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Committee peer review at an international research foundation: predictive validity and fairness of selection decisions on post-graduate fellowship applications

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  • Lutz Bornmann
  • Hans-Dieter Daniel

Abstract

We investigated committee peer review for awarding long-term fellowships to post-graduate researchers as practised by the Boehringer Ingelheim Fonds (BIF) — a foundation for the promotion of basic research in biomedicine. The most important aspect of our study was to investigate the predictive validity of the procedure, i.e. whether the foundation achieves its aim to select as fellowship recipients the best junior scientists. Our bibliometric analysis shows that this is indeed the case and that the selection procedure is thus highly valid. With regard to fairness of the procedure, we analysed the extent to which the foundation's Board of Trustees' practice of reviewing the applications in alphabetic order when making final selection decisions has an influence on the decisions that they make. A statistically significant influence of the postulated bias variable could be observed, but the effect size was small. Copyright , Beech Tree Publishing.

Suggested Citation

  • Lutz Bornmann & Hans-Dieter Daniel, 2005. "Committee peer review at an international research foundation: predictive validity and fairness of selection decisions on post-graduate fellowship applications," Research Evaluation, Oxford University Press, vol. 14(1), pages 15-20, April.
    • Handle: RePEc:oup:rseval:v:14:y:2005:i:1:p:15-20
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    File URL: http://hdl.handle.net/10.3152/147154405781776283
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    Citations

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    Cited by:

    1. Bornmann, Lutz & Mutz, Rüdiger & Daniel, Hans-Dieter, 2008. "Latent Markov modeling applied to grant peer review," Journal of Informetrics, Elsevier, vol. 2(3), pages 217-228.
    2. Qurat-ul Ain & Hira Riaz & Muhammad Tanvir Afzal, 2019. "Evaluation of h-index and its citation intensity based variants in the field of mathematics," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 187-211, April.
    3. Bornmann, Lutz & Mutz, Rüdiger & Daniel, Hans-Dieter, 2008. "How to detect indications of potential sources of bias in peer review: A generalized latent variable modeling approach exemplified by a gender study," Journal of Informetrics, Elsevier, vol. 2(4), pages 280-287.
    4. Azzurra Ragone & Katsiaryna Mirylenka & Fabio Casati & Maurizio Marchese, 2013. "On peer review in computer science: analysis of its effectiveness and suggestions for improvement," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 317-356, November.
    5. Bornmann, Lutz & Daniel, Hans-Dieter, 2007. "Gatekeepers of science—Effects of external reviewers’ attributes on the assessments of fellowship applications," Journal of Informetrics, Elsevier, vol. 1(1), pages 83-91.
    6. Rüdiger Mutz & Lutz Bornmann & Hans-Dieter Daniel, 2015. "Testing for the fairness and predictive validity of research funding decisions: A multilevel multiple imputation for missing data approach using ex-ante and ex-post peer evaluation data from the Austr," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2321-2339, November.
    7. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
    8. Bornmann, Lutz & Daniel, Hans-Dieter, 2007. "Convergent validation of peer review decisions using the h index," Journal of Informetrics, Elsevier, vol. 1(3), pages 204-213.

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