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A Generalised Linear Model Approach to Predict the Result of Research Evaluation

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Abstract

Peer review is still used as the main tool for research evaluation, but its costly and time-consuming nature triggers a debate about the necessity to use, alternatively or jointly with it, bibliometric indicators. In this contribution we introduce an approach based on generalised linear models that jointly uses former peer-review and bibliometric indicators to predict the outcome of UK’s Research Excellence Framework (REF) 2014. We use the outcomes of the Research Assessment Exercise (RAE) 2008 as peer-review indicators and the departmental h-indices for the period 2008–2014 as bibliometric indicators. The results show that a joint use of bibliometric and peer-review indicators can be an effective tool to predict the research evaluation made by REF.

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Notes

  1. 1.

    HEIs with missing values have been removed from the data set.

References

  1. Abramowitz, M., Stegun, I.A.: Gamma and related functions (Chapter 6). In: Handbook of Mathematical Functions. Dover, New York (1972)

    MATH  Google Scholar 

  2. Bertocchi, G., Gambardella, A., Jappelli, T., Nappi, C.A., Peracchi, F.: Bibliometric evaluation vs. informed peer-review: evidence from Italy. Res. Policy 44(2), 451–466 (2011)

    Google Scholar 

  3. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geol. 10(2–3), 191–203 (1984)

    Article  Google Scholar 

  4. Bornmann, L.: The hawthorne effect in journal peer review. Scientometrics 91, 857–862 (2012)

    Article  Google Scholar 

  5. Bornmann, L., Leydesdorff, I.: Scientometrics in a changing research landscape. EMBO Rep. 15(12), 1228–1232 (2014)

    Article  Google Scholar 

  6. De Bellis, N.: Bibliometrics and Citation Analysis: From the Science Citation Index to Cybermetrics. The Scarecrow Press, Lanham (2009)

    Google Scholar 

  7. Engels, T.C.E., Goos, P., Dexters, N., Spruyt, E.H.J.: Group size, h-index, and efficiency in publishing in top journals explain expert panel assessments of research group quality and productivity. Res. Eval. 22(4), 224–236 (2013)

    Article  Google Scholar 

  8. Evidence: The future of the UK university research base. Technical report, a Thomson Reuters business, Universities UK (2010)

    Google Scholar 

  9. Lawrance, P.: The politics of publications. Nature 422, 259–261 (2003)

    Article  Google Scholar 

  10. MacRoberts, M.H., MacRoberts, B.R.: Problems of citation analysis: a critical review. J. Am. Soc. Inf. Sci. 40(5), 342–349 (1989)

    Article  Google Scholar 

  11. Mryglod, O., Kenna, R., Holovatch, Y., Berche, B.: Absolute and specific measures of research group excellence. Scientometrics 95(1), 115–127 (2013)

    Article  Google Scholar 

  12. Mryglod, O., Kenna, R., Holovatch, Yu., Berche, B.: Predicting results of the research excellence framework using departmental h-index. Scientometrics 102(3), 2165–2180 (2015)

    Article  Google Scholar 

  13. Mryglod, O., Kenna, R., Holovatch, Yu., Berche, B.: Predicting results of the research excellence framework using departmental h-index: revisited. Scientometrics 104(3), 1013–1017 (2015)

    Article  Google Scholar 

  14. Nelder, J., Wedderburn, R.: Generalized linear models. J. R. Stat. Soc. Ser. A 135(3), 370–384 (1972)

    Article  Google Scholar 

  15. Wouters, P., Thelwall, M., Kousha, K., Waltman, L., de Rijcke, S., Rushforth, A., Franssen, T.: The metric tide: literature review. Technical report, Higher Education Funding Council for England (2015)

    Google Scholar 

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Correspondence to Giacomo di Tollo .

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Basso, A., di Tollo, G. (2017). A Generalised Linear Model Approach to Predict the Result of Research Evaluation. In: Corazza, M., Legros, F., Perna, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance . Springer, Cham. https://doi.org/10.1007/978-3-319-50234-2_3

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