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

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.

Automated summary

This study presents a method to estimate the Q model using generalized linear mixed models, which is a prominent approach for assessing researcher capacity. By using statistical software packages, estimates of researcher capacity and other Q model parameters can be obtained easily. In a large dataset, the Poisson Q model was found to have slightly better predictive accuracy than the original normal Q model. The reliability of Q parameter estimates depended heavily on the number of publications, with reliability increasing as the number of papers increased. The future and limitations of the Q model in estimating researcher capacity are thoroughly discussed.