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How much do different ways of calculating percentiles influence the derived performance indicators? A case study

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  • Michael Schreiber

    (Institute of Physics, Chemnitz University of Technology)

Abstract

Bibliometric indicators can be determined by comparing specific citation records with the percentiles of a reference set. However, there exists an ambiguity in the computation of percentiles because usually a significant number of papers with the same citation count are found at the border between percentile rank classes. The present case study of the citations to the journal Europhysics Letters (EPL) in comparison with all physics papers from the Web of Science shows the deviations which occur due to the different ways of treating the tied papers in the evaluation of the percentage of highly cited publications. A strong bias can occur, if the papers tied at the threshold number of citations are all considered as highly cited or all considered as not highly cited.

Suggested Citation

  • Michael Schreiber, 2013. "How much do different ways of calculating percentiles influence the derived performance indicators? A case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 821-829, December.
    • Handle: RePEc:spr:scient:v:97:y:2013:i:3:d:10.1007_s11192-013-0984-x
    DOI: 10.1007/s11192-013-0984-x
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    References listed on IDEAS

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    1. Loet Leydesdorff, 2012. "Accounting for the uncertainty in the evaluation of percentile ranks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(11), pages 2349-2350, November.
    2. Loet Leydesdorff & Lutz Bornmann & Rüdiger Mutz & Tobias Opthof, 2011. "Turning the tables on citation analysis one more time: Principles for comparing sets of documents," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(7), pages 1370-1381, July.
    3. Loet Leydesdorff & Lutz Bornmann, 2011. "Integrated impact indicators compared with impact factors: An alternative research design with policy implications," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(11), pages 2133-2146, November.
    4. Ronald Rousseau, 2012. "Basic properties of both percentile rank scores and the I3 indicator," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(2), pages 416-420, February.
    5. Michael Schreiber, 2012. "Inconsistencies of recently proposed citation impact indicators and how to avoid them," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(10), pages 2062-2073, October.
    6. Ronald Rousseau, 2012. "Basic properties of both percentile rank scores and the I3 indicator," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(2), pages 416-420, February.
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    Citations

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    Cited by:

    1. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    2. Brito, Ricardo & Rodríguez-Navarro, Alonso, 2018. "Research assessment by percentile-based double rank analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 315-329.
    3. Bornmann, Lutz & Leydesdorff, Loet & Wang, Jian, 2013. "Which percentile-based approach should be preferred for calculating normalized citation impact values? An empirical comparison of five approaches including a newly developed citation-rank approach (P1," Journal of Informetrics, Elsevier, vol. 7(4), pages 933-944.
    4. Schreiber, Michael, 2014. "How to improve the outcome of performance evaluations in terms of percentiles for citation frequencies of my papers," Journal of Informetrics, Elsevier, vol. 8(4), pages 873-879.
    5. Schreiber, Michael, 2014. "Examples for counterintuitive behavior of the new citation-rank indicator P100 for bibliometric evaluations," Journal of Informetrics, Elsevier, vol. 8(3), pages 738-748.
    6. Thelwall, Mike, 2016. "The precision of the arithmetic mean, geometric mean and percentiles for citation data: An experimental simulation modelling approach," Journal of Informetrics, Elsevier, vol. 10(1), pages 110-123.
    7. Pislyakov, Vladimir, 2022. "On some properties of medians, percentiles, baselines, and thresholds in empirical bibliometric analysis," Journal of Informetrics, Elsevier, vol. 16(4).
    8. Bornmann, Lutz & Haunschild, Robin, 2016. "Citation score normalized by cited references (CSNCR): The introduction of a new citation impact indicator," Journal of Informetrics, Elsevier, vol. 10(3), pages 875-887.

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