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

Boris Forthmann ()
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Boris Forthmann: University of Münster

Scientometrics, 2023, vol. 128, issue 8, No 24, 4753-4764

Abstract: 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.

Keywords: Q model; Researcher capacity; Generalized linear mixed model; 62P25 (search for similar items in EconPapers)
JEL-codes: C18 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11192-023-04756-9

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