ABSTRACT University ranking is a popular yet controversial endeavor. Most rankings are based on both public data, such as student test scores and retention rates, and proprietary data, such as school reputation as perceived by high school counselors and academic peers. The weights applied to these characteristics to compute the rankings are often determined in a subjective fashion. Of significant importance in the academic field, the Carnegie Classification was developed by the Carnegie Foundation for the Advancement of Teaching. It has been updated approximately every 5 years since 1973, most recently in February 2016. Based on bivariate scores, Carnegie assigns one of three classes (R1/R2/R3) to doctorate-granting universities according to their level of research activity. The Carnegie methodology uses only publicly available data and determines weights via principal component analysis. In this article, we review Carnegie’s stated goals and the extent to which their methodology achieves those goals. In particular, we examine Carnegie’s separation of aggregate and per capita (per tenured/tenure-track faculty member) variables and its use of two separate principal component analyses on each; the resulting bivariate scores are very highly correlated. We propose and evaluate two alternatives and provide a graphical tool for evaluating and comparing the three scenarios.

中文翻译：

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研究研究型大学的卡内基分类方法

摘要大学排名是一项颇受争议的热门尝试。大多数排名都是基于公共数据（例如学生考试成绩和保留率）以及专有数据（例如高中辅导员和学术同龄人认为的学校声誉）得出的。通常以主观方式确定应用于这些特征以计算排名的权重。在学术领域非常重要的是，卡内基分类法是由卡内基促进教学基金会制定的。自1973年以来，它大约每5年更新一次，最近一次是在2016年2月。根据双变量评分，卡内基根据研究活动的水平，为授予博士学位的大学分配三个类别（R1 / R2 / R3）之一。卡内基方法仅使用公开可用的数据，并通过主成分分析确定权重。在本文中，我们将回顾卡内基的既定目标及其方法达到这些目标的程度。特别是，我们研究了卡耐基对总变量和人均变量（按终身任职/终身制教师人数）的分离，以及对每个变量的两个独立主成分分析的使用；由此产生的双变量得分具有很高的相关性。我们提出并评估了两种选择，并提供了一种评估和比较这三种情况的图形工具。我们研究了卡耐基对总变量和人均变量（按终身任职/终身制教师的比例）的分离，以及对每个变量的两个独立主成分分析的使用；由此产生的双变量得分具有很高的相关性。我们提出并评估了两种选择，并提供了一种评估和比较这三种情况的图形工具。我们研究了卡耐基对总变量和人均变量（按终身任职/终身制教师的比例）的分离，以及对每个变量的两个独立主成分分析的使用；由此产生的双变量得分具有很高的相关性。我们提出并评估了两种选择，并提供了一种评估和比较这三种情况的图形工具。