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Re: st: Limit on imputing miss values?
Check out this
scheffer_2.pdf). It gives some guidance about which method of imputation
to use for x% of data to be imputed given which missingness mechanism is
> One alternativ is of course to assign the missing values a value
> which allows them to be included in the analyses as a dummy variable.
> It may be of interest to see how the other predictors perform if you
> include the dummyvariables for missing values compared to if you
> impute them. It would also be of interest to know if these persons
> are different from the others in any way. What persons are less
> willing to tell about their income? In what way may the exclusion of
> them give biased results?
> Roland Andersson
> --- In email@example.com, "Sabine N. Merz" <uribazo@r...>
> > Dear All:
> > I have a question where I simply do not know if there is a "right"
> > but I would much appreciate some feedback because there might be
> a "golden
> > rule" that I simply have overlooked. (Too much time spend dealing
> with this
> > would explain it.)
> > *Is there a limit on how how many missing values one can or rather
> > impute?
> > In my particular case the missing values all concern the oh, so
> > personal income question. I already cross checked, e.g., with labor
> > status, hours worked, and such but I am still left with about 7% of
> > respondents who did not answer this question. I would not feel
> > imputing 1% of missing cases but 7% seems a bit much. From the
> > values options provided it does seem that these folks simply
> refused to
> > answer the question.
> > I will likely simply use the stats "impute" option and see what I
> get and
> > compare these results to what would happen if I simply leave out 7%
> of my
> > sample. (Or would you suggest another method to deal with missing
> > Still neither one is a very elegant solution.
> > Thank you for any advice you might have.
> > Best wishes,
> > Sabine Merz
> > *
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