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What Can We Learn From 1000 Meta-Analyses Across 10 Different Disciplines?

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Abstract

This study describes and analyzes 1000 meta-analyses across ten different disciplines spanning medicine, science, and the social sciences. It highlights significant variations in methodology across disciplines and offers targeted recommendations for enhancing research synthesis. Our analysis reveals discipline-specific differences in the number of studies and estimates per study, types of effect sizes used, and the prevalence of unpublished studies included in meta-analyses. It also examines the extent of effect heterogeneity and the employment of meta-regression to explain this heterogeneity across different fields. Our findings underscore the underutilization of robust meta-analytic methods like three-level models and CR2 clustered standard errors, which are crucial for addressing dependencies among multiple estimates within studies. Finally, we discuss the implications of publication bias and the prevalence of various tests and corrections across disciplines. Our recommendations aim to learn from and apply best practices across all disciplines. This work serves as a resource for researchers conducting their first meta-analyses, as a benchmark for researchers designing simulation experiments, and as a reference for applied meta-analysts aiming to improve their methodological practices.

Suggested Citation

  • Weilun Wu & Jianhua Duan & W. Robert Reed & Elizabeth Tipton, 2025. "What Can We Learn From 1000 Meta-Analyses Across 10 Different Disciplines?," Working Papers in Economics 25/07, University of Canterbury, Department of Economics and Finance.
    • Handle: RePEc:cbt:econwp:25/07
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    1. James G. MacKinnon, 2019. "How cluster-robust inference is changing applied econometrics," Canadian Journal of Economics, Canadian Economics Association, vol. 52(3), pages 851-881, August.
    2. Elizabeth Tipton & James E. Pustejovsky, 2015. "Small-Sample Adjustments for Tests of Moderators and Model Fit Using Robust Variance Estimation in Meta-Regression," Journal of Educational and Behavioral Statistics, , vol. 40(6), pages 604-634, December.
    3. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    4. James E. Pustejovsky & Elizabeth Tipton, 2018. "Small-Sample Methods for Cluster-Robust Variance Estimation and Hypothesis Testing in Fixed Effects Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 672-683, October.
    5. Elff, Martin & Heisig, Jan Paul & Schaeffer, Merlin & Shikano, Susumu, 2021. "Multilevel Analysis with Few Clusters: Improving Likelihood-based Methods to Provide Unbiased Estimates and Accurate Inference," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 51(1), pages 412-426.
    6. Elff, Martin & Heisig, Jan Paul & Schaeffer, Merlin & Shikano, Susumu, 2021. "Multilevel Analysis with Few Clusters: Improving Likelihood-Based Methods to Provide Unbiased Estimates and Accurate Inference," British Journal of Political Science, Cambridge University Press, vol. 51(1), pages 412-426, January.
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    More about this item

    Keywords

    Meta-Analysis; Interdisciplinary comparison; Effect size heterogeneity; Tests for publication bias; Clustering; Meta-analytic estimators; Meta-regression; Statistical software;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology

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