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Predicting results of the Research Excellence Framework using departmental h-index

Author

Listed:
  • O. Mryglod

    (National Academy of Sciences of Ukraine)

  • R. Kenna

    (Coventry University)

  • Yu. Holovatch

    (National Academy of Sciences of Ukraine)

  • B. Berche

    (Université de Lorraine, Statistical Physics Group, IJL, UMR CNRS 7198)

Abstract

We compare estimates for past institutional research performances coming from two bibliometric indicators to the results of the UK’s Research Assessment Exercise which last took place in 2008. We demonstrate that a version of the departmental h-index is better correlated with the actual results of that peer-review exercise than a competing metric known as the normalised citation-based indicator. We then determine the corresponding h-indices for 2008–2013, the period examined in the UK’s Research Excellence Framework (REF) 2014. We place herewith the resulting predictions on the arXiv in advance of the REF results being published (December 2014). These may be considered as unbiased predictions of relative performances in that exercise. We will revisit this paper after the REF results are available and comment on the reliability or otherwise of these bibliometrics as compared with peer review.

Suggested Citation

  • O. Mryglod & R. Kenna & Yu. Holovatch & B. Berche, 2015. "Predicting results of the Research Excellence Framework using departmental h-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2165-2180, March.
    • Handle: RePEc:spr:scient:v:102:y:2015:i:3:d:10.1007_s11192-014-1512-3
    DOI: 10.1007/s11192-014-1512-3
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    References listed on IDEAS

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    1. Jean-Francois Molinari & Alain Molinari, 2008. "A new methodology for ranking scientific institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(1), pages 163-174, April.
    2. Anthony F. J. Raan, 2006. "Comparison of the Hirsch-index with standard bibliometric indicators and with peer judgment for 147 chemistry research groups," Scientometrics, Springer;Akadémiai Kiadó, vol. 67(3), pages 491-502, June.
    3. O. Mryglod & R. Kenna & Yu. Holovatch & B. Berche, 2013. "Absolute and specific measures of research group excellence," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 115-127, April.
    4. Michael H. MacRoberts & Barbara R. MacRoberts, 1989. "Problems of citation analysis: A critical review," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 40(5), pages 342-349, September.
    5. O. Mryglod & R. Kenna & Yu. Holovatch & B. Berche, 2013. "Comparison of a citation-based indicator and peer review for absolute and specific measures of research-group excellence," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 767-777, December.
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    Cited by:

    1. Daniele Checchi & Alberto Ciolfi & Gianni De Fraja & Irene Mazzotta & Stefano Verzillo, 2021. "Have you Read This? An Empirical Comparison of the British REF Peer Review and the Italian VQR Bibliometric Algorithm," Economica, London School of Economics and Political Science, vol. 88(352), pages 1107-1129, October.
    2. Kayvan Kousha & Mike Thelwall, 2024. "Factors associating with or predicting more cited or higher quality journal articles: An Annual Review of Information Science and Technology (ARIST) paper," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 75(3), pages 215-244, March.
    3. James Tooley & Barrie Craven, 2018. "Private Sector Alternatives to the Research Excellence Framework for University League Tables," Economic Affairs, Wiley Blackwell, vol. 38(3), pages 434-443, October.
    4. Shahd Al-Janabi & Lee Wei Lim & Luca Aquili, 2021. "Development of a tool to accurately predict UK REF funding allocation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 8049-8062, September.
    5. Stephan B. Bruns & David I. Stern, 2016. "Research assessment using early citation information," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 917-935, August.
    6. Paolo Fantozzi & Valerio Ficcadenti & Maurizio Naldi, 2025. "The university research assessment dilemma: a decision support system for the next evaluation campaigns," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(3), pages 1617-1658, March.
    7. Alberto Baccini & Giuseppe De Nicolao, 2016. "Do they agree? Bibliometric evaluation versus informed peer review in the Italian research assessment exercise," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1651-1671, September.
    8. Basso, Antonella & di Tollo, Giacomo, 2022. "Prediction of UK research excellence framework assessment by the departmental h-index," European Journal of Operational Research, Elsevier, vol. 296(3), pages 1036-1049.
    9. Thelwall, Mike & Kousha, Kayvan & Stuart, Emma & Makita, Meiko & Abdoli, Mahshid & Wilson, Paul & Levitt, Jonathan, 2023. "Do bibliometrics introduce gender, institutional or interdisciplinary biases into research evaluations?," Research Policy, Elsevier, vol. 52(8).
    10. Morgan-Thomas, Anna & Tsoukas, Serafeim & Dudau, Adina & Gąska, Paweł, 2024. "Beyond declarations: Metrics, rankings and responsible assessment," Research Policy, Elsevier, vol. 53(10).
    11. Giovanni Abramo & Ciriaco Andrea D’Angelo & Emanuela Reale, 2019. "Peer review versus bibliometrics: Which method better predicts the scholarly impact of publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 537-554, October.
    12. Banal-Estañol, Albert & Jofre-Bonet, Mireia & Iori, Giulia & Maynou, Laia & Tumminello, Michele & Vassallo, Pietro, 2023. "Performance-based research funding: Evidence from the largest natural experiment worldwide," Research Policy, Elsevier, vol. 52(6).
    13. Lloyd D Balbuena, 2018. "The UK Research Excellence Framework and the Matthew effect: Insights from machine learning," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-13, November.

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