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 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.

Many thanks.
 Bengt O. Muthen posted on Wednesday, February 05, 2014 - 2:47 pm
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.

which is on our website.
 Adrian Byrne posted on Tuesday, March 04, 2014 - 3:11 am
Dear Professor Muthén,

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

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?

Many thanks.
 Linda K. Muthen posted on Tuesday, March 04, 2014 - 10:14 am
You should treat the variable as categorical but not mention the variance term. Variances are not estimated for categorical variables.
 Adrian Byrne posted on Thursday, March 06, 2014 - 9:48 am
Dear Professor Muthén,

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?

Many thanks.
 Bengt O. Muthen posted on Friday, March 07, 2014 - 5:06 pm
You can use R3STEP for this. See the first couple of appendices.
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