Clustering Ordinal Data via Latent Variable Models

Item response modelling is a well established method for analysing ordinal response data. Ordinal data are typically collected as responses to a number
of questions or items. The observed data can be viewed as discrete versions of an
underlying latent Gaussian variable. Item response models assume that this latent
variable (and therefore the observed ordinal response) is a function of both respondent specific and item specific parameters. However, item response models assume
a homogeneous population in that the item specific parameters are assumed to be
the same for all respondents. Often a population is heterogeneous and clusters of
respondents exist; members of different clusters may view the items differently. A
mixture of item response models is developed to provide clustering capabilities in
the context of ordinal response data. The model is estimated within the Bayesian
paradigm and is illustrated through an application to an ordinal response data set
resulting from a clinical trial involving self-assessment of arthritis.