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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 Austrian science fund

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  • Rüdiger Mutz
  • Lutz Bornmann
  • Hans-Dieter Daniel

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  • 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.
    • Handle: RePEc:bla:jinfst:v:66:y:2015:i:11:p:2321-2339
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Yucel, Recai M., 2011. "State of the Multiple Imputation Software," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i01).
    3. Upali W. Jayasinghe & Herbert W. Marsh & Nigel Bond, 2003. "A multilevel cross‐classified modelling approach to peer review of grant proposals: the effects of assessor and researcher attributes on assessor ratings," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(3), pages 279-300, October.
    4. Martin Reinhart, 2009. "Peer review of grant applications in biology and medicine. Reliability, fairness, and validity," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(3), pages 789-809, December.
    5. Peter van den Besselaar & Loet Leydesdorff, 2009. "Past performance, peer review and project selection: a case study in the social and behavioral sciences," Research Evaluation, Oxford University Press, vol. 18(4), pages 273-288, October.
    6. Daniel Bauer, 2009. "A Note on Comparing the Estimates of Models for Cluster-Correlated or Longitudinal Data with Binary or Ordinal Outcomes," Psychometrika, Springer;The Psychometric Society, vol. 74(1), pages 97-105, March.
    7. 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.
    8. Lutz Bornmann & Christophe Weymuth & Hans-Dieter Daniel, 2010. "A content analysis of referees’ comments: how do comments on manuscripts rejected by a high-impact journal and later published in either a low- or high-impact journal differ?," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(2), pages 493-506, May.
    9. Hakan Demirtas & Donald Hedeker, 2008. "Imputing continuous data under some non‐Gaussian distributions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(2), pages 193-205, May.
    10. Lutz Bornmann & Hans-Dieter Daniel, 2005. "Selection of research fellowship recipients by committee peer review. Reliability, fairness and predictive validity of Board of Trustees' decisions," Scientometrics, Springer;Akadémiai Kiadó, vol. 63(2), pages 297-320, April.
    11. 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.
    12. 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.
    13. Meade, Adam W. & Tonidandel, Scott, 2010. "Not Seeing Clearly With Cleary: What Test Bias Analyses Do and Do Not Tell Us," Industrial and Organizational Psychology, Cambridge University Press, vol. 3(2), pages 192-205, June.
    14. Lutz Bornmann & Rüdiger Mutz & Werner Marx & Hermann Schier & Hans‐Dieter Daniel, 2011. "A multilevel modelling approach to investigating the predictive validity of editorial decisions: do the editors of a high profile journal select manuscripts that are highly cited after publication?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(4), pages 857-879, October.
    15. Lutz Bornmann & Hans‐Dieter Daniel, 2008. "Selecting manuscripts for a high‐impact journal through peer review: A citation analysis of communications that were accepted by Angewandte Chemie International Edition, or rejected but published else," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(11), pages 1841-1852, September.
    16. Meade, Adam W. & Tonidandel, Scott, 2010. "Final Thoughts on Measurement Bias and Differential Prediction," Industrial and Organizational Psychology, Cambridge University Press, vol. 3(2), pages 232-237, June.
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    Cited by:

    1. Bornmann, Lutz & Haunschild, Robin & Mutz, Rüdiger, 2020. "Should citations be field-normalized in evaluative bibliometrics? An empirical analysis based on propensity score matching," Journal of Informetrics, Elsevier, vol. 14(4).
    2. Kevin W. Boyack & Caleb Smith & Richard Klavans, 2018. "Toward predicting research proposal success," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 449-461, February.
    3. Linton, Jonathan D., 2016. "Improving the Peer review process: Capturing more information and enabling high-risk/high-return research," Research Policy, Elsevier, vol. 45(9), pages 1936-1938.

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