I am trying to conduct a latent profile analysis using the items of a scale as the 10 indicator variables. Some of these items are zero-inflated; others are normally distributed. To run a zero-inflated model, is the only addition to my syntax the COUNT statement:
AUXILIARY = bya (e) typ (e) drug (e) h1 (e); COUNT = slp1-slp10; classes = slp(2); ANALYSIS: type = mixture;
If only some of the indicators are zero-inflated, is there a different strategy that would be more beneficial?
The variables are on a 5-point scale with "Never" as one of the anchors, and "Always" on the other anchor. A few of the items have a typical zero-inflated distribution, with a large stack on the "Never."
In an LPA model, can some indicators be categorical and some continuous? Or is it better to just treat all the items on the scale as categorical if a couple of them need to be modeled as categorical to handle floor effects?