Latent class analysis for models with error of measurement using log-linear models and an application to women's liberation data

This article deals with the latent class analysis of models with error of measurement. If the latent variable is ordinal and manifest variables are nominal, an approach to handle the restrictions is given for latent class analysis of the models with error of measurement using log linear models. By this way, we include ordinal nature of the latent variable into the analysis. Therefore, overall uncertainty is decreased, and our inferences become more precise. The new approach is applied to a women's liberation data set.