Before revision: Data from surveys are increasingly available as the internet provides a new medium for conducting them. A typical survey consists of multiple questions, each with a menu of responses that are often categorical and qualitative in nature, and respondents are heterogeneous in both observed and unobserved ways. Existing methods that construct summary indices often ignore discreteness and do not provide adequately capture heterogeneity among individuals. We capture these features in a set of low dimensional latent variables using a Bayesian hierarchical latent class model that is adapted from probabilistic topic modeling of text data .
After revision: Beliefs are important determinants of an individual's choices and economic outcomes, so understanding how they differ across individuals is of considerable interest. Researchers often rely on surveys that report individual expectations as qualitative data. We propose using a Bayesian hierarchical latent class model that is adapted from probabilistic topic modeling of text data .