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Bias and Efficiency of Meta-Analytic Variance Estimators in the Random-Effects Model

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  • Wolfgang Viechtbauer

Abstract

The meta-analytic random effects model assumes that the variability in effect size estimates drawn from a set of studies can be decomposed into two parts: heterogeneity due to random population effects and sampling variance. In this context, the usual goal is to estimate the central tendency and the amount of heterogeneity in the population effect sizes. The amount of heterogeneity in a set of effect sizes has implications regarding the interpretation of the meta-analytic findings and often serves as an indicator for the presence of potential moderator variables. Five population heterogeneity estimators were compared in this article analytically and via Monte Carlo simulations with respect to their bias and efficiency.

Suggested Citation

  • Wolfgang Viechtbauer, 2005. "Bias and Efficiency of Meta-Analytic Variance Estimators in the Random-Effects Model," Journal of Educational and Behavioral Statistics, , vol. 30(3), pages 261-293, September.
    • Handle: RePEc:sae:jedbes:v:30:y:2005:i:3:p:261-293
    DOI: 10.3102/10769986030003261
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    Cited by:

    1. Christopher Hansen & Joern Block & Matthias Neuenkirch, 2020. "Family Firm Performance Over The Business Cycle: A Meta‐Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 476-511, July.
    2. Marco Colagrossi & Domenico Rossignoli & Mario A. Maggioni, 2017. "Does democracy cause growth? A meta-analysis perspective," DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo dis1703, Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS).
    3. Mathur, Maya B & VanderWeele, Tyler, 2019. "Sensitivity analysis for publication bias in meta-analyses," OSF Preprints s9dp6, Center for Open Science.
    4. Andrew L. Rukhin, 2013. "Estimating heterogeneity variance in meta-analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 451-469, June.
    5. Rutherford, Matthew W. & O’Boyle, Ernest H. & Miao, Chao & Goering, Daniel & Coombs, Joseph E., 2017. "Do response rates matter in entrepreneurship research?," Journal of Business Venturing Insights, Elsevier, vol. 8(C), pages 93-98.
    6. Maria-Francisca Cespedes-Lopez & Raul-Tomas Mora-Garcia & V. Raul Perez-Sanchez & Juan-Carlos Perez-Sanchez, 2019. "Meta-Analysis of Price Premiums in Housing with Energy Performance Certificates (EPC)," Sustainability, MDPI, vol. 11(22), pages 1-59, November.
    7. Verver, Hugo & van Zelst, Marino & Lucas, Gerardus Johannes Maria & Meeus, Marius, 2019. "Understanding Heterogeneity in the Performance Feedback – Organizational Responsiveness Relationship: A Meta-Analysis," OSF Preprints hq4uw, Center for Open Science.
    8. Bakbergenuly, Ilyas & Hoaglin, David C. & Kulinskaya, Elena, 2020. "Simulation study of estimating between-study variance and overall effect in meta-analyses of log-response-ratio for normal data," MetaArXiv 3bnxs, Center for Open Science.

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