A reparameterization of a latent class model is presented to simultaneously classify and scale nominal and ordered categorical choice data. Latent class-specific probabilities are constrained to be equal to the preference probabilities from a probabilistic ideal-point or vector model that yields a graphical, multidimensional representation of the classification results. In addition, background variables can be incorporated as an aid to interpreting the latent class-specific response probabilities. The analyses of synthetic and real data sets illustrate the proposed method.
|Original language||English (US)|
|Number of pages||18|
|State||Published - Dec 1 1991|
- latent class analysis
- multidimensional scaling
ASJC Scopus subject areas
- Applied Mathematics