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.
I encounter a problem that did not occur when I modelled only one categorical latent variable. Now that I model a two categorical latent variable multiple group LCA, I always get the following error message:
*** ERROR in MODEL command Unknown class label in MODEL : %CCYCLE#1.CC#1%
However, CYCLE has been introduced in the names command and further:
This worked with only one categorical latent variable, and I had assumed I could introduce similar restrictions in a multiple group LCA with two latent variables. If not so, how can I fix the conditional response probabilities (and class sizes) to be the same in both (known) groups?
Thank you kindly for the response. Can you suggest any sources with annotated output or articles? I'm not sure how to interpret these, where to look in the output? I'm specifically not sure how to differentiate the effect of the classes and covariates on the DV.
Nylund-Gibson, K., Grimm, R., Quirk, M., & Furlong, M. (2014): A latent transition mixture model using the three-step specification. Structural Equation Modeling: A Multidisciplinary Journal, 21, 439-454.