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Researcher capacity estimation based on the Q model: a generalized linear mixed model perspective

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  • Boris Forthmann

    (University of Münster)

Abstract

Chance models of scientific creative productivity allow estimation of researcher capacity. One prominent such model is the Q model in which the impact of a scholarly work is modeled as a multiplicative function of researcher capacity and a potential impact (i.e., luck) parameter. Previous work estimated researcher capacity based on an approximation of the Q parameter. In this work, however, I outline how the Q model can be estimated within the framework of generalized linear mixed models. This way estimates of researcher capacity (and all other parameters of the Q model) are readily available and obtained by standard statistical software packages. Usage of such software further allows comparing different distributional assumptions and calculation of reliability of the Q parameter (i.e., researcher capacity). This is illustrated for a large dataset of multidisciplinary scientists (N = 20,296). The Poisson Q model was found to have negligibly better predictive accuracy than the original normal Q model. Reliability estimates revealed excellent reliability of Q estimates with conditional reliability being mostly in acceptable ranges. Reliability of Q parameter estimates further depended heavily on the number of publications of a scientist with reliability increasing with the number of papers. The future and limitations of the Q model in the context of researcher capacity estimation are thoroughly discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:8:d:10.1007_s11192-023-04756-9
    DOI: 10.1007/s11192-023-04756-9
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    References listed on IDEAS

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    1. James Hartley, 2017. "Authors and their citations: a point of view," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 1081-1084, February.
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    3. Mutz, Rüdiger & Daniel, Hans-Dieter, 2018. "The bibliometric quotient (BQ), or how to measure a researcher’s performance capacity: A Bayesian Poisson Rasch model," Journal of Informetrics, Elsevier, vol. 12(4), pages 1282-1295.
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    5. 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.
    6. Boris Forthmann & Philipp Doebler, 2021. "Reliability of researcher capacity estimates and count data dispersion: a comparison of Poisson, negative binomial, and Conway-Maxwell-Poisson models," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3337-3354, April.
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    Cited by:

    1. Boris Forthmann & Philipp Doebler & Rüdiger Mutz, 2024. "Why summing up bibliometric indicators does not justify a composite indicator," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(12), pages 7475-7499, December.

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    More about this item

    Keywords

    Q model; Researcher capacity; Generalized linear mixed model;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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