Elina Vaara posted on Monday, November 11, 2013 - 12:03 am
I have longitudinal NMAR data (n in the first round about 2500). There is severe (about 30%) dropout rate from 1st measurement timepoint, and I wish to separate three agegroups and do analysis for them separately. I have earlier done CFA with categorical variables, but it does listwise deletion.
I have 30 correlated variables, which I plan to use in longitudinal BSEM with Mplus, and check for measurement invariance longitudinally and between genders. I am thinking is this a good way to do my analysis: even if Bayes estimation gives me more flexibility, I was wondering if the amount of missing data is still a concern?
I have found papers on Bayes and papers on missing data, but still I am not convinced that I could rely on BSEM results with nonignorable missingness I have. Could you give me advise on this?
Bayes and maximum likelihood both require MAR and have the same concerns. For NMAR, see the following paper which is available on the website:
Muthén, B., Asparouhov, T., Hunter, A. & Leuchter, A. (2011). Growth modeling with non-ignorable dropout: Alternative analyses of the STAR*D antidepressant trial. Psychological Methods, 16, 17-33.
Elina Vaara posted on Sunday, November 17, 2013 - 11:45 pm
Thank you! I have your article now, but here are a few more questions: For now I should see how the latent factors are associated in time. I wish to see change/association of different timepoints for about 5 factors (from CFA or such for ordinal variables). Growth curves model just one aspect at a time? So should I use (continous) latent factors as outcomes in five separate growth analyses? I would see possibility to model dropout (ex. pattern mixture approeach) and 5 longitudinal factors at the same time.