Evaluation of the Efficacy of Meta-Analytic Structural Equation Modeling with Missing Correlations

Abstract

Researchers often face problems with missing correlations when applying meta-analytic structural equation modeling (MASEM). However, methodological investigation regarding the performance of MASEM paired with a large proportion of missingness is limited. This study investigated the impacts of missing conditions on the performance of correlation-based MASEM, utilizing weighted-covariance generalized least squares (W-COV GLS) with pairwise deletion (PD) and/or multiple imputation (MI), and two-stage structural equation modeling (TSSEM), with two factorial models. The results show the superiority of W-COV GLS with MI and the necessity of including studies with complete correlation matrices for TSSEM and W-COV GLS with PD. We identified the boundary conditions in which each approach performs satisfactorily and illustrated how the variation of factor loadings impacts the performance of correlation-based MASEM.

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