Meta-Analysis with JASP, Part II: Bayesian Approaches

2025-09-11

AI Summary

Bayesian meta-analysis remains inaccessible to researchers without programming expertise, hindering its adoption in empirical social sciences. Method: We developed and integrated the first user-oriented Bayesian meta-analysis module into the open-source statistical platform JASP. The module implements core Bayesian techniques—including estimation, hypothesis testing, model averaging, meta-regression, multilevel modeling, and publication bias adjustment—via an intuitive graphical user interface, modular workflow, and built-in visualizations (e.g., forest plots, bubble plots, marginal means plots). Contribution/Results: Officially released and empirically validated, the module substantially improves efficiency and reproducibility of rigorous cumulative evidence synthesis for non-technical users. It lowers methodological barriers, promotes standardized application of Bayesian methods in social science research, and advances open, transparent meta-analytic practice.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Bayesian Learning

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Web data visualization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Making Bayesian meta-analysis accessible without programming
• Implementing advanced Bayesian methods in JASP software
• Enabling evidence evaluation and uncertainty quantification for researchers

Innovation

• Bayesian meta-analysis in JASP
• Free open-source software implementation
• Advanced methods without programming required