The Impact of Effect Size Heterogeneity on Meta-Analysis: A Monte Carlo Experiment

In this paper we use Monte Carlo simulation to investigate the impact of effect size heterogeneity on the results of a meta-analysis. Specifically, we address the small sample behaviour of the OLS, the fixed effects regression and the mixed effects meta-estimators under three alternative scenarios of effect size heterogeneity. We distinguish heterogeneity in effect size variance, heterogeneity due to a varying true underlying effect across primary studies, and heterogeneity due to a non-systematic impact of omitted variable bias in primary studies. Our results show that the mixed effects estimator is to be preferred to the other two estimators in the first two situations. However, in the presence of random effect size variation due to a non-systematic impact of omitted variable bias, using the mixed effects estimator may be suboptimal. We also address the impact of sample size and show that meta-analysis sample size is far more effective in reducing meta-estimator variance and increasing the power of hypothesis testing than primary study sample size.