogeneity Among Covariance Structures

Structural equation modeling (SEM) is a useful technique in the behavioral sciences, where latent variables and measurement errors are hypothesized to exist. To investigate a certain phenomenon of interest, different researchers may carry out different SEM studies and report (seemingly or not) different results. It is meaningful to synthesize their findings, so as to explain how and why a set of relationships differs in different situations and better understand the phenomenon of interest. The method to synthesize SEM studies is commonly referred to as meta-analytic SEM (MASEM). Currently, methods to perform MASEM are all limited to the fixed-effects context, where the assumption is made that all the SEM studies included in a meta-analysis have exactly the same population covariance matrix. However, this is an unrealistic assumption because different studies usually have their own characteristics and a set of relationships usually behave differently in different situations. A more reasonable method is one that acknowledges and models the heterogeneity among the SEM studies. In this dissertation, I propose an MASEM method that allows the meta-analyst to include categorical study-level moderators in the meta-analysis, so as to account for the systematic heterogeneity among the SEM studies.