My data comes from a two stage stratified cluster sample. There are some categorical (ordinal) manifest (dependent/endogenous) variables. I believe these variables can be divided into three major groups and are manifested by three factors, such as 'economic independence', 'self-esteem' etc. which can also be related to each other. Some covariates and independent factors are also there which are supposed to be associated with these latent variables and manifest variables. I want to adjust for clustering and check for some statistical causal relationships among the latent factors and variables. As the data are supposed to have clustering, I want to adjust for the cluster effect as well. Could you tell me about the issues I should consider regarding such an analysis? I suppose there can also be a stratum effect.
I wanted to perform Structural Equation Modelling (SEM) with categorical survey data. I found
gsem function in
Stata that can do it. But it takes too long, and I even wonder if it can handle complicated analysis with many variables. I also observe that it has some memory usage problem. If I want to account for clustering, survey nature of the data and at the same time want to perform SEM with some categorical variables, is there any other software available? Sections & Interest Groups suggests
Mplus supports only continuous variables. I have also checked
laavan-survey package in
R. But that also only supports continuous data. Please correct me if I am wrong.