Meta-Analytic Structural Equation Modeling With Moderating Effects on SEM Parameters

Meta-analytic structural equation modeling (MASEM) is an increasingly popular meta-analytic technique that combines the strengths of meta-analysis and structural equation modeling. MASEM facilitates the evaluation of complete theoretical models (e.g., path models or factor analytic models), accounts for sampling covariance between effect sizes, and provides measures of overall fit of the hypothesized model on meta-analytic data. We propose a novel MASEM method, one-stage MASEM, which is better suitable to explain study-level heterogeneity than existing methods. One-stage MASEM allows researchers to incorporate continuous or categorical moderators into the MASEM, in which any parameter in the structural equation model (e.g., path coefficients and factor loadings) can be modeled by the moderator variable, while the method does not require complete data for the primary studies included in the meta-analysis. We illustrate the new method on two real data sets, evaluate its empirical performance via a computer simulation study, and provide user-friendly R-functions and annotated syntax to assist researchers in applying one-stage MASEM. We close the article by presenting several future research directions. Translational Abstract Meta-analytic structural equation modeling (MASEM) is an increasingly popular statistical technique that combines the strengths of meta-analysis and structural equation modeling. Meta-analysis is useful in combining results from different studies, whereas structural equation modeling allows researchers to test different theoretical models. MASEM facilitates the evaluation of complete theoretical models and tests how good the proposed models fit the published data. Because published studies may be different in terms of samples and measurements, the findings are likely heterogeneous, that is, nonidentical. We propose a novel MASEM method, one-stage MASEM, which is better suitable to explain study-level heterogeneity than existing methods. One-stage MASEM allows researchers to use continuous or categorical moderators, for example, the mean age of the participants and gender ratio, to explain the differences across studies with potential missing data. We illustrate the new method on two real data sets, and provide user-friendly R-functions and annotated syntax to assist researchers in applying one-stage MASEM. We close the paper by presenting several future research directions.