IDEAS home Printed from https://ideas.repec.org/p/cbt/econwp/25-07.html
   My bibliography  Save this paper

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

Author

Listed:

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differences in the number of studies and estimates per study, types of effect sizes used, and the prevalence of unpublished studies included in meta-analyses.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
    as

    Download full text from publisher

    File URL: https://repec.canterbury.ac.nz/cbt/econwp/2507.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    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. 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.
    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," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 51(1), pages 412-426.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
    2. repec:osf:metaar:3qp2w_v1 is not listed on IDEAS
    3. Jeffrey D. Michler & Anna Josephson, 2022. "Recent developments in inference: practicalities for applied economics," Chapters, in: A Modern Guide to Food Economics, chapter 11, pages 235-268, Edward Elgar Publishing.
    4. repec:osf:metaar:3qp2w_v2 is not listed on IDEAS
    5. James G. MacKinnon & Matthew D. Webb & Morten Ø. Nielsen, 2017. "Bootstrap And Asymptotic Inference With Multiway Clustering," Working Paper 1386, Economics Department, Queen's University.
    6. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2023. "Fast and reliable jackknife and bootstrap methods for cluster‐robust inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 671-694, August.
    7. Hirschauer, Norbert & Grüner, Sven & Mußhoff, Oliver & Becker, Claudia & Jantsch, Antje, 2020. "Can p-values be meaningfully interpreted without random sampling?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 14, pages 71-91.
    8. James G. MacKinnon, 2019. "How cluster‐robust inference is changing applied econometrics," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 52(3), pages 851-881, August.
    9. Heisig, Jan Paul & Matthewes, Sönke Hendrik, 2022. "No Evidence that Strict Educational Tracking Improves Student Performance through Classroom Homogeneity: A Critical Reanalysis of Esser and Seuring (2020) [Keine Belege für leistungsfördernde Effek," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 51(1), pages 99-111.
    10. Neimanns, Erik & Blossey, Nils, 2022. "From media-party linkages to ownership concentration causes of cross-national variation in media outlets' economic positioning," MPIfG Discussion Paper 22/8, Max Planck Institute for the Study of Societies.
    11. Harold D. Chiang & Kengo Kato & Yukun Ma & Yuya Sasaki, 2022. "Multiway Cluster Robust Double/Debiased Machine Learning," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1046-1056, June.
    12. Fernando Bruna & Juan Fernández‐Sastre, 2021. "Regional characteristics and the decision to innovate in a developing country: A multilevel analysis of Ecuadorian firms," Papers in Regional Science, Wiley Blackwell, vol. 100(6), pages 1337-1354, December.
    13. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Testing for the appropriate level of clustering in linear regression models," Journal of Econometrics, Elsevier, vol. 235(2), pages 2027-2056.
    14. Weiss, Amanda, 2024. "How Much Should We Trust Modern Difference-in-Differences Estimates?," OSF Preprints bqmws, Center for Open Science.
    15. Welz, Thilo & Viechtbauer, Wolfgang & Pauly, Markus, 2023. "Cluster-robust estimators for multivariate mixed-effects meta-regression," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    16. David Blazar & Blake Heller & Thomas J. Kane & Morgan Polikoff & Douglas O. Staiger & Scott Carrell & Dan Goldhaber & Douglas N. Harris & Rachel Hitch & Kristian L. Holden & Michal Kurlaender, 2020. "Curriculum Reform in The Common Core Era: Evaluating Elementary Math Textbooks Across Six U.S. States," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(4), pages 966-1019, September.
    17. Francis L. Huang, 2022. "Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations," Journal of Educational and Behavioral Statistics, , vol. 47(1), pages 101-125, February.
    18. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2023. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust," Stata Journal, StataCorp LLC, vol. 23(4), pages 942-982, December.
    19. Antoine A. Djogbenou & James G. MacKinnon & Morten Ø. Nielsen, 2017. "Validity Of Wild Bootstrap Inference With Clustered Errors," Working Paper 1383, Economics Department, Queen's University.
    20. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2021. "Wild Bootstrap and Asymptotic Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 505-519, March.
    21. McLaughlin, Darragh & McLaughlin, Eoin & Kenny, Sean, 2025. "Taking a punt: Monetary experimentation and the Irish macroeconomic crisis of 1955-56," QUCEH Working Paper Series 25-02, Queen's University Belfast, Queen's University Centre for Economic History.
    22. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2024. "Cluster-Robust Jackknife and Bootstrap Inference for Binary Response Models," Working Paper 1515, Economics Department, Queen's University.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cbt:econwp:25/07. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Albert Yee (email available below). General contact details of provider: https://edirc.repec.org/data/decannz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.