

LPA with both categorical and continu... 

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I am using LPA to examine if latent profiles form from 8 indicator variables. 1 indicator variable is heavily weighted at 0. So, I categorized this variable into 4 categories. The model runs when covariances are fixed to 0 and variances are equal across classes, but I don’t know how to check the different variance/covariance structures when I treat the ordinal variable as categorical. Also, when I use a sensitivity analysis to see if I treat the ordinal variable as continuous, I get different results (evidence for 4class model) compared to when I treat the ordinal variable as categorical (evidence for 3class model). Main Questions: 1. I received an error about needing theta parameterization to examine covariances for when I treat the ordinal indicator as categorical. I’m not sure if/how parameterizing the model in that way would impact findings for the model as a whole (i.e., given that most of my indicators are continuous, will parameterizing the model in this way change the parameterization for my continuous indicators?) 2. Would it be simpler/kosher if I examined class differences in the variance/covariance matrix for my 7 continuous variables but simply ignored the possibility of testing the conditional independence assumption for my 1 ordinal indicator? 


1) Theta param'n is relevant only for WLSMV which doesn't do mixtures. You can add a single factor and see if some loadings are significant, indicating nonindependence conditional on classes. 2) Better to use a factor. 

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