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How to consider fractional counting and field normalization in the statistical modeling of bibliometric data: A multilevel Poisson regression approach

Author

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  • Mutz, Rüdiger
  • Daniel, Hans-Dieter

Abstract

The numerical-algorithmic procedures of fractional counting and field normalization are often mentioned as indispensable requirements for bibliometric analyses. Against the background of the increasing importance of statistics in bibliometrics, a multilevel Poisson regression model (level 1: publication, level 2: author) shows possible ways to consider fractional counting and field normalization in a statistical model (fractional counting I). However, due to the assumption of duplicate publications in the data set, the approach is not quite optimal. Therefore, a more advanced approach, a multilevel multiple membership model, is proposed that no longer provides for duplicates (fractional counting II). It is assumed that the citation impact can essentially be attributed to time-stable dispositions of researchers as authors who contribute with different fractions to the success of a publication’s citation. The two approaches are applied to bibliometric data for 254 scientists working in social science methodology. A major advantage of fractional counting II is that the results no longer depend on the type of fractional counting (e.g., equal weighting). Differences between authors in rankings are reproduced more clearly than on the basis of percentiles. In addition, the strong importance of field normalization is demonstrated; 60% of the citation variance is explained by field normalization.

Suggested Citation

  • Mutz, Rüdiger & Daniel, Hans-Dieter, 2019. "How to consider fractional counting and field normalization in the statistical modeling of bibliometric data: A multilevel Poisson regression approach," Journal of Informetrics, Elsevier, vol. 13(2), pages 643-657.
  • Handle: RePEc:eee:infome:v:13:y:2019:i:2:p:643-657
    DOI: 10.1016/j.joi.2019.03.007
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    Citations

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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. Saarela, Mirka & Kärkkäinen, Tommi, 2020. "Can we automate expert-based journal rankings? Analysis of the Finnish publication indicator," Journal of Informetrics, Elsevier, vol. 14(2).
    3. Yu, Dejian & Pan, Tianxing, 2021. "Tracing the main path of interdisciplinary research considering citation preference: A case from blockchain domain," Journal of Informetrics, Elsevier, vol. 15(2).
    4. Pan Wu & Jinlong Li & Yuzhuang Pian & Xiaochen Li & Zilin Huang & Lunhui Xu & Guilin Li & Ruonan Li, 2022. "How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method," Sustainability, MDPI, vol. 14(15), pages 1-31, August.
    5. Boris Forthmann, 2023. "Researcher capacity estimation based on the Q model: a generalized linear mixed model perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4753-4764, August.
    6. Ba, Zhichao & Ma, Yaxue & Cai, Jinyao & Li, Gang, 2023. "A citation-based research framework for exploring policy diffusion: Evidence from China's new energy policies," Technological Forecasting and Social Change, Elsevier, vol. 188(C).

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