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Evaluating meta-analysis as a replication success measure

Author

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  • Jasmine Muradchanian
  • Rink Hoekstra
  • Henk Kiers
  • Don van Ravenzwaaij

Abstract

Background: The importance of replication in the social and behavioural sciences has been emphasized for decades. Various frequentist and Bayesian approaches have been proposed to qualify a replication study as successful or unsuccessful. One of them is meta-analysis. The focus of the present study is on the way meta-analysis functions as a replication success metric. To investigate this, original and replication studies that are part of two large-scale replication projects were used. For each original study, the probability of replication success was calculated using meta-analysis under different assumptions of the underlying population effect when replication results were unknown. The accuracy of the predicted overall replication success was evaluated once replication results became available using adjusted Brier scores. Results: Our results showed that meta-analysis performed poorly when used as a replication success metric. In many cases, quantifying replication success using meta-analysis resulted in the conclusion where the replication was deemed a success regardless of the results of the replication study. Discussion: We conclude that when using meta-analysis as a replication success metric, it has a relatively high probability of finding evidence in favour of a non-zero population effect even when it is zero. This behaviour largely results from the significance of the original study. Furthermore, we argue that there are fundamental reasons against using meta-analysis as a metric for replication success.

Suggested Citation

  • Jasmine Muradchanian & Rink Hoekstra & Henk Kiers & Don van Ravenzwaaij, 2024. "Evaluating meta-analysis as a replication success measure," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-18, December.
    • Handle: RePEc:plo:pone00:0308495
    DOI: 10.1371/journal.pone.0308495
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    References listed on IDEAS

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    1. T. D. Stanley, 2005. "Beyond Publication Bias," Journal of Economic Surveys, Wiley Blackwell, vol. 19(3), pages 309-345, July.
    2. Hanea, Anca & Wilkinson, David Peter & McBride, Marissa & Lyon, Aidan & van Ravenzwaaij, Don & Singleton Thorn, Felix & Gray, Charles T. & Mandel, David R. & Willcox, Aaron & Gould, Elliot, 2021. "Mathematically aggregating experts' predictions of possible futures," MetaArXiv rxmh7, Center for Open Science.
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    4. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
    5. A M Hanea & D P Wilkinson & M McBride & A Lyon & D van Ravenzwaaij & F Singleton Thorn & C Gray & D R Mandel & A Willcox & E Gould & E T Smith & F Mody & M Bush & F Fidler & H Fraser & B C Wintle, 2021. "Mathematically aggregating experts’ predictions of possible futures," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-24, September.
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