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How does the normalization of data affect the ARWU ranking?

Author

Listed:
  • Milica Jovanovic

    (University of Belgrade)

  • Veljko Jeremic

    (University of Belgrade)

  • Gordana Savic

    (University of Belgrade)

  • Milica Bulajic

    (University of Belgrade)

  • Milan Martic

    (University of Belgrade)

Abstract

The aim of this paper is to present new ideas in evaluating Shanghai University’s Academic Ranking of World Universities (ARWU). In particular, this paper shall try to determine whether the normalization of data affects University ranks. In accordance with this, both the normalized and original (raw) data for each of the six variables has been obtained. Based on a sample containing the 54 US universities which are placed in the ARWU top 100, the statistical I-distance method was performed. The results showed great inconsistencies between university ranks obtained for the original and normalized data. These findings were then analyzed and the universities that had the greatest fluctuation in their ranks were noted.

Suggested Citation

  • Milica Jovanovic & Veljko Jeremic & Gordana Savic & Milica Bulajic & Milan Martic, 2012. "How does the normalization of data affect the ARWU ranking?," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 319-327, November.
    • Handle: RePEc:spr:scient:v:93:y:2012:i:2:d:10.1007_s11192-012-0674-0
    DOI: 10.1007/s11192-012-0674-0
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    References listed on IDEAS

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    2. Milica Maricic & Jose A. Egea & Veljko Jeremic, 2019. "A Hybrid Enhanced Scatter Search—Composite I-Distance Indicator (eSS-CIDI) Optimization Approach for Determining Weights Within Composite Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(2), pages 497-537, July.
    3. Enar Ruiz-Conde & Aurora Calderón-Martínez, 2014. "University institutional repositories: competitive environment and their role as communication media of scientific knowledge," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1283-1299, February.
    4. Milica Maricic & Milica Kostic-Stankovic, 2016. "Towards an impartial Responsible Competitiveness Index: a twofold multivariate I-distance approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 103-120, January.
    5. Seyithan Ahmet Ates & Kursad Derinkuyu, 2021. "Green growth and OECD countries: measurement of country performances through distance-based analysis (DBA)," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(10), pages 15062-15073, October.
    6. Dobrota, Marina & Martic, Milan & Bulajic, Milica & Jeremic, Veljko, 2015. "Two-phased composite I-distance indicator approach for evaluation of countries’ information development," Telecommunications Policy, Elsevier, vol. 39(5), pages 406-420.
    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. Maričić Milica & Bulajić Milica & Radojičić Zoran & Jeremić Veljko, 2019. "Shedding Light on the Doing Business Index: a Machine Learning Approach," Business Systems Research, Sciendo, vol. 10(2), pages 73-84, September.
    9. M. Marković & S. Zdravković & M. Mitrović & A. Radojičić, 2016. "An Iterative Multivariate Post Hoc I-Distance Approach in Evaluating OECD Better Life Index," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 126(1), pages 1-19, March.

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    More about this item

    Keywords

    Ranking of universities; The I-distance method; ARWU; Statistical methods; Classification; Normalization of data;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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