Item selection by latent class-based methods: an application to nursing home evaluation

Francesco Bartolucci, Giorgio E. Montanari () and Silvia Pandolfi
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Giorgio E. Montanari: University of Perugia

Advances in Data Analysis and Classification, 2016, vol. 10, issue 2, No 8, 245-262

Abstract: Abstract The evaluation of nursing homes is usually based on the administration of questionnaires made of a large number of polytomous items to their patients. In such a context, the latent class model represents a useful tool for clustering subjects in homogenous groups corresponding to different degrees of impairment of the health conditions. It is known that the performance of model-based clustering and the accuracy of the choice of the number of latent classes may be affected by the presence of irrelevant or noise variables. In this paper, we show the application of an item selection algorithm to a dataset collected within a project, named ULISSE, on the quality-of-life of elderly patients hosted in Italian nursing homes. This algorithm, which is closely related to that proposed by Dean and Raftery in 2010, is aimed at finding the subset of items which provides the best clustering according to the Bayesian Information Criterion. At the same time, it allows us to select the optimal number of latent classes. Given the complexity of the ULISSE study, we perform a validation of the results by means of a sensitivity analysis, with respect to different specifications of the initial subset of items, and of a resampling procedure.

Keywords: Bayesian information criterion; Expectation-maximization algorithm; Polytomous items; Quality-of-life; ULISSE project; 62H30 Classification and discrimination; cluster analysis, 62P25 Applications to social sciences (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11634-016-0232-3

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