A biclustering approach to university performances: an Italian case study

TL;DR

This paper introduces a biclustering method to analyze university performance data, revealing distinct private and public university profiles and emphasizing the importance of considering complex data structures in evaluations.

Contribution

It presents a novel biclustering approach that captures both similarities among faculties and correlations between indicators in university performance analysis.

Findings

Two main university performance groups identified: Private and Public.

Each group exhibits unique features, priorities, strengths, and weaknesses.

Standard rankings may overlook complex data structures, leading to misinterpretation.

Abstract

University evaluation is a topic of increasing concern in Italy as well as in other countries. In empirical analysis, university activities and performances are generally measured by means of indicator variables, summarizing the available information under different perspectives. In this paper, we argue that the evaluation process is a complex issue that can not be addressed by a simple descriptive approach and thus association between indicators and similarities among the observed universities should be accounted for. Particularly, we examine faculty-level data collected from different sources, covering 55 Italian Economics faculties in the academic year 2009/2010. Making use of a clustering framework, we introduce a biclustering model that accounts for both homogeneity/heterogeneity among faculties and correlations between indicators. Our results show that there are two substantial different performances between universities which can be strictly related to the nature of the institutions, namely the Private and Public profiles . Each of the two groups has its own peculiar features and its own group-specific list of priorities, strengths and weaknesses. Thus, we suggest that caution should be used in interpreting standard university rankings as they generally do not account for the complex structure of the data.