st: Re: micombine and overall model fit statistics

["Rodrigo A. Alfaro" <raalfaroa@gmail.com>]

Claire,

As the 99% of the procedures Multiple Imputation (MI) is not magic. The 
command -micombine- collapses the results that you get from several 
simulated complete dataset and adjustes the standard errors to reconize that 
part of your data (the missing values) was "created" by some method 
(Bayesian, ICE, etc.). This is the way that MI "solves" the problem of 
missing values, basically you assign some values to replace the missing but 
you are ending with "more" datasets.

Usually, when the method works the R2's for each imputation are not too much 
different. I suggest you to report the average writing a small note about 
the dispersion of the all R2's that you get... maybe something like that in 
the table "R2  = 0.45" and in the note "Average R2 obtained from 5 imputed 
datasets. The min R2 obtained was 0.42 and the max 0.47". Assuming that the 
reviewers know what is MI, they will understand that the procedure worked 
for your case.