A hybrid approach for the analysis of complex categorical data structures: assessment of latent distance learning perception in higher education

Maria Iannario (), Alfonso Iodice D’Enza () and Rosaria Romano ()
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Maria Iannario: University of Naples Federico II
Alfonso Iodice D’Enza: University of Naples Federico II
Rosaria Romano: University of Naples Federico II

Computational Statistics, 2024, vol. 39, issue 1, No 9, 179 pages

Abstract: Abstract A long tradition of analysing ordinal response data deals with parametric models, which started with the seminal approach of cumulative models. When data are collected by means of Likert scale survey questions in which several scored items measure one or more latent traits, one of the sore topics is how to deal with the ordered categories. A stacked ensemble (or hybrid) model is introduced in the proposal to tackle the limitations of summing up the items. In particular, multiple items responses are synthesised into a single meta-item, defined via a joint data reduction approach; the meta-item is then modelled according to regression approaches for ordered polytomous variables accounting for potential scaling effects. Finally, a recursive partitioning method yielding trees provides automatic variable selection. The performance of the method is evaluated empirically by using a survey on Distance Learning perception.

Keywords: Distance learning; Location-scale model; Joint data reduction; Recursive partitioning for ordinal data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-022-01272-x

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