I am working on a proposal and would like to know if it's possible to do a path analysis to examine a fully longitudinal indirect effect with the data I will have.
The data include stratification (3 countries), survey weights, and clustering - and I expect I will need to use multiple imputation with them. There are about 500 individuals in the sample, all of whom have data for the outcome variable.
At time 1, I have a dichotomous predictor variable and I can control for the mediator at this time as well.
At time 2, I have a sum score mediator and I can control for the dichotomous outcome.
At time 3, I have a dichotomous outcome variable.
In reading through the message boards and several papers, it seems to me that Mplus can account for much, if not all, of these things individually using:
I maybe should have included that I think I am going to need to do a log-binomial model rather than a logistic model. This is because my outcome is greater than 10% (VanderWeele, 2016 Mediation Analysis, A Practitioner's Guide)
WLSMV and ML can do these things together. Even though your binary outcome has prevalence > 10%, you can use the Mplus counterfactually-defined indirect/direct effects for logistic link - they don't assume a rare event for the outcome as the VanderWeele approximate odds ratio approach does. See chapter 8 of our RMA book.