Scientometrics

, 81:407

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

Article
  • 105 Downloads

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.

References

  1. Bornmann, L., Daniel, H.-D. (2005a), Criteria used by a peer review committee for selection of research fellows. A Boolean probit analysis. International Journal of Selection and Assessment, 13(4), 296–303.CrossRefGoogle Scholar
  2. Bornmann, L., Daniel, H.-D. (2005b), Selection of research fellowship recipients by committee peer review. Analysis of reliability, fairness and predictive validity of Board of Trustees’ decisions. Scientometrics, 63(2): 297–320.CrossRefGoogle Scholar
  3. Bornmann, L., Mutz, R., Daniel, H.-D. (2007), Gender differences in grant peer review: A meta-analysis. Journal of Informetrics, 1(3): 226–238.CrossRefGoogle Scholar
  4. Bornmann, L., Mutz, R., Daniel, H.-D. (2008), Latent Markov modeling applied to grant peer review. Journal of Informetrics, 2(3): 217–228.CrossRefGoogle Scholar
  5. Cole, S. (1992), Making Science. Between Nature and Society, Cambridge, MA, USA, Harvard University Press.Google Scholar
  6. Ledin, A., Bornmann, L., Gannon, F., Wallon, G. (2007), A persistent problem. Traditional gender roles hold back female scientists. EMBO Reports, 8(11): 982–987.CrossRefGoogle Scholar
  7. Marsh, H. W., Jayasinghe, U. W., Bond, N. W. (2008), Improving the peer-review process for grant applications — reliability, validity, bias, and generalizability. American Psychologist, 63(3): 160–168.CrossRefGoogle Scholar
  8. Marsh, H. W., Bornmann, L., Mutz, R., Daniel, H.-D., O’Mara, A. (in press), Gender effects in the peer reviews of grant proposals: a comprehensive meta-analysis comparing traditional and multilevel approaches. Review of Educational Research.Google Scholar
  9. Nieva, V. F., Gutek, B. A. (1980), Sex effects on evaluation. Academy of Management Review, 5(2): 267–276.CrossRefGoogle Scholar
  10. van de Pol, F., Langeheine, R., de Jong, W. (2000), PANMARK 3: User’s Manual: PANel Analysis Using MARKov Chains; A Latent Class Analysis Program, Voorburg, Netherlands Central Bureau of Statistics.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2009

Authors and Affiliations

  • Lutz Bornmann
    • 1
  • Rüdiger Mutz
    • 1
  • Hans-Dieter Daniel
    • 1
    • 2
  1. 1.Professorship for Social Psychology and Research on Higher EducationETH ZurichZurichSwitzerland
  2. 2.Evaluation OfficeUniversity of ZurichZurichSwitzerland

Personalised recommendations