30 Apr
2008
30 Apr
'08
8:44 a.m.
Hi Gary,
Is this approach valid (or at least the best option) for an analysis with
several binary variables? We currently do what the original post suggested and
are a little unsure about averaging 1s and 0s across data sets.
Thanks,
Dan
Quoting Gary King <king at harvard.edu>:
it wouldn't be terrible but it might be a pain since you'd likely have
different numbers of observations for each imputed data set. why don't
you instead temporarily create a data set that is the average of the 5
imputed data sets (so the observed data are the same as in each of the 5
and the missing data are the averages of the imputations). then match on
that so you know what obervations to delete. then delete those
observations from each of the original 5 data sets and use those.
this is also a hack but would be less of a pain.
Gary
On Tue, 29 Apr 2008, Johannes Castner wrote:
...I wonder how terrible it would be,
comparatively speaking, if one was to
impute five times then match on each of the imputation data sets, then run
the
analysis on each of the 5 imputed and then
matched data sets, and lastly
average the results. Does anyone have any idea how well that would do?
Johannes
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