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Analysis of university course evaluations: from descriptive measures to multilevel models

Author

Listed:
  • Carla Rampichini

    (Dipartimento di Statistica “G. Parenti”)

  • Leonardo Grilli

    (Dipartimento di Statistica “G. Parenti”)

  • Alessandra Petrucci

    (Dipartimento di Statistica “G. Parenti”)

Abstract

. In the paper we present a comprehensive methodology for the analysis of student ratings of university courses. First, simple descriptive measures, which take into account the ordinal nature of the ratings, are discussed. Then net measures, which adjust for the characteristics of the students, are obtained through multilevel modelling. Finally, the measures relative to the various aspects of the course are synthesized through a weighted mean, building gross or net multidimensional indicators of course quality. The different indicators are then contrasted with respect to the rankings of courses they induce.

Suggested Citation

  • Carla Rampichini & Leonardo Grilli & Alessandra Petrucci, 2004. "Analysis of university course evaluations: from descriptive measures to multilevel models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 13(3), pages 357-373, December.
    • Handle: RePEc:spr:stmapp:v:13:y:2004:i:3:d:10.1007_s10260-004-0087-1
    DOI: 10.1007/s10260-004-0087-1
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    Citations

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    Cited by:

    1. Marco Guerra & Francesca Bassi & José G. Dias, 2020. "A Multiple-Indicator Latent Growth Mixture Model to Track Courses with Low-Quality Teaching," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 361-381, January.
    2. Arpino, Bruno & Varriale, Roberta, 2009. "Assessing the quality of institutions’ rankings obtained through multilevel linear regression models," MPRA Paper 19873, University Library of Munich, Germany.
    3. Bruno ARPINO & Roberta VARRIALE, 2010. "Assessing The Quality Of Institutions’ Rankings Obtained Through Multilevel Linear Regression Models," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 5(1(11)_Spr), pages 7-22.
    4. Pier Ferrari & Laura Pagani & Carlo Fiorio, 2011. "A Two-Step Approach to Analyze Satisfaction Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 104(3), pages 545-554, December.
    5. Isabella Sulis & Mariano Porcu, 2012. "Comparing degree programs from students’ assessments: A LCRA-based adjusted composite indicator," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(2), pages 193-209, June.
    6. Michele La Rocca & Maria Lucia Parrella & Ilaria Primerano & Isabella Sulis & Maria Prosperina Vitale, 2017. "An integrated strategy for the analysis of student evaluation of teaching: from descriptive measures to explanatory models," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 675-691, March.
    7. Isabella Sulis & Mariano Porcu & Vincenza Capursi, 2019. "On the Use of Student Evaluation of Teaching: A Longitudinal Analysis Combining Measurement Issues and Implications of the Exercise," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(3), pages 1305-1331, April.
    8. Annalina Sarra & Adelia Evangelista & Barbara Iannone & Tonio Battista, 2023. "Looking for patterns of change amid pandemic period in students’ evaluation of academic teaching," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4759-4777, October.
    9. Giorgio E. Montanari & Marco Doretti, 2019. "Ranking Nursing Homes’ Performances Through a Latent Markov Model with Fixed and Random Effects," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 307-326, November.

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