Meta-Analysis with JASP, Part II: Bayesian Approaches

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.

• Publication year

  2025

• Venue

  Unknown venue

• Publication date

  2025-09-11

• Fields of study

  Mathematics, Computer Science

• Identifiers
  arXiv 2509.09850
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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• Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews

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• A tutorial on Bayes factor estimation with the product space method

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• The Likelihood Principle

  2011cited by this paper
• An informed reference prior for between‐study heterogeneity in meta‐analyses of binary outcomes

  2011cited by this paper
• Bayesian data analysis.

  2010cited by this paper
• Methods to elicit beliefs for Bayesian priors: a systematic review.

  2010cited by this paper
• A re-evaluation of random-effects meta-analysis

  2009cited by this paper
• A practical solution to the pervasive problems ofp values

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• JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling

  2003cited by this paper
• Adjustment for Publication Bias and Quality Bias in Bayesian Meta-analysis

  2000cited by this paper
• Modeling publication bias using weighted distributions in a Bayesian framework

  1998cited by this paper
• Hierarchical Selection Models with Applications in Meta-Analysis

  1997cited by this paper
• Borrowing strength from external trials in a meta-analysis.

  1996cited by this paper
• A general linear model for estimating effect size in the presence of publication bias

  1995cited by this paper
• Estimating and Adjusting for Publication Bias Using Data Augmentation in Bayesian Meta-Analysis

  1995cited by this paper
• Bayes factors

  1995cited by this paper
• Stochastically dependent effect sizes.

  1994cited by this paper
• Inference from Iterative Simulation Using Multiple Sequences

  1992cited by this paper
• Synthesis of environmental evidence: nitrogen dioxide epidemiology studies.

  1992cited by this paper
• Robust Bayesian analysis : sensitivity to the prior

  1990cited by this paper
• An introduction to meta-analysis.

  1989cited by this paper
• Memoir on the Probability of the Causes of Events

  1986cited by this paper
• Student Ratings of Instruction and Student Achievement: A Meta-analysis of Multisection Validity Studies

  1981cited by this paper
• Specification Searches: Ad Hoc Inference with Nonexperimental Data

  1980cited by this paper
• A Further Look at Robustness via Bayes's Theorem

  1962cited by this paper
• Some Tests of Significance, Treated by the Theory of Probability

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