Abstract
ABSTRACTUniversity 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.
Highlights
The Carnegie Classification (CC), under the auspices of Indiana University Bloomington’s Center for Postsecondary Research (Indiana University 2016), is one of the oldest regularly published rankings of university programs and reputations for doctorate-granting universities
Doctorates are split into four categories: humanities, social science, STEM, and “professional.” These seven variables are each transformed to ranks, which reduces the influence of extreme skewness and outliers
If a normal mixture distribution is fitted using the mclust (Fraley and Raftery 2002) package, having three clusters is determined to be optimal from an information criterion perspective; the three clusters are quite different from the three clusters given by the Carnegie Foundation and k-means clustering, as can be seen in the bottom left panel of Figure 6
Summary
The Carnegie Classification (CC), under the auspices of Indiana University Bloomington’s Center for Postsecondary Research (Indiana University 2016), is one of the oldest regularly published rankings of university programs and reputations for doctorate-granting universities. USNWR uses the institutional categories as defined by the Carnegie Foundation Both USNWR and Carnegie create scores based upon a data matrix. The latest scheme used in the 2005, 2010, and 2015 iterations of the Carnegie Classification is much more sophisticated than the earlier versions It uses a more diverse set of variables and advanced statistical techniques to characterize schools. We propose and examine two modifications that we believe are consistent with Carnegie’s stated goals, more faithfully represent the structure of the data, and offer organizational advantages We evaluate their impact on the final clustering. We discuss the relative merits of these alternatives and Carnegie’s technique
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