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 donít 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 4-class model) compared to when I treat the ordinal variable as categorical (evidence for 3-class model).
Main Questions: 1. I received an error about needing theta parameterization to examine covariances for when I treat the ordinal indicator as categorical. Iím 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?