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Empirical evidence for the relevance of fractional scoring in the calculation of percentile rank scores

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  • Michael Schreiber

Abstract

Fractional scoring has been proposed to avoid inconsistencies in the attribution of publications to percentile rank classes. Uncertainties and ambiguities in the evaluation of percentile ranks can be demonstrated most easily with small data sets. But for larger data sets, an often large number of papers with the same citation count leads to the same uncertainties and ambiguities, which can be avoided by fractional scoring, demonstrated by four different empirical data sets with several thousand publications each, which are assigned to six percentile rank classes. Only by utilizing fractional scoring does, the total score of all papers exactly reproduce the theoretical value in each case.

Suggested Citation

  • Michael Schreiber, 2013. "Empirical evidence for the relevance of fractional scoring in the calculation of percentile rank scores," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(4), pages 861-867, April.
    • Handle: RePEc:bla:jamist:v:64:y:2013:i:4:p:861-867
    DOI: 10.1002/asi.22774
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    Cited by:

    1. 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.
    2. 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.

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