How does one determine the number of latent classes?
The primary method is to statistically assess latent class models with 2, 3, …, up to the maximum plausible number of latent classes, and to statistically assess the fit of each one to the data. In general, as the number of classes becomes fewer, models fit the data worse, and a point is reached after which models are rejected by the G2 criterion. A more computation-intensive approach relies on bootstrapping, Monte Carlo, or similar methods (see Aitken et al, 1981; and especially Langeheine et al., 1996 and van der Heijden et al, 1997). These methods require no assumptions about the data such as those required for chi-squared tests. Other, more “heuristic” methods include use of parsimony indices (AIC, BIC or CAIC), a “scree”-type test (where one plots model fit against number of latent classes, and looks for a leveling-off point of the curve), and examination of parameter estimates (for example, one might reject models as having too many latent classes if some latent classes are ass
