Latent Variable Modeling of Scientific Impact: Estimation of the Q Model Parameters with Structural Equation Models
Quantitative Science Studies(2024)
摘要
Abstract 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 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. Peer Review https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00313
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