title: Item selection via Bayesian IRT models.
abstract: With reference to a questionnaire that aimed to assess the quality of life for dysarthric speakers, we investigate the usefulness of a model-based procedure for reducing the number of items. We propose a mixed cumulative logit model, which is known in the psychometrics literature as the graded response model: responses to different items are modelled as a function of individual latent traits and as a function of item characteristics, such as their difficulty and their discrimination power. We jointly model the discrimination and the difficulty parameters by using a k-component mixture of normal distributions. Mixture components correspond to disjoint groups of items. Items that belong to the same groups can be considered equivalent in terms of both difficulty and discrimination power. According to decision criteria, we select a subset of items such that the reduced questionnaire is able to provide the same information that the complete questionnaire provides. The model is estimated by using a Bayesian approach, and the choice of the number of mixture components is justified according to information criteria. We illustrate the proposed approach on the basis of data that are collected for 104 dysarthric patients by local health authorities in Lecce and in Milan.
authors.affiliation: Dipartimento di Metodi e Modelli per l'Economia, il Territorio e la Finanza, Sapienza Università di Roma, Rome, Italy.
authors.forename: Serena
authors.initials: S
authors.lastname: Arima
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D004401:Dysarthria / Q000523:psychology; D006801:Humans; D007558:Italy; D016015:Logistic Models; D025341:Principal Component Analysis; D011594:Psychometrics / Q000379:methods*; D011788:Quality of Life; D018434:Sickness Impact Profile; D011795:Surveys and Questionnaires
keywords: MCMC; item response model; item selection; mixture distribution