ABSTRACT
The linear factor analysis (FA) model is a popular tool for exploratory data analysis or, more precisely, for assessing the dimensionality of sets of items. Although it is well known that it is meant for continuous observed indicators, it is often used with dichotomous, ordinal, and other types of discrete variables, yielding results that might be incorrect. Not only parameter estimates may be biased, but also goodness-of-fit indices cannot be trusted. Magidson and Vermunt (2001) presented a nonlinear factor-analytic model based on latent class (LC) analysis that is especially suited for dealing with categorical indicators, such as dichotomous, ordinal, and nominal variables, and counts. The approach is called latent class factor analysis (LCFA) because it combines elements from L C and traditional FA. This LCFA model is one of the L C models implemented in the Latent GOLD program (Vermunt & Magidson, 2000, 2003).
