The authors illustrate how to perform maximum-likelihood estimation in latent class (LC) analysis when there are sampling weights. The methods are natural extensions of the approaches proposedby Clogg and Eliason (1987) and Magidson (1987) for dealing with sampling weights in the log-linear analysis of frequency tables. For the log-linear form of the LC model, the approachcorresponds to a special case of Haberman’s (1979) log-linear LC model with cell weights. This approach can also be applied to the probability formulation of the LC model with cell weights,which can accommodate many indicators. The authors propose an efficient estimation-maximization algorithm for estimating the parameters for this formulation. A small simulation study shows that the probability estimates obtained by this approach compare favorably to other weighting approaches. Several empirical examples are provided to illustrate various possible weighting methods in LC analysis.
Key Words: latent class analysis • mixture model • complex sampling • log-linear analysis • EM algorithm