An Alternative to the Carnegie Classifications: Identifying Similar Doctoral Institutions With Structural Equation Models and Clustering

Paul Harmon, Sarah McKnight, Laura Hildreth, Ian Godwin and Mark Greenwood

Statistics and Public Policy, 2019, vol. 6, issue 1, 87-97

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.

Date: 2019
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DOI: 10.1080/2330443X.2019.1666761

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