A comparison of meta-methods for synthesizing indirect effects

Synthesizing findings about the indirect (mediated) effect plays an important role in determining the mechanism through which variables affect one another. This simulation study compared six methods for synthesizing indirect effects: correlation-based MASEM, parameter-based MASEM, marginal likelihood synthesis, an adjustment to marginal likelihood synthesis, and univariate, and two-parameter sequential Bayesian methods. This paper provides an empirical example and code for using all methods compared in the simulation study. The methods were compared on (relative) bias, precision, and RMSE of the point estimates and the power, coverage, and type I error rates of the interval estimates. The factors in the simulation were the methods, the strength of the indirect effect, the measurement level of the independent variable, and the number of studies available for synthesis. Correlation-based MASEM had the lowest bias out of all methods and produced interval estimates with the best statistical properties. The precision of the point estimates and the RMSE was marginally different across methods. Marginal likelihood synthesis had the highest power but performed poorly in terms of coverage and type I error rates. The adjusted marginal likelihood synthesis and two-parameter sequential Bayesian methods performed adequately in terms of bias and power, and the adjusted marginal likelihood synthesis had higher power than the sequential Bayesian method. Correlation-based MASEM performed best out of the six methods. Guidelines for optimal practices when synthesizing indirect effects (eg, required number of studies, type of results reported) are provided, as well as suggestions for further methodological research.