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Factor analysis is a flexible technique for assessment of multivariatedependence and codependence. Besides being an exploratory tool used to reducethe dimensionality of multivariate data, it allows estimation of common factorsthat often have an interesting theoretical interpretation in real problems.However, standard factor analysis is only applicable when the variables arescaled, which is often inappropriate, for example, in data obtained fromquestionnaires in the field of psychology,where the variables are oftencategorical. In this framework, we propose a factor model for the analysis ofmultivariate ordered and non-ordered polychotomous data. The inferenceprocedure is done under the Bayesian approach via Markov chain Monte Carlomethods. Two Monte-Carlo simulation studies are presented to investigate theperformance of this approach in terms of estimation bias, precision andassessment of the number of factors. We also illustrate the proposed method toanalyze participants' responses to the Motivational State Questionnairedataset, developed to study emotions in laboratory and field settings.

Bayesian factor models for multivariate categorical data obtained from questionnaires