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How can differences in international university rankings be explained?

Author

Listed:
  • Fredrik Niclas Piro

    (Nordic Institute for Studies in Innovation, Research and Education (NIFU))

  • Gunnar Sivertsen

    (Nordic Institute for Studies in Innovation, Research and Education (NIFU))

Abstract

University rankings are typically presenting their results as league tables with more emphasis on final scores and positions, than on the clarification of why the universities are ranked as they are. Finding out the latter is often not possible, because final scores are based on weighted indicators where raw data and the processing of these are not publically available. In this study we use a sample of Scandinavian universities, explaining what is causing differences between them in the two most influential university rankings: Times Higher Education and the Shanghai-ranking. The results show that differences may be attributed to both small variations on what we believe are not important indicators, as well as substantial variations on what we believe are important indicators. The overall aim of this paper is to provide a methodology that can be used in understanding universities’ different ranks in global university rankings.

Suggested Citation

  • Fredrik Niclas Piro & Gunnar Sivertsen, 2016. "How can differences in international university rankings be explained?," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 2263-2278, December.
    • Handle: RePEc:spr:scient:v:109:y:2016:i:3:d:10.1007_s11192-016-2056-5
    DOI: 10.1007/s11192-016-2056-5
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    References listed on IDEAS

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    1. Domingo Docampo & Lawrence Cram, 2015. "On the effects of institutional size in university classifications: the case of the Shanghai ranking," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1325-1346, February.
    2. Fred L. Bookstein & Horst Seidler & Martin Fieder & Georg Winckler, 2010. "Too much noise in the Times Higher Education rankings," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 295-299, October.
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    Citations

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    Cited by:

    1. Michael McAleer & Tamotsu Nakamura & Clinton Watkins, 2019. "Size, Internationalization, and University Rankings: Evaluating and Predicting Times Higher Education (THE) Data for Japan," Sustainability, MDPI, vol. 11(5), pages 1-12, March.
    2. McAleer, M.J. & Nakamura, T. & Watkins, C., 2018. "Size, Internationalization and University Rankings: Evaluating Times Higher Education (THE) Data for Japan," Econometric Institute Research Papers EI2018-43, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Gerhard Reichmann & Christian Schlögl, 2022. "On the possibilities of presenting the research performance of an institute over a long period of time: the case of the Institute of Information Science at the University of Graz in Austria," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3193-3223, June.
    4. Mehdi Rhaiem & Nabil Amara, 2020. "Determinants of research efficiency in Canadian business schools: evidence from scholar-level data," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 53-99, October.
    5. Vicente Safón & Domingo Docampo, 2020. "Analyzing the impact of reputational bias on global university rankings based on objective research performance data: the case of the Shanghai Ranking (ARWU)," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2199-2227, December.
    6. Lutz Bornmann & Werner Marx, 2020. "Thomas theorem in research evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 553-555, April.
    7. Antonio Fernández-Cano & Elvira Curiel-Marin & Manuel Torralbo-Rodríguez & Mónica Vallejo-Ruiz, 2018. "Questioning the Shanghai Ranking methodology as a tool for the evaluation of universities: an integrative review," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 2069-2083, September.
    8. Wu, Jiang & Ou, Guiyan & Liu, Xiaohui & Dong, Ke, 2022. "How does academic education background affect top researchers’ performance? Evidence from the field of artificial intelligence," Journal of Informetrics, Elsevier, vol. 16(2).
    9. Ernesto Galbán-Rodríguez & Deborah Torres-Ponjuán & Ricardo Arencibia-Jorge, 2021. "Multidimensional quantitative analysis of the Cuban scientific output and its regional context," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2643-2665, March.
    10. Dag W. Aksnes & Liv Langfeldt & Paul Wouters, 2019. "Citations, Citation Indicators, and Research Quality: An Overview of Basic Concepts and Theories," SAGE Open, , vol. 9(1), pages 21582440198, February.
    11. Cinzia Daraio, 2017. "A framework for the Assessment of Research and its impacts," DIAG Technical Reports 2017-04, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    12. Gauffriau, Marianne, 2017. "A categorization of arguments for counting methods for publication and citation indicators," Journal of Informetrics, Elsevier, vol. 11(3), pages 672-684.
    13. Frenken, Koen & Heimeriks, Gaston J. & Hoekman, Jarno, 2017. "What drives university research performance? An analysis using the CWTS Leiden Ranking data," Journal of Informetrics, Elsevier, vol. 11(3), pages 859-872.
    14. Vicente Safón, 2019. "Inter-ranking reputational effects: an analysis of the Academic Ranking of World Universities (ARWU) and the Times Higher Education World University Rankings (THE) reputational relationship," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 897-915, November.

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    University rankings; Performance;

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