Adjusting for Publication Bias in JASP & R — Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis


Meta-analyses are essential for cumulative science, but their validity can be compromised by publication bias. In order to mitigate the impact of publication bias, one may apply publication bias adjustment techniques such as PET-PEESE and selection models. Implemented in JASP & R, these methods allow researchers without programming experience to conduct state-of-the-art publication bias adjusted meta-analysis. In this tutorial, we demonstrate how to conduct a publication bias adjusted meta-analysis in JASP & R and interpret the results. First, we explain two frequentist bias correction methods: PET-PEESE and selection models. Second, we introduce robust Bayesian meta-analysis (RoBMA), a Bayesian approach that simultaneously considers both PET-PEESE and selection models. We illustrate the methodology on an example data set, provide instructional video (, R-markdown script (, and discuss the interpretation of the results. Finally, we include concrete guidance on reporting the meta-analytic results in an academic article.