[size=14.944px]Abstract
[size=14.944px]In analyzing data deriving from the administration of a questionnaire to a
group of individuals, Item Response Theory (IRT) models provide a flexible
framework to account for several aspects involved in the response process,
such as the existence of multiple latent traits. In this paper, we focus on a class
of semi-parametric multidimensional IRT models, in which these traits are represented
through one or more discrete latent variables; these models allow us to cluster individuals
into homogeneous latent classes and, at the same time, to properly study item characteristics.
In particular, we follow a within-item multidimensional formulation similar to that adopted
in the two-tier models, with each item measuring one or two latent traits. The proposed class of
models may be estimated through the package MLCIRTwithin, whose functioning is illustrated
in this paper with examples based on data about quality-of-life measurement and
about the propensity to commit a crime.
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