Meta-Analytic Structural Equation Modeling (MASEM)

Meta-analytic Structural Equation Modeling (MASEM) has drawn interest from many researchers recently. In doing MASEM, researchers usually first synthesize correlation matrices across studies using meta-analysis techniques and then analyze the pooled correlation matrix using structural equation modeling techniques. Several multivariate methods of MASEM have been proposed by the researchers. In this dissertation, I compared the commonly used multivariate methods for meta-analytic path modeling. Specifically, I examined the Generalized Least Squares (GLS) method (Becker, 1992; Becker & Schram, 1994) and the Two-Stage Structural Equation Modeling (TSSEM) method (Cheung, 2002; Cheung & Chan, 2005) using both simulation studies and real data analyses. Both the traditional GLS approach (Becker, 1992) and the modified GLS approaches (Becker & Fahrbach, 1994) were applied and compared with the TSSEM approach. Fixed-effects data and random-effects data were generated to see how these approaches differ at the first and second stages of MASEM. The results shows that the modified GLS approach performs as well as or better than the TSSEM approach in both the first step of synthesizing correlation matrices and the second step estimation of the parameters and standard errors, using both fixed-effects data and random-effects data. The original GLS approach only performs well when the within-study sample size is large enough (of the simulation situations in this dissertation, n ). Both the modified GLS approach and the TSSEM approach produce equivalent parameter estimates across all conditions. However, the standard errors from the TSSEM approach seem to be over-estimates under certain conditions. Overall, both the modified GLS and TSSEM approaches are appropriate for conducting meta-analytic path modeling and the difference in parameter estimates is minimal.