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Predicting the replicability of social and behavioural science claims in COVID-19 preprints

Author

Listed:
  • Alexandru Marcoci

    (University of Cambridge
    University of Nottingham)

  • David P. Wilkinson

    (University of Melbourne
    University of Melbourne)

  • Ans Vercammen

    (University of Melbourne
    The University of Queensland
    Curtin University)

  • Bonnie C. Wintle

    (University of Melbourne)

  • Anna Lou Abatayo

    (Wageningen University and Research)

  • Ernest Baskin

    (Saint Joseph’s University)

  • Henk Berkman

    (University of Auckland)

  • Erin M. Buchanan

    (Harrisburg University of Science and Technology)

  • Sara Capitán

    (Swedish University of Agricultural Sciences)

  • Tabaré Capitán

    (Swedish University of Agricultural Sciences)

  • Ginny Chan

    (Rhizom Psychological Services LLC)

  • Kent Jason G. Cheng

    (The Pennsylvania State University)

  • Tom Coupé

    (University of Canterbury)

  • Sarah Dryhurst

    (University of Cambridge
    University of Cambridge
    University College London)

  • Jianhua Duan

    (Statistics New Zealand)

  • John E. Edlund

    (Rochester Institute of Technology)

  • Timothy M. Errington

    (Center for Open Science)

  • Anna Fedor

    (Independent researcher)

  • Fiona Fidler

    (University of Melbourne)

  • James G. Field

    (West Virginia University)

  • Nicholas Fox

    (Center for Open Science)

  • Hannah Fraser

    (University of Melbourne)

  • Alexandra L. J. Freeman

    (University of Cambridge)

  • Anca Hanea

    (University of Melbourne
    University of Melbourne)

  • Felix Holzmeister

    (University of Innsbruck)

  • Sanghyun Hong

    (University of Canterbury)

  • Raquel Huggins

    (Harrisburg University of Science and Technology)

  • Nick Huntington-Klein

    (Seattle University)

  • Magnus Johannesson

    (Stockholm School of Economics)

  • Angela M. Jones

    (Texas State University)

  • Hansika Kapoor

    (Monk Prayogshala
    University of Connecticut)

  • John Kerr

    (University of Cambridge
    University of Otago)

  • Melissa Kline Struhl

    (Massachusetts Institute of Technology)

  • Marta Kołczyńska

    (Polish Academy of Sciences)

  • Yang Liu

    (University of California, Santa Cruz)

  • Zachary Loomas

    (Center for Open Science)

  • Brianna Luis

    (Center for Open Science)

  • Esteban Méndez

    (Central Bank of Costa Rica)

  • Olivia Miske

    (Center for Open Science)

  • Fallon Mody

    (University of Melbourne
    University of Melbourne)

  • Carolin Nast

    (University of Stavanger, School of Business and Law)

  • Brian A. Nosek

    (Center for Open Science
    University of Virginia)

  • E. Simon Parsons

    (Center for Open Science)

  • Thomas Pfeiffer

    (Massey University)

  • W. Robert Reed

    (University of Canterbury)

  • Jon Roozenbeek

    (University of Cambridge)

  • Alexa R. Schlyfestone

    (Harrisburg University of Science and Technology)

  • Claudia R. Schneider

    (University of Cambridge
    University of Cambridge
    University of Canterbury)

  • Andrew Soh

    (University of Hawaii at Manoa)

  • Zhongchen Song

    (New Zealand Institute of Economic Research (NZIER))

  • Anirudh Tagat

    (Monk Prayogshala)

  • Melba Tutor

    (Independent researcher)

  • Andrew H. Tyner

    (Center for Open Science)

  • Karolina Urbanska

    (Independent researcher)

  • Sander Linden

    (University of Cambridge)

Abstract

Replications are important for assessing the reliability of published findings. However, they are costly, and it is infeasible to replicate everything. Accurate, fast, lower-cost alternatives such as eliciting predictions could accelerate assessment for rapid policy implementation in a crisis and help guide a more efficient allocation of scarce replication resources. We elicited judgements from participants on 100 claims from preprints about an emerging area of research (COVID-19 pandemic) using an interactive structured elicitation protocol, and we conducted 29 new high-powered replications. After interacting with their peers, participant groups with lower task expertise (‘beginners’) updated their estimates and confidence in their judgements significantly more than groups with greater task expertise (‘experienced’). For experienced individuals, the average accuracy was 0.57 (95% CI: [0.53, 0.61]) after interaction, and they correctly classified 61% of claims; beginners’ average accuracy was 0.58 (95% CI: [0.54, 0.62]), correctly classifying 69% of claims. The difference in accuracy between groups was not statistically significant and their judgements on the full set of claims were correlated (r(98) = 0.48, P

Suggested Citation

  • Alexandru Marcoci & David P. Wilkinson & Ans Vercammen & Bonnie C. Wintle & Anna Lou Abatayo & Ernest Baskin & Henk Berkman & Erin M. Buchanan & Sara Capitán & Tabaré Capitán & Ginny Chan & Kent Jason, 2025. "Predicting the replicability of social and behavioural science claims in COVID-19 preprints," Nature Human Behaviour, Nature, vol. 9(2), pages 287-304, February.
    • Handle: RePEc:nat:nathum:v:9:y:2025:i:2:d:10.1038_s41562-024-01961-1
    DOI: 10.1038/s41562-024-01961-1
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