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Scoring research output using statistical quantile plotting

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  • Beirlant, Jan
  • Glänzel, Wolfgang
  • Carbonez, An
  • Leemans, Herlinde

Abstract

In this paper, we propose two methods for scoring scientific output based on statistical quantile plotting. First, a rescaling of journal impact factors for scoring scientific output on a macro level is proposed. It is based on normal quantile plotting which allows to transform impact data over several subject categories to a standardized distribution. This can be used in comparing scientific output of larger entities such as departments working in quite different areas of research. Next, as an alternative to the Hirsch index [Hirsch, J.E. (2005). An index to quantify an individuals scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569–16572], the extreme value index is proposed as an indicator for assessment of the research performance of individual scientists. In case of Lotkaian–Zipf–Pareto behaviour of citation counts of an individual, the extreme value index can be interpreted as the slope in a Pareto–Zipf quantile plot. This index, in contrast to the Hirsch index, is not influenced by the number of publications but stresses the decay of the statistical tail of citation counts. It appears to be much less sensitive to the science field than the Hirsch index.

Suggested Citation

  • Beirlant, Jan & Glänzel, Wolfgang & Carbonez, An & Leemans, Herlinde, 2007. "Scoring research output using statistical quantile plotting," Journal of Informetrics, Elsevier, vol. 1(3), pages 185-192.
    • Handle: RePEc:eee:infome:v:1:y:2007:i:3:p:185-192
    DOI: 10.1016/j.joi.2007.04.002
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    References listed on IDEAS

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    1. Igor Podlubny, 2005. "Comparison of scientific impact expressed by the number of citations in different fields of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(1), pages 95-99, July.
    2. Wolfgang Glänzel & Henk F. Moed, 2002. "Journal impact measures in bibliometric research," Scientometrics, Springer;Akadémiai Kiadó, vol. 53(2), pages 171-193, February.
    3. Leo Egghe & Ronald Rousseau, 2006. "An informetric model for the Hirsch-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 121-129, October.
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    Citations

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    Cited by:

    1. J. Martin Zyl, 2013. "The generalized Pareto distribution fitted to research outputs of countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1099-1109, March.
    2. Wolfgang Glänzel, 2013. "High-end performance or outlier? Evaluating the tail of scientometric distributions," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(1), pages 13-23, October.
    3. Loet Leydesdorff & Ping Zhou & Lutz Bornmann, 2013. "How can journal impact factors be normalized across fields of science? An assessment in terms of percentile ranks and fractional counts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 96-107, January.
    4. Waltman, L. & van Eck, N.J.P., 2009. "Some Comments on Egghe’s Derivation of the Impact Factor Distribution," ERIM Report Series Research in Management ERS-2009-016-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. Tang, Li, 2013. "Does “birds of a feather flock together” matter—Evidence from a longitudinal study on US–China scientific collaboration," Journal of Informetrics, Elsevier, vol. 7(2), pages 330-344.
    6. Stegehuis, Clara & Litvak, Nelly & Waltman, Ludo, 2015. "Predicting the long-term citation impact of recent publications," Journal of Informetrics, Elsevier, vol. 9(3), pages 642-657.
    7. Richard S.J. Tol, 2013. "Measuring catch-up growth in malnourished populations," Working Paper Series 6013, Department of Economics, University of Sussex Business School.
    8. Tol, Richard S.J., 2013. "Identifying excellent researchers: A new approach," Journal of Informetrics, Elsevier, vol. 7(4), pages 803-810.
    9. Mutz, Rüdiger & Daniel, Hans-Dieter, 2012. "Skewed citation distributions and bias factors: Solutions to two core problems with the journal impact factor," Journal of Informetrics, Elsevier, vol. 6(2), pages 169-176.
    10. Wolfgang Glänzel & Henk F. Moed, 2013. "Opinion paper: thoughts and facts on bibliometric indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 381-394, July.
    11. Wolfgang Glänzel & Bart Thijs & Koenraad Debackere, 2014. "The application of citation-based performance classes to the disciplinary and multidisciplinary assessment in national comparison and institutional research assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 939-952, November.
    12. Wolfgang Glänzel & András Schubert & Bart Thijs & Koenraad Debackere, 2011. "A priori vs. a posteriori normalisation of citation indicators. The case of journal ranking," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(2), pages 415-424, May.
    13. Waltman, Ludo & van Eck, Nees Jan, 2009. "Some comments on Egghe's derivation of the impact factor distribution," Journal of Informetrics, Elsevier, vol. 3(4), pages 363-366.

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