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

0562627 - ÚI 2023 RIV US eng J - Journal Article
Bartoš, František - Maier, M. - Quintana, D. S. - Wagenmakers, J. E. … Total 5 authors
Adjusting for Publication Bias in JASP and R: Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis.
Advances in Methods and Practices in Psychological Science. Roč. 5, č. 3 (2022), s. 1-19. ISSN 2515-2459
Institutional support: RVO:67985807
Keywords : selection models * PET-PEESE * robust Bayesian meta-analysis * model averaging * publication bias
OECD category: Statistics and probability
Impact factor: 13.6, year: 2022
Method of publishing: Open access
https://dx.doi.org/10.1177/25152459221109259

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.
Permanent Link: https://hdl.handle.net/11104/0334899


Research data: CRAN (RoBMA), CRAN (Weightr)