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Evaluating meta-analysis as a replication success measure

Author

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

    (University of Groningen)

Abstract

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. 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.

Suggested Citation

  • Muradchanian, Jasmine & Hoekstra, Rink & Kiers, Henk & van Ravenzwaaij, Don, 2023. "Evaluating meta-analysis as a replication success measure," MetaArXiv ax825, Center for Open Science.
    • Handle: RePEc:osf:metaar:ax825
    DOI: 10.31219/osf.io/ax825
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    4. Colin F. Camerer & Anna Dreber & Felix Holzmeister & Teck-Hua Ho & Jürgen Huber & Magnus Johannesson & Michael Kirchler & Gideon Nave & Brian A. Nosek & Thomas Pfeiffer & Adam Altmejd & Nick Buttrick , 2018. "Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015," Nature Human Behaviour, Nature, vol. 2(9), pages 637-644, September.
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