Abstract The Carnegie Classification of Institutions of Higher Education is a commonly used framework for institutional classification that classifies doctoral-granting schools into three groups based on research productivity. Despite its wide use, the Carnegie methodology involves several shortcomings, including a lack of thorough documentation, subjectively placed thresholds between institutions, and a methodology that is not completely reproducible. We describe the methodology of the 2015 and 2018 updates to the classification and propose an alternative method of classification using the same data that relies on structural equation modeling (SEM) of latent factors rather than principal component-based indices of productivity. In contrast to the Carnegie methodology, we use SEM to obtain a single factor score for each school based on latent metrics of research productivity. Classifications are then made using a univariate model-based clustering algorithm as opposed to subjective thresholding, as is done in the Carnegie methodology. Finally, we present a Shiny web application that demonstrates sensitivity of both the Carnegie Classification and SEM-based classification of a selected university and generates a table of peer institutions in line with the stated goals of the Carnegie Classification.

中文翻译：

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卡耐基分类的替代方法：使用结构方程模型和聚类识别相似的机构

摘要卡内基高等教育机构分类法是一种常用的机构分类框架，它根据研究生产率将授予博士学位的学校分为三类。尽管卡耐基方法学得到了广泛使用，但它仍存在一些缺点，包括缺乏详尽的文档，各机构之间主观地设定了门槛以及无法完全重现的方法学。我们描述了2015年和2018年更新分类的方法，并提出了使用相同数据的替代分类方法，该数据依赖于潜在因素的结构方程模型（SEM）而不是基于生产率的主成分指标。与卡内基方法论相反，我们使用SEM根据潜在的研究生产力指标为每所学校获得单因素得分。然后使用卡内基方法中的基于主变量的单变量基于模型的聚类算法进行分类。最后，我们提供了一个Shiny Web应用程序，该应用程序展示了所选大学的卡内基分类和基于SEM的分类的敏感性，并根据卡内基分类的既定目标生成了同等机构的表格。