Bartoš, F.

Maier, M.

Quintana, D.S.

Wagenmakers, E.-J.

English

Meta-analyses are essential for cumulative science, but their validity can be compromised by publication bias. To mitigate the impact of publication bias, one may apply publication-bias-adjustment techniques such as precision-effect test and precision-effect estimate with standard errors (PET-PEESE) and selection models. These methods, implemented in JASP and R, 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 and 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, a Bayesian approach that simultaneously considers both PET-PEESE and selection models. We illustrate the methodology on an example data set, provide an instructional video (https://bit.ly/pubbias) and an R-markdown script (https://osf.io/uhaew/), and discuss the interpretation of the results. Finally, we include concrete guidance on reporting the meta-analytic results in an academic article.</p

International Migration, Integration and Social Cohesion online publications

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

https://pure.uva.nl/ws/files/98074442/25152459221109259.pdf

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

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International Migration, Integration and Social Cohesion online publications