Small Sample Meta-Analyses
Caspar J. Van Lissa
Abstract
Meta-analyses often suffer from two related problems: A small sample of studies, and many between-studies differences that might influence the effect size. Power is typically too low to adequately account for these between-study differences using meta-regression. Researchers risk overfitting: Capturing noise in the data, rather than true effects. This chapter introduces MetaForest: A machine-learning-based approach for identifying relevant moderators in meta-analysis. MetaForest is robust to overfitting, handles many moderators, and captures non-linear effects and higher-order interactions. This chapter discusses the problems with small samples and many moderators, introduces MetaForest as a small sample solution, and provides a tutorial example analysis.