Statistical modeling of scientific productivity and impact provides insights into bibliometric measures used also to quantify differences between individual scholars. The Q model decomposes the log-transformed impact of a published paper into a researcher capacity parameter and a random luck parameter. These two parameters are then modeled together with the log-transformed number of published papers (i.e., an indicator of productivity) by means of a trivariate normal distribution. In this work we propose a formulation of the Q model that can be estimated as a structural equation model. The Q model as a structural equation model allows us to quantify the reliability of researchers’ Q parameter estimates. It can be extended to incorporate person covariates and multivariate extensions of the Q model could also be estimated. We empirically illustrate our approach to estimate the Q model and also provide openly available code for R and Mplus.

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Handling Editor: Vincent Larivière

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