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Authors and reviewers who suffer from confirmatory bias

Author

Listed:
  • J. A. García

    (CITIC-UGR, Universidad de Granada)

  • Rosa Rodriguez-Sánchez

    (CITIC-UGR, Universidad de Granada)

  • J. Fdez-Valdivia

    (CITIC-UGR, Universidad de Granada)

Abstract

Confirmatory bias induces overconfidence in the sense that people believe more strongly than they should in their preferred hypotheses. This work describes a Bayesian-based formal model to study the effect of overconfidence about the causes of manuscript rejection due to confirmatory bias in peer review. In addition, we also present an online tool that helps authors to study their beliefs about the causes of rejection. This tool takes the authors’ self-evaluated probability of misinterpretation (i.e. confirmatory bias) and self-evaluated probability of perceiving a review signal correlated with the true cause of rejection and a sequence of review signals perceived as input, and gives a prediction of whether there is overconfidence and wrongness in the author’s belief that bias in peer review caused rejection. We continue to discuss the effect of confirmatory bias in the editor-reviewer relationship in peer review process and show that when the strength of informative signals about the manuscript quality is sufficiently weak and reviewer’s confirmatory bias is sufficiently severe, there is a strong probability that the reviewer would erroneously identify the manuscript quality, making the editor less inclined to offer the potential reviewer any incentive to accept the invitation to review the manuscript. Based on these, we offer a theoretical explanation of current practices adopted to improve the review performance (e.g., desk rejection).

Suggested Citation

  • J. A. García & Rosa Rodriguez-Sánchez & J. Fdez-Valdivia, 2016. "Authors and reviewers who suffer from confirmatory bias," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1377-1395, November.
    • Handle: RePEc:spr:scient:v:109:y:2016:i:2:d:10.1007_s11192-016-2079-y
    DOI: 10.1007/s11192-016-2079-y
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    References listed on IDEAS

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    1. J. A. García & Rosa Rodriguez-Sánchez & J. Fdez-Valdivia, 2016. "Why the referees’ reports I receive as an editor are so much better than the reports I receive as an author?," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 967-986, March.
    2. Carole J. Lee & Cassidy R. Sugimoto & Guo Zhang & Blaise Cronin, 2013. "Bias in peer review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 2-17, January.
    3. Carole J. Lee & Cassidy R. Sugimoto & Guo Zhang & Blaise Cronin, 2013. "Bias in peer review," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(1), pages 2-17, January.
    4. Keren, Gideon, 1987. "Facing uncertainty in the game of bridge: A calibration study," Organizational Behavior and Human Decision Processes, Elsevier, vol. 39(1), pages 98-114, February.
    5. Jose A. García & Rosa Rodriguez-Sánchez & Joaquín Fdez-Valdivia, 2015. "Adverse selection of reviewers," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(6), pages 1252-1262, June.
    6. Matthew Rabin & Joel L. Schrag, 1999. "First Impressions Matter: A Model of Confirmatory Bias," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(1), pages 37-82.
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    Cited by:

    1. Bayindir, Esra Eren & Gurdal, Mehmet Yigit & Saglam, Ismail, 2019. "A Game Theoretic Approach to Peer Review of Grant Proposals," Journal of Informetrics, Elsevier, vol. 13(4).
    2. J. A. García & Rosa Rodriguez-Sánchez & J. Fdez-Valdivia, 2017. "STRATEGY: a tool for the formulation of peer-review strategies," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 45-60, October.
    3. Ivana Drvenica & Giangiacomo Bravo & Lucija Vejmelka & Aleksandar Dekanski & Olgica Nedić, 2018. "Peer Review of Reviewers: The Author’s Perspective," Publications, MDPI, vol. 7(1), pages 1-10, December.
    4. J. A. Garcia & Rosa Rodriguez-Sánchez & J. Fdez-Valdivia, 2020. "Confirmatory bias in peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 517-533, April.

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