The between-level variables will be the manipulation, and other time-invariant covariates such as personality. We predict these will affect OutcomeA, and possibly Measures A, B, C.
I would like to use Multilevel Growth Mixture Modeling because I want to compare the two conditions in the same overall model. This way I can test for manipulation differences in the intercept and slope of OutcomeA, and how those intercepts and slopes are affected by the second level variables.
Is this possible? I can't find a relevant example in the Mplus manual. But I think I might be able to do it by using a modification of 10.10 (Mplus manual) and adding KNOWNCLASS, and combining that with 6.18, 6.12, or 6.16. Would this work?
I greatly appreciate any help you could give me with this perplexing problem!
The first group should be assigned missing on MeasureC. If you use KNOWNCLASS, this should work. You should be able to add this to any example. You need to use TYPE=MIXTURE, the CLASSES and KNOWNCLASS options, and add %OVERALL% to the MODEL command.
Is it okay if I build on this problem? I'm trying to understand what is allowable and not in this type of model. In general, do the same assumptions and restrictions about LGC models need to hold? For example, does the time span between OutcomeA's need to be the same across conditions?
To clarify, suppose a round lasts 5 minutes. OutcomeA is a measure of the behaviors person 'A' can do in 5 minutes. In Condition1, person A is allowed to do behaviors every round, and there are 5 total rounds: