Evaluating the Efficacy of Meta-Analytic Structural Equation Modeling Approaches With Missing Correlations

• In Event: 25.026 - Challenges in Meta-Analysis

Abstract

Missing correlations often happen in the primary correlation matrices and create major problems for the performance of meta-analytic structural equation modeling (MASEM). However, methodological investigation regarding the performance of MASEM methods paired with a large proportion of missing data is limited in literature. This study was designed to investigate the impacts of missing conditions on the MASEM performance, utilizing weighted-covariance (W-COV) GLS with pairwise deletion (PD) or multiple imputation (MI), and two-stage SEM (TSSEM) to pool the correlation matrices. Moreover, the outcomes demonstrated the necessity of including at least one study with full correlation matrices in the study pool for TSSEM and W-COV GLS with PD.

Authors

• Jing Lv, East China Normal University

• Yukiko Maeda, Purdue University