I'm attempting to use the (manual) BCH approach to fit a model for the mean of a (manifest) continuous variable (DTSCORE) as a function of a four-category latent category variable (C), first without adjustment (for other covariates) and then with adjustment for other covariates.
The continuous variable has only non-negative integer values (eg., 0, 1, . . . , 22). However, for the unadjusted model, the estimated mean for DTSCORE is negative for category C = 2. I was wondering how the mean can be negative?
I'm using the following syntax for the unadjusted model:
ANALYSIS: TYPE = COMPLEX MIXTURE; Starts = 0; Estimator= MLR;
MODEL: %OVERALL% DTSCORE;
%c#1% [DTSCORE] (mn1);
%c#2% [DTSCORE] (mn2);
%c#3% [DTSCORE] (mn3);
%c#4% [DTSCORE] (mn4);
mn1 = mn2;
And here are the model results for Class 2, showing the negative mean (-0.586):
It seems possible to get a negative estimated mean if your variable has a large percentage at 0. Your model assumes a normally distributed variable so the left tail may go into the negative. If you have a large percentage at 0 and integer outcomes you might want to declare your variable as a count variable.
The modeling of counts uses log(mean), so if you want to get back to means you have to exponentiate the value. See chapter 6 of our new book.
Sam Craft posted on Wednesday, April 03, 2019 - 4:22 am
I'm trying to use the manual BCH method to regress a continuous distal outcome on 7 latent classes and 9 other covariates. Some of the weights take on negative values which i assume will make the estimates for the auxiliary model inadmissible.
Are there any possible solutions to this? If not would you recommend trying an alternative method (i.e.DU3STEP)