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Does the average JIF percentile make a difference?

Author

Listed:
  • Liping Yu

    (Yunan University of finance and economics)

  • Houqiang Yu

    (Wuhan University)

Abstract

Average journal impact factor (JIF) percentile is a novel bibliometric indicator introduced by Thomson Reuters. It’s of great significance to study the characteristics of its data distribution and relationship with other bibliometric indicators, in order to assess its usefulness as a new bibliometric indicator. The research began by analyzing the meaning of average JIF percentile, and compared its statistical difference with impact factor. Based upon factor analysis, the paper used multivariate regression and quantile regression to study the relationship between average JIF percentile and other bibliometric indicators. Results showed that average JIF percentile had changed the statistical characteristic of impact factor, e.g. improved the relative value of impact factor, having smaller variation coefficient and distribution closer to normal distribution. Because it’s non-parametric transformation, it cannot be used to measure the relative gap between journals; Average JIF percentile had the highest regression coefficient with journal impact, followed by timeliness and lastly the citable items; The lower the average JIF percentile, the higher the elastic coefficient of journal impact; When average JIF percentile was extremely high or extremely low, citable items were not correlated with the average JIF percentile at all; When average JIF percentile was low, elastic coefficient of timeliness was even higher; Average JIF percentile was not a proper indicator for multivariate journal evaluation; Average JIF percentile had both the advantages and disadvantages of impact factor, and thus had the same limitation in applying as the impact factor.

Suggested Citation

  • Liping Yu & Houqiang Yu, 2016. "Does the average JIF percentile make a difference?," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1979-1987, December.
    • Handle: RePEc:spr:scient:v:109:y:2016:i:3:d:10.1007_s11192-016-2156-2
    DOI: 10.1007/s11192-016-2156-2
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    References listed on IDEAS

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    1. 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.
    2. Campanario, Juan Miguel, 2014. "Analysis of the distribution of cited journals according to their positions in the h-core of citing journal listed in Journal Citation Reports," Journal of Informetrics, Elsevier, vol. 8(3), pages 534-545.
    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 American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(2), pages 416-420, February.
    5. Lutz Bornmann, 2013. "The problem of percentile rank scores used with small reference sets," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(3), pages 650-650, March.
    6. 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.
    7. Loet Leydesdorff, 2012. "Alternatives to the journal impact factor: I3 and the top-10% (or top-25%?) of the most-highly cited papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(2), pages 355-365, August.
    8. 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.
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