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Statistical tools for student evaluation of academic educational quality

Author

Listed:
  • Violetta Simonacci

    (University of Naples L’Orientale)

  • Michele Gallo

    (University of Naples L’Orientale)

Abstract

Measuring academic educational quality presents three major difficulties, typical of all customer satisfaction and service quality studies: the use of subjective scales; the ordinal nature of the data; and the multifold structure of satisfaction. In order to solve these problems, principal component analysis (PCA) of compositional data is proposed in this work. The core idea behind this methodology is to analyze by PCA the relative information within the data rather than focusing on absolute scores. This approach is discussed in comparison with a widely used Item Response Theory method (the Partial Credit Model) in order to assess its merits, e.g. always identifying a coherent preference structure. Both procedures were, thus, carried out on a real dataset collected with the 2013/14 ANVUR questionnaire by L’Universitá di Napoli-L’Orientale.

Suggested Citation

  • Violetta Simonacci & Michele Gallo, 2017. "Statistical tools for student evaluation of academic educational quality," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 565-579, March.
    • Handle: RePEc:spr:qualqt:v:51:y:2017:i:2:d:10.1007_s11135-016-0425-z
    DOI: 10.1007/s11135-016-0425-z
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    References listed on IDEAS

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    2. Ron S. Kenett & Silvia Salini, 2011. "Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 27(5), pages 465-475, September.
    3. J. Kruskal, 1964. "Nonmetric multidimensional scaling: A numerical method," Psychometrika, Springer;The Psychometric Society, vol. 29(2), pages 115-129, June.
    4. David Andrich, 1978. "A rating formulation for ordered response categories," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 561-573, December.
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    Cited by:

    1. Violetta Simonacci & Michele Gallo, 2024. "Three-way principal balance analysis: algorithm and interpretation," Annals of Operations Research, Springer, vol. 342(3), pages 1429-1443, November.
    2. Maria Anna Di Palma & Michele Gallo, 2019. "External Information Model in a Compositional Perspective: Evaluation of Campania Adolescents’ Preferences in the Allocation of Leisure-Time," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 117-133, November.

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