Latent Variable Modeling of Scientific Impact: Estimation of the Q Model Parameters with Structural Equation Models

IF 5.4 3区 材料科学 Q2 CHEMISTRY, PHYSICAL
Boris Forthmann, Steffen Nestler
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引用次数: 0

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. https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00313
科学影响力的潜在变量建模:用结构方程模型估计 Q 模型参数
科学生产力和影响力的统计建模为文献计量学提供了深入的见解,也用于量化学者个体之间的差异。Q 模型将已发表论文的对数转换影响力分解为研究人员能力参数和随机运气参数。然后通过三变量正态分布将这两个参数与对数变换后的已发表论文数量(即生产力指标)一起建模。在这项工作中,我们提出了一种可以作为结构方程模型估算的 Q 模型。将 Q 模型作为结构方程模型,可以量化研究人员对 Q 参数估计的可靠性,还可以将其扩展到包含人的协变量,并对 Q 模型的多变量扩展进行估计。我们通过经验说明了我们估计 Q 模型的方法,并提供了 R 和 Mplus 的公开代码。https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00313。
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来源期刊
ACS Applied Energy Materials
ACS Applied Energy Materials Materials Science-Materials Chemistry
CiteScore
10.30
自引率
6.20%
发文量
1368
期刊介绍: ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.
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