Hi, I am working with a LCA model with two latent variables each with two categories. The model looks good and the categories makes a lot of sense and look like what I had expected. I wanted to use auxiliary variables in 3step process to check their effect on latent classes. However 3 step does not seem to work with the models with more than one latent variable. When I run a LCA model with one variable and 4 categories instead of having two variables with two categories I get different classes which don't look as good as two variable categories. So is there a way to constrain LCA model with one latent variable (4 categories) in a way that it will have the same categories as the model with two variables? In this case I would have the categories I want and still use 3step for multinomial regression.