How are cases classified?
Once the latent class model is estimated, cases can be classified to their most likely latent class by means of _recruitment probabilities_. A recruitment probability is the probability that, for a randomly selected member of a given latent class, a given response pattern will be observed. The recruitment probabilities are calculated from the estimated conditional response probabilities in a straightforward way (see, e.g., Lazarsfeld & Henry, 1968). From the recruitment probabilities, the estimated prevalence of each latent class, and Bayes’ theorem, one easily calculates the a posteriori probability of a case’s membership in each class. One may then assign the case to the latent class with the highest a posteriori probability (modal assignment), or leave classification “fuzzy”–i.e, view the case as belonging probabilistically to each latent class to the degree indicated.
