Meta-analysis of social science research: A practitioner's guide - Zuzana Havránková; Chris Doucouliagos; Tomáš Havránek; Tom D. Stanley (Charles University in Prague, Institute of Economic Studies (IES), 2023)
This paper provides concise, nontechnical, step-by-step guidelines on how to conduct a modern meta-analysis, especially in social sciences. We treat publication bias, p-hacking, and heterogeneity as phenomena meta-analysts must always confront. To this end, we provide concrete methodological recommendations. Meta-analysis methods have advanced notably over the last few years. Yet many meta-analyses still rely on outdated approaches, some ignoring publication bias and systematic heterogeneity. While limitations persist, recently developed techniques allow robust inference even in the face of formidable problems in the underlying empirical literature. The purpose of this paper is to summarize the state of the art in a way accessible to aspiring meta-analysts in any field. We also discuss how meta-analysts can use advances in artificial intelligence to work more efficiently.
Keywords: A14; B49; C83; 330; meta-analysis; publication bias; p-hacking; artificial intelligence; model uncertainty