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Median and percentile impact factors: A set of new indicators

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  • Ronald Rousseau

    (KHBO - Industrial Sciences and Technology)

Abstract

Summary In a recent article Sombatsompop et al. (2004) proposed a new way of calculating a synchronous journal impact factor. Their proposal seems quite interesting and will be discussed in this note. Their index will be referred as the Median Impact Factor (MIF). I explain every step in detail so that readers with little mathematical background can understand and apply the procedure. Illustrations of the procedure are presented. Some attention is given to the estimation of the median cited age in case it is larger than ten year. I think the idea introduced by Sombatsompop, Markpin and Premkamolnetr has a great theoretical value as they are - to the best of my knowledge - the first ones to consider impact factors not using years as a basic ingredient, but an element of the actual form of the citation curve. The MIF is further generalized to the notion of a percentile impact factor.

Suggested Citation

  • Ronald Rousseau, 2005. "Median and percentile impact factors: A set of new indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 63(3), pages 431-441, June.
    • Handle: RePEc:spr:scient:v:63:y:2005:i:3:d:10.1007_s11192-005-0223-1
    DOI: 10.1007/s11192-005-0223-1
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    Citations

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    Cited by:

    1. Zhenyu Gou & Fan Meng & Zaida Chinchilla-Rodríguez & Yi Bu, 2022. "Encoding the citation life-cycle: the operationalization of a literature-aging conceptual model," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 5027-5052, August.
    2. Johan Bollen & Herbert Van de Sompel & Aric Hagberg & Ryan Chute, 2009. "A Principal Component Analysis of 39 Scientific Impact Measures," PLOS ONE, Public Library of Science, vol. 4(6), pages 1-11, June.
    3. Zitt, Michel, 2010. "Citing-side normalization of journal impact: A robust variant of the Audience Factor," Journal of Informetrics, Elsevier, vol. 4(3), pages 392-406.
    4. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    5. Liu, Xuan Zhen & Fang, Hui, 2020. "A comparison among citation-based journal indicators and their relative changes with time," Journal of Informetrics, Elsevier, vol. 14(1).
    6. José María Gómez-Sancho & María Jesús Mancebón-Torrubia, 2010. "A new approach to measuring scientific production in JCR journals and its application to Spanish public universities," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 271-293, October.
    7. Opthof, Tobias & Leydesdorff, Loet, 2010. "Caveats for the journal and field normalizations in the CWTS (“Leiden”) evaluations of research performance," Journal of Informetrics, Elsevier, vol. 4(3), pages 423-430.
    8. Tahamtan, Iman & Bornmann, Lutz, 2018. "Creativity in science and the link to cited references: Is the creative potential of papers reflected in their cited references?," Journal of Informetrics, Elsevier, vol. 12(3), pages 906-930.
    9. 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.
    10. José María Gómez-Sancho & María Jesús Mancebón-Torrubia, 2009. "The evaluation of scientific production: Towards a neutral impact factor," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(2), pages 435-458, November.
    11. Peter Jacso, 2012. "Grim tales about the impact factor and the h-index in the Web of Science and the Journal Citation Reports databases: reflections on Vanclay’s criticism," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(2), pages 325-354, August.
    12. Weibin Wang & Zheng Wang & Tian Yu & CholMyong Pak & Guang Yu, 2020. "Research on citation mention times and contributions using a neural network," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2383-2400, December.

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