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The generalized propensity score methodology for estimating unbiased journal impact factors

Author

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  • Rüdiger Mutz

    (ETH Zurich)

  • Hans-Dieter Daniel

    (ETH Zurich
    University of Zurich)

Abstract

The journal impact factor (JIF) proposed by Garfield in the year 1955 is one of the most commonly used and prominent citation-based indicators of the performance and significance of a scientific journal. The JIF is simple, reasonable, clearly defined, and comparable over time and, what is more, can be easily calculated from data provided by Thomson Reuters, but at the expense of serious technical and methodological flaws. The paper discusses one of the core problems: The JIF is affected by bias factors (e.g., document type) that have nothing to do with the prestige or quality of a journal. For solving this problem, we suggest using the generalized propensity score methodology based on the Rubin Causal Model. Citation data for papers of all journals in the ISI subject category “Microscopy” (Journal Citation Report) are used to illustrate the proposal.

Suggested Citation

  • Rüdiger Mutz & Hans-Dieter Daniel, 2012. "The generalized propensity score methodology for estimating unbiased journal impact factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(2), pages 377-390, August.
    • Handle: RePEc:spr:scient:v:92:y:2012:i:2:d:10.1007_s11192-012-0670-4
    DOI: 10.1007/s11192-012-0670-4
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    References listed on IDEAS

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    Cited by:

    1. Bornmann, Lutz & Haunschild, Robin & Mutz, Rüdiger, 2020. "Should citations be field-normalized in evaluative bibliometrics? An empirical analysis based on propensity score matching," Journal of Informetrics, Elsevier, vol. 14(4).
    2. Rüdiger Mutz & Tobias Wolbring & Hans-Dieter Daniel, 2017. "The effect of the “very important paper” (VIP) designation in Angewandte Chemie International Edition on citation impact: A propensity score matching analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(9), pages 2139-2153, September.
    3. Xu, Shuqi & Mariani, Manuel Sebastian & Lü, Linyuan & Medo, Matúš, 2020. "Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data," Journal of Informetrics, Elsevier, vol. 14(1).
    4. Adriana Bin & Sergio Salles-Filho & Luiza Maria Capanema & Fernando Antonio Basile Colugnati, 2015. "What difference does it make? Impact of peer-reviewed scholarships on scientific production," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1167-1188, February.
    5. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    6. Fernando A. B. Colugnati & Sergio Firpo & Paula F. Drummond Castro & Juan E. Sepulveda & Sergio L. M. Salles-Filho, 2014. "A propensity score approach in the impact evaluation on scientific production in Brazilian biodiversity research: the BIOTA Program," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 85-107, October.
    7. Pilar Valderrama & Manuel Escabias & Evaristo Jiménez-Contreras & Alberto Rodríguez-Archilla & Mariano J. Valderrama, 2018. "Proposal of a stochastic model to determine the bibliometric variables influencing the quality of a journal: application to the field of Dentistry," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 1087-1095, May.
    8. Juan Miguel Campanario, 2014. "The effect of citations on the significance of decimal places in the computation of journal impact factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 289-298, May.

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