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The integrated impact indicator revisited (I3*): a non-parametric alternative to the journal impact factor

Author

Listed:
  • Loet Leydesdorff

    (University of Amsterdam)

  • Lutz Bornmann

    (Max Planck Society, Administrative Headquarters)

  • Jonathan Adams

    (King’s College London
    Institute for Scientific Information, Clarivate Analytics)

Abstract

We propose the I3* indicator as a non-parametric alternative to the journal impact factor (JIF) and h-index. We apply I3* to more than 10,000 journals. The results can be compared with other journal metrics. I3* is a promising variant within the general scheme of non-parametric I3 indicators introduced previously: I3* provides a single metric which correlates with both impact in terms of citations (c) and output in terms of publications (p). We argue for weighting using four percentile classes: the top-1% and top-10% as excellence indicators; the top-50% and bottom-50% as output indicators. Like the h-index, which also incorporates both c and p, I3*-values are size-dependent; however, division of I3* by the number of publications (I3*/N) provides a size-independent indicator which correlates strongly with the 2- and 5-year journal impact factors (JIF2 and JIF5). Unlike the h-index, I3* correlates significantly with both the total number of citations and publications. The values of I3* and I3*/N can be statistically tested against the expectation or against one another using chi-squared tests or effect sizes. A template (in Excel) is provided online for relevant tests.

Suggested Citation

  • Loet Leydesdorff & Lutz Bornmann & Jonathan Adams, 2019. "The integrated impact indicator revisited (I3*): a non-parametric alternative to the journal impact factor," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1669-1694, June.
    • Handle: RePEc:spr:scient:v:119:y:2019:i:3:d:10.1007_s11192-019-03099-8
    DOI: 10.1007/s11192-019-03099-8
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    Cited by:

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    2. Caroline S. Wagner & Lin Zhang & Loet Leydesdorff, 2022. "A discussion of measuring the top-1% most-highly cited publications: quality and impact of Chinese papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1825-1839, April.
    3. Lutz Bornmann & Richard Williams, 2020. "An evaluation of percentile measures of citation impact, and a proposal for making them better," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1457-1478, August.
    4. Pech, Gerson & Delgado, Catarina, 2021. "Screening the most highly cited papers in longitudinal bibliometric studies and systematic literature reviews of a research field or journal: Widespread used metrics vs a percentile citation-based app," Journal of Informetrics, Elsevier, vol. 15(3).
    5. Gabriel-Alexandru Vȋiu & Mihai Păunescu, 2021. "The lack of meaningful boundary differences between journal impact factor quartiles undermines their independent use in research evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1495-1525, February.
    6. Yves Fassin, 2020. "The HF-rating as a universal complement to the h-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 965-990, November.

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