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Inconsistencies of recently proposed citation impact indicators and how to avoid them

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  • Michael Schreiber

Abstract

It is shown that under certain circumstances in particular for small data sets, the recently proposed citation impact indicators I3(6PR) and R(6,k) behave inconsistently when additional papers or citations are taken into consideration. Three simple examples are presented, in which the indicators fluctuate strongly and the ranking of scientists in the evaluated group is sometimes completely mixed up by minor changes in the database. The erratic behavior is traced to the specific way in which weights are attributed to the six percentile rank classes, specifically for the tied papers. For 100 percentile rank classes, the effects will be less serious. For the six classes, it is demonstrated that a different way of assigning weights avoids these problems, although the nonlinearity of the weights for the different percentile rank classes can still lead to (much less frequent) changes in the ranking. This behavior is not undesired because it can be used to correct for differences in citation behavior in different fields. Remaining deviations from the theoretical value R(6,k) = 1.91 can be avoided by a new scoring rule: the fractional scoring. Previously proposed consistency criteria are amended by another property of strict independence at which a performance indicator should aim.

Suggested Citation

  • Michael Schreiber, 2012. "Inconsistencies of recently proposed citation impact indicators and how to avoid them," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(10), pages 2062-2073, October.
  • Handle: RePEc:bla:jamist:v:63:y:2012:i:10:p:2062-2073
    DOI: 10.1002/asi.22703
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    Cited by:

    1. Albarrán, Pedro & Herrero, Carmen & Ruiz-Castillo, Javier & Villar, Antonio, 2017. "The Herrero-Villar approach to citation impact," Journal of Informetrics, Elsevier, vol. 11(2), pages 625-640.
    2. 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.
    3. Thor, Andreas & Marx, Werner & Leydesdorff, Loet & Bornmann, Lutz, 2016. "Introducing CitedReferencesExplorer (CRExplorer): A program for reference publication year spectroscopy with cited references standardization," Journal of Informetrics, Elsevier, vol. 10(2), pages 503-515.
    4. Ludo Waltman & Michael Schreiber, 2013. "On the calculation of percentile-based bibliometric indicators," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(2), pages 372-379, February.
    5. Zhou, Ping & Zhong, Yongfeng, 2012. "The citation-based indicator and combined impact indicator—New options for measuring impact," Journal of Informetrics, Elsevier, vol. 6(4), pages 631-638.
    6. 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.
    7. Bornmann, Lutz & Leydesdorff, Loet & Mutz, Rüdiger, 2013. "The use of percentiles and percentile rank classes in the analysis of bibliometric data: Opportunities and limits," Journal of Informetrics, Elsevier, vol. 7(1), pages 158-165.

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