I have conducted a series of longitudinal analyses whereby I am assuming MAR as a first step and NMAR as a second. There are 6 timepoints. For the first step, I am using the default estimator in a multigroup latent growth model. For the second second, I'm implementing the Muthen-Roy PMM method.
I now want to assess whether the difference I am seeing between the groups at various timepoints are significant (primarily, the final timepoint).
For the LGM using FIML, I am succesfully doing this by making the final timepoint the intercept, and earlier timepoints fixed @ minus occasions. However, this is not successful within the PMM approach. Doing this within the PMM approach changes the latent class allocations and the trajectories of change.
Is there a way to get the SDs or SEs for the means at each timepoint?
I've checked the users guide and explored the internet for examples of the syntax I would use to use the Model Constraint to get the estimate and SE. However, I am still unsure what the exact syntax would be.
Wonderful - this is extremely helpful and with a listwise deletion model, FIML, and LOCF I have my estimates. However, for my PMM (basic PMM, no LCs) the produced means are worrying. They track the means and SEs from listwise deletion (where I have lost over 3,000 cases). They are also quite different to the model-estimated and sample means shown in the figures. This leaves me wondering whether the dropout indicators need to be somehow built in to the model constraints, to ensure that the means produced are taking those in to account.