Meta analytical structural equation modeling: comments on issues with current methods and viable alternatives

Dealing With Dependent Effect Sizes in MASEM

Abstract. The objective of the present study was to examine whether different methods for dealing with dependency in meta-analytic structural equation modeling (MASEM) lead to different results. Four different methods for dealing with dependent effect sizes in MASEM were applied to empirical data, including: (1) ignoring dependency; (2) aggregation; (3) elimination; and (4) a multilevel approach. Random-effects two-stage structural equation modeling was conducted for each method separately, and potential moderators were examined using subgroup analysis. Results demonstrated that the different methods of dealing with dependency in MASEM lead to different results. Thus, the decision on which approach should be used in MASEM-analysis should be carefully considered. Given that the multilevel approach is the only approach that includes all available information while explicitly modeling dependency, it is currently the theoretically preferred approach for dealing with dependency in MASEM. Future research should evaluate the multilevel approach with simulated data.

Some reflections on combining meta-analysis and structural equation modeling

Meta-analysis and structural equation modeling (SEM) are 2 of the most prominent statistical techniques employed in the behavioral, medical, and social sciences. They each have their own well-established research communities, terminologies, statistical models, software packages, and journals (Research Synthesis Methods and Structural Equation Modeling: A Multidisciplinary Journal). In this paper, I will provide some personal reflections on combining meta-analysis and SEM in the forms of meta-analytic SEM and SEM-based meta-analysis. The critical contributions of Becker (1992), Shadish (1992), and Viswesvaran and Ones (1995) in the early development of meta-analytic SEM are highlighted. Another goal of the paper is to illustrate how meta-analysis can be extended and integrated with other techniques to address new research questions such as the analysis of Big Data. I hope that this paper may stimulate more research development in the area of combining meta-analysis and SEM.