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Classification trees for multivariate ordinal response: an application to Student Evaluation Teaching

Author

Listed:
  • Mariangela Sciandra

    (University of Palermo)

  • Antonella Plaia

    (University of Palermo)

  • Vincenza Capursi

    (University of Palermo)

Abstract

Data from multiple items on an ordinal scale are commonly collected when qualitative variables, such as feelings, attitudes and many other behavioral and health-related variables are observed. In this paper we introduce a method to derive a distance-based tree for multivariate ordinal response that allows, when subject-specific characteristics are available, to derive common profiles for respondents giving the same/similar multivariate ratings. Special attention will be paid to the performance comparison in terms of AUC, for three different distances used as splitting criteria. Simulated data an a dataset from a Student Evaluation of Teaching survey will be used as illustrative examples. The latter will be used to show the performance of the procedure in profiling students by identifying which features of their experience are most closely related to their expressed satisfaction.

Suggested Citation

  • Mariangela Sciandra & Antonella Plaia & Vincenza Capursi, 2017. "Classification trees for multivariate ordinal response: an application to Student Evaluation Teaching," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 641-655, March.
    • Handle: RePEc:spr:qualqt:v:51:y:2017:i:2:d:10.1007_s11135-016-0430-2
    DOI: 10.1007/s11135-016-0430-2
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    References listed on IDEAS

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    1. Raffaella Piccarreta, 2008. "Classification trees for ordinal variables," Computational Statistics, Springer, vol. 23(3), pages 407-427, July.
    2. Mariano Ruiz Espejo, 2015. "Statistical Methods for Ranking Data," International Statistical Review, International Statistical Institute, vol. 83(1), pages 172-173, April.
    3. Agostino Tarsitano, 2009. "Comparing The Effectiveness Of Rank Correlation Statistics," Working Papers 200906, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    4. Shih, Yu-Shan, 2001. "Selecting the best splits for classification trees with categorical variables," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 341-345, October.
    5. Siciliano, Roberta & Mola, Francesco, 2000. "Multivariate data analysis and modeling through classification and regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 32(3-4), pages 285-301, January.
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    Cited by:

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    2. Gerhard Tutz, 2022. "Ordinal Trees and Random Forests: Score-Free Recursive Partitioning and Improved Ensembles," Journal of Classification, Springer;The Classification Society, vol. 39(2), pages 241-263, July.

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