Events and Meetings of Italian Statistical Society, Advances in Latent Variables - Methods, Models and Applications

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A classification of university courses based on students satisfaction: an application of a three-level mixture Item Response Theory (IRT) model
Silvia Bacci, Michela Gnaldi

Last modified: 2013-06-16

Abstract


Students' opinions about a few aspects of academic life are sought by Italian universities in the form of a satisfaction feedback questionnaire. The aim of this paper is to classify university courses in homogeneous classes with respect to the level of students' satisfaction through the use of a three-level mixture Item Response Theory (IRT) model. The data are drawn from the Italian questionnaire on students' satisfaction administered at a Faculty of Political Sciences. The latent variables measured by the questionnaire are detected performing a model-based hierarchical clustering and a confirmatory factor analysis. Then, a special case of multilevel mixture factor model characterized by an IRT parametrization and discrete latent variables at all hierarchical levels is estimated. Findings indicate that (i) satisfaction with higher education courses is a bi-dimensional construct loading on two main latent dimensions; (ii) the covariates have different effects on the two dimensions; and (iii) worst and best courses on each or both the latent traits are identified.

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