A Two-Stage Approach to Synthesizing Covariance Matrices in Meta-Analytic Structural Equation Modeling

Structural equation modeling (SEM) is widely used as a statistical framework to test complex models in behavioral and social sciences. When the number of publications increases, there is a need to systematically synthesize them. Methodology of synthesizing findings in the context of SEM is known as meta-analytic SEM (MASEM). Although correlation matrices are usually preferred in MASEM, there are cases in which synthesizing covariance matrices is useful, especially when the scales of the measurement are comparable. This study extends the 2-stage SEM (TSSEM) approach proposed by M. W. L. Cheung and Chan (2005b) Cheung, M. W. L. and Chan, W. 2005b. Meta-analytic structural equation modeling: A two-stage approach. Psychological Methods, 10: 40–64. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar] to synthesizing covariance matrices in MASEM. A simulation study was conducted to compare the TSSEM approach with several approximate methods. An empirical example is used to illustrate the procedures and future directions for MASEM are discussed.