Adrian Byrne posted on Wednesday, February 05, 2014 - 8:27 am
Dear Professor Muthen,
How can one tell Mplus to distinguish between latent class indicators at time 1 and observed categorical distal outcomes at time 2?
We do not wish for the distal outcome to affect the classes, which occurred previously in time, in a cross-sectional way. We want to investigate how the latent class membership at time 1 affects observed category membership at time 2.
Please see the stepwise mixture modeling techniques discussed in
Asparouhov & Muthén (2013). Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus. Accepted for publication in Structural Equation Modeling. An earlier version of this paper is posted as web note 15. Appendices with Mplus scripts are available here.
Thank you for your quick response and suggested solution. I have been following the method and Mplus code (Appendices E and F) to conduct the 3-step procedure with an arbitrary secondary model within your paper. However, I encounter a problem at step 3 as I receive the following ERROR message:
*** ERROR in MODEL command Variances for categorical outcomes can only be specified using PARAMETERIZATION=THETA with estimators WLS, WLSM, or WLSMV.
I am using a binary outcome variable (which I am treating as categorical). I am aware that PARAMETERIZATION=THETA is not allowed when TYPE = mixture. Is it simply best to treat the outcome variable as continuous (if possible) or is there another solution to allow for categorical outcome variables at step 3 of the suggested procedure?
Thanks again for your help. I have another query regarding the 3-step procedure (Appendices E and F); can I still apply the same procedure when I want to regress the latent class membership at time 2 on observed predictors at time 1? This time I want to treat the latent variable as a response rather than a predictor.
If this 3-step procedure is not appropriate, can you suggest a better alternative?