  Factor analysis is a flexible technique for assessment of multivariate
dependence and codependence. Besides being an exploratory tool used to reduce
the dimensionality of multivariate data, it allows estimation of common factors
that often have an interesting theoretical interpretation in real problems.
However, standard factor analysis is only applicable when the variables are
scaled, which is often inappropriate, for example, in data obtained from
questionnaires in the field of psychology,where the variables are often
categorical. In this framework, we propose a factor model for the analysis of
multivariate ordered and non-ordered polychotomous data. The inference
procedure is done under the Bayesian approach via Markov chain Monte Carlo
methods. Two Monte-Carlo simulation studies are presented to investigate the
performance of this approach in terms of estimation bias, precision and
assessment of the number of factors. We also illustrate the proposed method to
analyze participants' responses to the Motivational State Questionnaire
dataset, developed to study emotions in laboratory and field settings.
