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University leagues alongside rankings

Author

Listed:
  • Zsuzsanna Banász

    (University of Pannonia
    MTA-PE Budapest Ranking Research Group, Eötvös Loránd Research Network (ELKH))

  • Zsolt T. Kosztyán

    (MTA-PE Budapest Ranking Research Group, Eötvös Loránd Research Network (ELKH)
    University of Pannonia
    Institute of Advanced Studies, Koszeg
    Research Centre of Faculty of Business and Economics University of Pannonia)

  • Vivien V. Csányi

    (University of Pannonia)

  • András Telcs

    (MTA-PE Budapest Ranking Research Group, Eötvös Loránd Research Network (ELKH)
    University of Pannonia
    MTA Wigner Research Center for Physics
    Budapest University of Technology and Economics)

Abstract

University rankings can both orient and disorient potential students. In rankings, universities with very different characteristics are compared, which makes interpretation difficult. We propose the application of a clustering method, which creates groups of universities that are close to each other with respect to a subset of indicators, but the indicators also show homogeneity with respect to the universities in that group. We call such groups leagues. These leagues are defined by the data themselves and are not based on subjective criteria. We demonstrate our proposition using one member of the family of the two-way clustering method, namely, biclustering. The case we present is based on the Round University Ranking (RUR) 2020 dataset. The use of leagues could provide better guidance not only for potential applicants but also for university funding organizations and policy-makers. Our case study led to a somewhat surprising observation. In the top league (based on the RUR data and indicators), the three most important indicators measure reputation, not scientific or educational performance.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:qualqt:v:57:y:2023:i:1:d:10.1007_s11135-022-01374-0
    DOI: 10.1007/s11135-022-01374-0
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    References listed on IDEAS

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    1. 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.
    2. Cinzia Daraio & Andrea Bonaccorsi, 2017. "Beyond university rankings? Generating new indicators on universities by linking data in open platforms," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(2), pages 508-529, February.
    3. Jill Johnes, 2018. "University rankings: What do they really show?," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 585-606, April.
    4. Horstschräer, Julia, 2012. "University rankings in action? The importance of rankings and an excellence competition for university choice of high-ability students," Economics of Education Review, Elsevier, vol. 31(6), pages 1162-1176.
    5. Francesca Petrarca & Silvia Terzi, 2018. "The Global Competitiveness Index: an alternative measure with endogenously derived weights," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(5), pages 2197-2219, September.
    6. Guneren Genc, Elif & Deniz Basar, Ozlem, 2019. "Comparison of Country Ratings of Credit Rating Agencies with MOORA Method," Business and Economics Research Journal, Uludag University, Faculty of Economics and Administrative Sciences, vol. 10(2), pages 391-404, April.
    7. Griffith, Amanda & Rask, Kevin, 2007. "The influence of the US News and World Report collegiate rankings on the matriculation decision of high-ability students: 1995-2004," Economics of Education Review, Elsevier, vol. 26(2), pages 244-255, April.
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