I'm trying to conduct Associative Latent Transition Analysis in Mplus, in which I want to jointly condition class membership of some outcome latent class (C) at time t+1 on class membership in some predictor latent class (D) at t+1, as well as class membership for both the outcome and predictor latent classes at time t.
Here is my attempt:
c2#1 on d2#1 d2#2 d1#1 d1#2 c1#1 c1#2; c2#2 on d2#1 d2#2 d1#1 d1#2 c1#1 c1#2;
I am trying to do an ALTA in MPlus. I need to fix certain latent status patterns to zero in the ALTA since very few people have these patterns as their most likely latent statuses.
I've got two time points for each LTA: c1 and c2 with 4 classes, d1 and d2 with 3 classes. I would like to set all associations with d1#3 and c1#1 to zero but since the last class (class 3) of the d1 variable is the reference class, I get an error saying that "references to slopes of the last class are not allowed". I would also like to set certain associations involving a specific combination of all 4 latent variables to zero.
I tried PARAMETERIZATION = PROBABILITY but that does not allow me to regress a latent variable on more than one variable as needed for an LTA (c2 on c1; d1 on c2 c1; d2 on d1 c2 c1;). Please let me know if you have any ideas on how I can set associations involving the reference class of one of the latent variables to zero. Thanks for your help.
I am trying to do a 3-step ALTA to ensure that latent class formation is not affected by the addition of covariates to my model. I am following the steps for the 3-step LTA with measurement invariance (MPlus Web Note 15: Appendix K-N). I noticed in Appendix K that the LCA models at the two time points are run independently of each other (i.e. no c2 on c1 regression).
I have two different constructs, c and d, measured at two time points resulting in four latent variables: c1, c2, d1, and d2. For step 1, should I run the four LCA models independently of each other but with item-response prob. constrained to be equal across (c1 and c2) and (d1 and d2)? I then plan on repeating Appendices L and M for each latent variable. Finally, in step 3, I will include covariates and latent variable regressions (full, independence, cross-sectional, longitudinal ALTA models). Does this approach account for associations between the c and d constructs or should the 3-step LTAs for each construct be combined in the last step? Thanks for your help.