Message/Author 

Sue Lee posted on Thursday, March 08, 2012  2:37 pm



Dear Drs. Muthen, Could you point to me resources that explain how the auxiliary (e) command tests the difference in the means across latent classes by a covariate? Explanation in the Mplus user guide stating that it is using the posterior probabilitybased multiple imputations was not sufficient for me. Thank you. 


There is a technical appendix on the website that covers this. It is called: Equality Test of Means Across Latent Classes Using Wald ChiSquare Based on Draws From Posterior Probabilities 

Sue Lee posted on Friday, March 09, 2012  8:06 am



Thank you very much, Dr. Muthen! 

Sue Lee posted on Monday, April 09, 2012  12:28 pm



Dear Drs. Muthen, If the predictor of interest is a categorical variable such as race, what is the interpretation of the probability of the covariate given class membership? I am trying to use this command to test if I need to stratify the LCA model by race. Would it be an appropriate use of an auxiliary(e) command? Thank you. 


It is the proportion in each class of the race equal to one in the dummy variable. The AUXILIARY command is used for screening purposes. 

Sue Lee posted on Wednesday, April 11, 2012  8:39 am



Thank you, Dr. Muthen. Do you suggest a way to test if the LCA model needs to be stratified by race? I can use the theory and the fact that conditional probabilities of indicators seem to differ substantially in a certain class, but I would like to know if there are ways to back it up statistically. 


You can look at the direct effects of the latent class indicators regressed on race and allow them to vary across classes. You cannot look at all of them at the same time because this would not be identified. 


I am estimating a growth mixture model and using AUXILIARY (e) function to test differences in predictor means across classes. A reviewer wants to know whether the procedure accounts for multiple testing. I could not find information about this in the technical appendices from 2010 and 2014. 


No, it does not. But if you focus on the overall test there is no such issue. 

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