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Improving quality assessment of composite indicators in university rankings: a case study of French and German universities of excellence

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  • M. Benito

    (Universidad Carlos III de Madrid)

  • R. Romera

    (Universidad Carlos III de Madrid)

Abstract

Composite indicators play an essential role for benchmarking higher education institutions. One of the main sources of uncertainty building composite indicators and, undoubtedly, the most debated problem in building composite indicators is the weighting schemes (assigning weights to the simple indicators or subindicators) together with the aggregation schemes (final composite indicator formula). Except the ideal situation where weights are provided by the theory, there clearly is a need for improving quality assessment of the final rank linked with a fixed vector of weights. We propose to use simulation techniques to generate random perturbations around any initial vector of weights to obtain robust and reliable ranks allowing to rank universities in a range bracket. The proposed methodology is general enough to be applied no matter the weighting scheme used for the composite indicator. The immediate benefit achieved is a reduction of the uncertainty associated with the assessment of a specific rank which is not representative of the real performance of the university, and an improvement of the quality assessment of composite indicators used to rank. To illustrate the proposed methodology we rank the French and the German universities involved in their respective 2008 Excellence Initiatives.

Suggested Citation

  • M. Benito & R. Romera, 2011. "Improving quality assessment of composite indicators in university rankings: a case study of French and German universities of excellence," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 153-176, October.
    • Handle: RePEc:spr:scient:v:89:y:2011:i:1:d:10.1007_s11192-011-0419-5
    DOI: 10.1007/s11192-011-0419-5
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    References listed on IDEAS

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    1. Marco Geraci & M. Degli Esposti, 2011. "Where do Italian universities stand? An in-depth statistical analysis of national and international rankings," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 667-681, June.
    2. Jingda Ding & Junping Qiu, 2011. "An approach to improve the indicator weights of scientific and technological competitiveness evaluation of Chinese universities," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(2), pages 285-297, February.
    3. M. Saisana & A. Saltelli & S. Tarantola, 2005. "Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 307-323, March.
    4. Isidro F. Aguillo & Judit Bar-Ilan & Mark Levene & José Luis Ortega, 2010. "Comparing university rankings," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 243-256, October.
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    3. Giménez, Víctor & Thieme, Claudio & Prior, Diego & Tortosa-Ausina, Emili, 2022. "Evaluation and determinants of preschool effectiveness in Chile," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    4. M. M. Segovia-González & I. Contreras, 2023. "A Composite Indicator to Compare the Performance of Male and Female Students in Educational Systems," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 165(1), pages 181-212, January.
    5. Elio Atenógenes Villaseñor & Ricardo Arencibia-Jorge & Humberto Carrillo-Calvet, 2017. "Multiparametric characterization of scientometric performance profiles assisted by neural networks: a study of Mexican higher education institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 77-104, January.
    6. Yelin Fu & Kong Xiangtianrui & Hao Luo & Lean Yu, 2020. "Constructing Composite Indicators with Collective Choice and Interval-Valued TOPSIS: The Case of Value Measure," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 117-135, November.
    7. Alfio Ferrara & Silvia Salini, 2012. "Ten challenges in modeling bibliographic data for bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 765-785, December.
    8. ZAHARIEV, Boyan & BOYADJIEVA, Pepka, 2012. "The Impact Of Weighting Preferences On University Rankings: The Example Of Bulgaria," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 12(3).
    9. Yang Ding & Yelin Fu & Kin Keung Lai & W. K. John Leung, 2018. "Using Ranked Weights and Acceptability Analysis to Construct Composite Indicators: A Case Study of Regional Sustainable Society Index," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 139(3), pages 871-885, October.
    10. M. Ryan Haley, 2020. "Combining the weighted and unweighted Euclidean indices: a graphical approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 103-111, April.

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