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Potential sources of bias in research fellowship assessments: effects of university prestige and field of study

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  • Lutz Bornmann
  • Hans-Dieter Daniel

Abstract

In this study we analysed the committee peer review process used by an international foundation for the promotion of basic research in biomedicine (Boehringer Ingelheim Fonds) as to whether the scientific field of study within which the research project is proposed and the institutional prestige of the grant applicant's university influenced the decision to award doctoral and post-doctoral fellowships. The results of the comprehensive empirical analyses show statistically significant evidence of a bias in the review process towards certain fields of study, but no evidence of a bias towards applications for fellowships from researchers affiliated with prestigious universities. Copyright , Beech Tree Publishing.

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  • 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.
    • Handle: RePEc:oup:rseval:v:15:y:2006:i:3:p:209-219
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    File URL: http://hdl.handle.net/10.3152/147154406781775850
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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. 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.
    3. Antonio Messeni Petruzzelli & Gianluca Murgia, 2020. "University–Industry collaborations and international knowledge spillovers: a joint-patent investigation," The Journal of Technology Transfer, Springer, vol. 45(4), pages 958-983, August.
    4. Alfredo Yegros Yegros & Carlos B. Amat, 2009. "Editorial delay of food research papers is influenced by authors’ experience but not by country of origin of the manuscripts," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(2), pages 367-380, November.
    5. Dekel Omer & Schurr Amos, 2014. "Cognitive Biases in Government Procurement – An Experimental Study," Review of Law & Economics, De Gruyter, vol. 10(2), pages 169-200, July.
    6. Zhentao Liang & Jin Mao & Gang Li, 2023. "Bias against scientific novelty: A prepublication perspective," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 99-114, January.
    7. 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.
    8. 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.
    9. Materia, V.C. & Pascucci, S. & Kolympiris, C., 2015. "Understanding the selection processes of public research projects in agriculture: The role of scientific merit," Food Policy, Elsevier, vol. 56(C), pages 87-99.

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