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The influence of the applicants’ gender on the modeling of a peer review process by using latent Markov models

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  • Lutz Bornmann

    (ETH Zurich)

  • Rüdiger Mutz

    (ETH Zurich)

  • Hans-Dieter Daniel

    (ETH Zurich
    University of Zurich)

Abstract

In the grant peer review process we can distinguish various evaluation stages in which assessors judge applications on a rating scale. Bornmann & al. [2008] show that latent Markov models offer a fundamentally good opportunity to model statistically peer review processes. The main objective of this short communication is to test the influence of the applicants’ gender on the modeling of a peer review process by using latent Markov models. We found differences in transition probabilities from one stage to the other for applications for a doctoral fellowship submitted by male and female applicants.

Suggested Citation

  • Lutz Bornmann & Rüdiger Mutz & Hans-Dieter Daniel, 2009. "The influence of the applicants’ gender on the modeling of a peer review process by using latent Markov models," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(2), pages 407-411, November.
    • Handle: RePEc:spr:scient:v:81:y:2009:i:2:d:10.1007_s11192-008-2189-2
    DOI: 10.1007/s11192-008-2189-2
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    References listed on IDEAS

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    1. Bornmann, Lutz & Mutz, Rüdiger & Daniel, Hans-Dieter, 2007. "Gender differences in grant peer review: A meta-analysis," Journal of Informetrics, Elsevier, vol. 1(3), pages 226-238.
    2. 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.
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    Cited by:

    1. Lutz Bornmann & Hanna Herich & Hanna Joos & Hans-Dieter Daniel, 2012. "In public peer review of submitted manuscripts, how do reviewer comments differ from comments written by interested members of the scientific community? A content analysis of comments written for Atmo," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 915-929, December.
    2. Thomas Feliciani & Junwen Luo & Lai Ma & Pablo Lucas & Flaminio Squazzoni & Ana Marušić & Kalpana Shankar, 2019. "A scoping review of simulation models of peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 555-594, October.
    3. 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.
    4. Bradford Demarest & Guo Freeman & Cassidy R. Sugimoto, 2014. "The reviewer in the mirror: examining gendered and ethnicized notions of reciprocity in peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 717-735, October.
    5. Mingliang Yue & Hongbo Tang & Fan Liu & Tingcan Ma, 2021. "Consistency index: measuring the performances of scholar journal reviewers," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7183-7195, August.

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