I'm working on a model which is, well not to put too fine a point on it,
complex:
-Two dependent variables, one binary (accuracy) and the other
well-approximated by the normal (log-reaction time);
-Multilevel structure (120 observations per subject, about 300
subjects);
-Missing data that would delete entire subjects from the dataset, i.e.,
level 2 missingess (but which is probably MAR).
Fortunately there is a pretty strong theory about the relationship
between accuracy and RT and the regressors.
Any two of the three would be workable but the third is just a killer.
Suggestions? Does it make sense to do MI first and then run -gllamm-?
Anyone have any experience doing something like this?
JV
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