Helps if I use the right domain . . .
Greetings.
I'm doing something that might be a bit dicey, but I don't have any better ideas and I'm hoping for feedback.
I've done a latent class analysis in Mplus. LCA, of course, is a data driven procedure for establishing the class definitions.
Mplus handles missing data in the class indicators, but not in predictors of class membership. So when I added predictors, some of which had significant (~20%) missingness, I went to MI. I'm interested in the effects of one of the predictors on class membership.
There is missingness in the indicators, so naturally class definitions and sizes vary across imputations. This introduces a great deal of (what seems to be to be spurious) between-imputation variance that is swamping any effects. The fractions of missing information are running around 99%, and df are only fractionally above the number of imputations, so I need to do a large number of imputations to get any stability.
I tried a hybrid approach, where I set the latent class definition parameters to be fixed at the values from the original LCA (with no predictors and thus no need for MI) across imputations. I'm still getting extremely high missing information and low degrees of freedom. With large numbers of imputations (50), I am finding effects, but they're with DF of 51. I'm assuming the problem is because the sizes of the classes vary.
Does the large missing information open me up to criticism? Or if I find an effect can I be confident it's real? Any other suggestions?
Thanks, Pat
-- Patrick S. Malone, Ph.D., Research Scientist Duke University Center for Child and Family Policy North Carolina, USA http://www.duke.edu/~malone http://www.pubpol.duke.edu/centers/child/
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