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A biclustering approach to university performances: an Italian case study

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  • Valentina Raponi
  • Francesca Martella
  • Antonello Maruotti

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 often measured by means of indicator variables. The available information are then summarized to respond to different aims. We argue that the evaluation process is a complex phenomenon that cannot be addressed by a simple descriptive approach. In this paper, we used a model-based approach to account for association between indicators and similarities among the observed universities. 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 methodology, 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.

Suggested Citation

  • Valentina Raponi & Francesca Martella & Antonello Maruotti, 2016. "A biclustering approach to university performances: an Italian case study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(1), pages 31-45, January.
  • Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:31-45
    DOI: 10.1080/02664763.2015.1009005
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    Cited by:

    1. Marco Centoni & Vieri Del Panta & Antonello Maruotti & Valentina Raponi, 2019. "Concomitant-Variable Latent-Class Beta Inflated Models to Assess Students’ Performance: An Italian Case Study," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 7-18, November.
    2. Telcs, András & Kosztyán, Zsolt Tibor & Banász, Zsuzsanna & Csányi, Vivien Valéria, 2019. "Felsőoktatási ligák, parciális rangsorok képzése biklaszterezési eljárásokkal [How to rate higher education systems partial rankings using bi-clustering methods]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(9), pages 905-931.
    3. Esteban Fernández Tuesta & Máxima Bolaños-Pizarro & Daniel Pimentel Neves & Geziel Fernández & Justin Axel-Berg, 2020. "Complex networks for benchmarking in global universities rankings," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 405-425, October.
    4. Zsuzsanna Banász & Zsolt T. Kosztyán & Vivien V. Csányi & András Telcs, 2023. "University leagues alongside rankings," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 721-736, February.

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