

Auxiliary models with BCH weights 

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Hello, I used Mplus to conduct a latent profile analysis and am now using BCH weights to test a regression auxiliary model combined with latent class regression. I have two questions. 1. Is it possible to do this type of analysis without using listwise deletion? I am losing a large number of observations. 2. I used model constraint and model test to evaluate whether profile means are significantly different from each other. I got the following error: WALD'S TEST COULD NOT BE COMPUTED BECAUSE OF A SINGULAR COVARIANCE MATRIX. However, I still got pvalues for the new/additional parameters, indicating that some mean differences were significantly different from 0. Can you explain what that error message means and whether I can still trust the pairwise pvalues? Thanks so much! 


1. We do not use listwise deletion. There is a listwise command in Mplus but it is never the default. If you have a single dependent variable Y and some covariates X, the model for [YX] simply has an empty likelihood when Y is missing. Data containing information regarding [YX] is not removed. 2. Yes you can use the pairwise pvalues. For the Wald test the issue is the following. If you have 3 classes do not use model tests: 0=m1m2; 0=m1m3; 0=m2m3; Instead use model tests: 0=m1m2; 0=m1m3; The third equation is redundant and causes singularity. 


Thank you so much, this is really helpful! (And I apologize for my delay in getting back to you). I do have missing Y variables. Are there any other options for handling missing data? For example, is this compatible with multiple imputation? Thank you again! 


You can use multiple imputations (manually where each imputed data set is analyzed separately and then manually combine the results) however I would not recommend it unless you have some kind of a set of background variables that you can use for the imputation but you are not using in the model. If you don't have such a set of variables I would not recommend multiple imputation and our default method is the better option, which already uses all the information in the data, i.e., it is fullinformation FIML. 

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