Meta-Analysis with JASP, Part II: Bayesian Approaches

2025/09/11 by Frantis̆ek Bartos̆, Eric‐Jan Wagenmakers, Bartoš, František +1 · 1 citation
Decision Sciences · #Computation (stat.CO) #FOS: Computer and information sciences #G.3 #Meta-analysis and systematic reviews #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.2509.09850

openalex publication_date 2025/09/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Bayesian inference is on the rise, partly because it allows researchers to quantify parameter uncertainty, evaluate evidence for competing hypotheses, incorporate model ambiguity, and seamlessly update knowledge as information accumulates. All of these advantages apply to the meta-analytic settings; however, advanced Bayesian meta-analytic methodology is often restricted to researchers with programming experience. In order to make these tools available to a wider audience, we implemented state-of-the-art Bayesian meta-analysis methods in the Meta-Analysis module of JASP, a free and open-source statistical software package (https://jasp-stats.org/). The module allows researchers to conduct Bayesian estimation, hypothesis testing, and model averaging with models such as meta-regression, multilevel meta-analysis, and publication bias adjusted meta-analysis. Results can be interpreted using forest plots, bubble plots, and estimated marginal means. This manuscript provides an overview of the Bayesian meta-analysis tools available in JASP and demonstrates how the software enables researchers of all technical backgrounds to perform advanced Bayesian meta-analysis.

Citations

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Cited by

• Meta-Analysis with JASP, Part I: Classical Approaches

Related

• Meta-Analysis with JASP, Part I: Classical Approaches
• The JASP guidelines for conducting and reporting a Bayesian analysis
• A meta-analyst should make informed decisions: Issues with Bayesian model-averaging meta-analyses
• Robust Bayesian meta‐analysis: Model‐averaging across complementary publication bias adjustment methods
• Bayesian methods in meta-analysis and evidence synthesis