[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]
st: Re: micombine and overall model fit statistics
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.