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Menage a Quoi? Optimal Number of Peer Reviewers

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  • Richard R Snell

Abstract

Peer review represents the primary mechanism used by funding agencies to allocate financial support and by journals to select manuscripts for publication, yet recent Cochrane reviews determined literature on peer review best practice is sparse. Key to improving the process are reduction of inherent vulnerability to high degree of randomness and, from an economic perspective, limiting both the substantial indirect costs related to reviewer time invested and direct administrative costs to funding agencies, publishers and research institutions. Use of additional reviewers per application may increase reliability and decision consistency, but adds to overall cost and burden. The optimal number of reviewers per application, while not known, is thought to vary with accuracy of judges or evaluation methods. Here I use bootstrapping of replicated peer review data from a Post-doctoral Fellowships competition to show that five reviewers per application represents a practical optimum which avoids large random effects evident when fewer reviewers are used, a point where additional reviewers at increasing cost provides only diminishing incremental gains in chance-corrected consistency of decision outcomes. Random effects were most evident in the relative mid-range of competitiveness. Results support aggressive high- and low-end stratification or triaging of applications for subsequent stages of review, with the proportion and set of mid-range submissions to be retained for further consideration being dependent on overall success rate.

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  • Richard R Snell, 2015. "Menage a Quoi? Optimal Number of Peer Reviewers," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-14, April.
    • Handle: RePEc:plo:pone00:0120838
    DOI: 10.1371/journal.pone.0120838
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    References listed on IDEAS

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    1. Hendy Abdoul & Christophe Perrey & Philippe Amiel & Florence Tubach & Serge Gottot & Isabelle Durand-Zaleski & Corinne Alberti, 2012. "Peer Review of Grant Applications: Criteria Used and Qualitative Study of Reviewer Practices," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-15, September.
    2. Michael Obrecht & Karl Tibelius & Guy D'Aloisio, 2007. "Examining the value added by committee discussion in the review of applications for research awards," Research Evaluation, Oxford University Press, vol. 16(2), pages 79-91, June.
    3. Lutz Bornmann & Gerlind Wallon & Anna Ledin, 2008. "Does the Committee Peer Review Select the Best Applicants for Funding? An Investigation of the Selection Process for Two European Molecular Biology Organization Programmes," PLOS ONE, Public Library of Science, vol. 3(10), pages 1-11, October.
    4. Peres-Neto, Pedro R. & Jackson, Donald A. & Somers, Keith M., 2005. "How many principal components? stopping rules for determining the number of non-trivial axes revisited," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 974-997, June.
    5. Koehler, Elizabeth & Brown, Elizabeth & Haneuse, Sebastien J.-P. A., 2009. "On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses," The American Statistician, American Statistical Association, vol. 63(2), pages 155-162.
    6. 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.
    7. Lutz Bornmann & Hans-Dieter Daniel, 2006. "Potential sources of bias in research fellowship assessments: effects of university prestige and field of study," Research Evaluation, Oxford University Press, vol. 15(3), pages 209-219, December.
    8. Squazzoni, Flaminio & Bravo, Giangiacomo & Takács, Károly, 2013. "Does incentive provision increase the quality of peer review? An experimental study," Research Policy, Elsevier, vol. 42(1), pages 287-294.
    9. J Britt Holbrook & Robert Frodeman, 2011. "Peer review and the ex ante assessment of societal impacts," Research Evaluation, Oxford University Press, vol. 20(3), pages 239-246, September.
    10. 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.
    11. Benda, Wim G.G. & Engels, Tim C.E., 2011. "The predictive validity of peer review: A selective review of the judgmental forecasting qualities of peers, and implications for innovation in science," International Journal of Forecasting, Elsevier, vol. 27(1), pages 166-182.
    12. Liv Langfeldt, 2006. "The policy challenges of peer review: managing bias, conflict of interests and interdisciplinary assessments," Research Evaluation, Oxford University Press, vol. 15(1), pages 31-41, April.
    13. Paul J Roebber & David M Schultz, 2011. "Peer Review, Program Officers and Science Funding," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-6, April.
    14. Terttu Luukkonen, 2012. "Conservatism and risk-taking in peer review: Emerging ERC practices," Research Evaluation, Oxford University Press, vol. 21(1), pages 48-60, February.
    15. David Kaplan & Nicola Lacetera & Celia Kaplan, 2008. "Sample Size and Precision in NIH Peer Review," PLOS ONE, Public Library of Science, vol. 3(7), pages 1-3, July.
    16. Lipworth, Wendy L. & Kerridge, Ian H. & Carter, Stacy M. & Little, Miles, 2011. "Journal peer review in context: A qualitative study of the social and subjective dimensions of manuscript review in biomedical publishing," Social Science & Medicine, Elsevier, vol. 72(7), pages 1056-1063, April.
    17. Benda, Wim G.G. & Engels, Tim C.E., 2011. "The predictive validity of peer review: A selective review of the judgmental forecasting qualities of peers, and implications for innovation in science," International Journal of Forecasting, Elsevier, vol. 27(1), pages 166-182, January.
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