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A Bayesian Perspective on the Reproducibility Project: Psychology

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  • Alexander Etz
  • Joachim Vandekerckhove

Abstract

We revisit the results of the recent Reproducibility Project: Psychology by the Open Science Collaboration. We compute Bayes factors—a quantity that can be used to express comparative evidence for an hypothesis but also for the null hypothesis—for a large subset (N = 72) of the original papers and their corresponding replication attempts. In our computation, we take into account the likely scenario that publication bias had distorted the originally published results. Overall, 75% of studies gave qualitatively similar results in terms of the amount of evidence provided. However, the evidence was often weak (i.e., Bayes factor

Suggested Citation

  • Alexander Etz & Joachim Vandekerckhove, 2016. "A Bayesian Perspective on the Reproducibility Project: Psychology," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-12, February.
    • Handle: RePEc:plo:pone00:0149794
    DOI: 10.1371/journal.pone.0149794
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    Cited by:

    1. Minh-Hoang Nguyen & Tam-Tri Le & Hong-Kong To Nguyen & Manh-Toan Ho & Huyen T. Thanh Nguyen & Quan-Hoang Vuong, 2021. "Alice in Suicideland: Exploring the Suicidal Ideation Mechanism through the Sense of Connectedness and Help-Seeking Behaviors," IJERPH, MDPI, vol. 18(7), pages 1-24, April.
    2. Larry V. Hedges & Jacob M. Schauer, 2019. "More Than One Replication Study Is Needed for Unambiguous Tests of Replication," Journal of Educational and Behavioral Statistics, , vol. 44(5), pages 543-570, October.
    3. Zenker, Frank & Witte, Erich H., 2021. "The case for default point-H1-hypotheses: a theory-construction perspective," OSF Preprints zue4h, Center for Open Science.
    4. Samuel Pawel & Leonhard Held, 2022. "The sceptical Bayes factor for the assessment of replication success," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 879-911, July.
    5. Adler, Susanne Jana & Sharma, Pratyush Nidhi & Radomir, Lăcrămioara, 2023. "Toward open science in PLS-SEM: Assessing the state of the art and future perspectives," Journal of Business Research, Elsevier, vol. 169(C).
    6. 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.
    7. Valentine, Kathrene D & Buchanan, Erin Michelle & Scofield, John E. & Beauchamp, Marshall T., 2017. "Beyond p-values: Utilizing Multiple Estimates to Evaluate Evidence," OSF Preprints 9hp7y, Center for Open Science.
    8. Fanelli, Daniele, 2022. "The "Tau" of Science - How to Measure, Study, and Integrate Quantitative and Qualitative Knowledge," MetaArXiv 67sak, Center for Open Science.
    9. Fanelli, Daniele, 2020. "Metascientific reproducibility patterns revealed by informatic measure of knowledge," MetaArXiv 5vnhj, Center for Open Science.
    10. Larry V. Hedges & Jacob M. Schauer, 2021. "The design of replication studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 868-886, July.
    11. Robbie C M van Aert & Marcel A L M van Assen, 2017. "Bayesian evaluation of effect size after replicating an original study," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-23, April.
    12. Maya B. Mathur & Tyler J. VanderWeele, 2020. "New statistical metrics for multisite replication projects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1145-1166, June.
    13. Mathur, Maya B & VanderWeele, Tyler, 2018. "Statistical methods for evidence synthesis," Thesis Commons kd6ja, Center for Open Science.

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