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Medientyp:
E-Artikel
Titel:
Adjusting for Publication Bias in JASP and R: Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis
Beteiligte:
Bartoš, František;
Maier, Maximilian;
Quintana, Daniel S.;
Wagenmakers, Eric-Jan
Erschienen:
SAGE Publications, 2022
Erschienen in:Advances in Methods and Practices in Psychological Science
Sprache:
Englisch
DOI:
10.1177/25152459221109259
ISSN:
2515-2459;
2515-2467
Entstehung:
Anmerkungen:
Beschreibung:
<jats:p> 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. </jats:p>