摘要：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.
