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 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?
 Linda K. Muthen posted on Monday, November 11, 2013 - 10:38 am
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.
 Bengt O. Muthen posted on Monday, November 18, 2013 - 8:22 am
Start with one growth curve at a time. It is possible to do all 5 at a time, but you should first do one at a time.
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