  Beliefs are important determinants of an individual's choices and economic
outcomes, so understanding how they comove and differ across individuals is of
considerable interest. Researchers often rely on surveys that report individual
beliefs as qualitative data. We propose using a Bayesian hierarchical latent
class model to analyze the comovements and observed heterogeneity in
categorical survey responses. We show that the statistical model corresponds to
an economic structural model of information acquisition, which guides
interpretation and estimation of the model parameters. An algorithm based on
stochastic optimization is proposed to estimate a model for repeated surveys
when responses follow a dynamic structure and conjugate priors are not
appropriate. Guidance on selecting the number of belief types is also provided.
Two examples are considered. The first shows that there is information in the
Michigan survey responses beyond the consumer sentiment index that is
officially published. The second shows that belief types constructed from
survey responses can be used in a subsequent analysis to estimate heterogeneous
returns to education.
