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 protected], "Sabine N. Merz" <uribazo@r...>
wrote:
> Dear All:
>
> I have a question where I simply do not know if there is a "right"
answer
> 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
should
> impute?
>
> In my particular case the missing values all concern the oh, so
sensitive
> personal income question. I already cross checked, e.g., with labor
force
> status, hours worked, and such but I am still left with about 7% of
my
> respondents who did not answer this question. I would not feel
uncomfortable
> imputing 1% of missing cases but 7% seems a bit much. From the
missing
> 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
values?)
> Still neither one is a very elegant solution.
>
> Thank you for any advice you might have.
>
> Best wishes,
> Sabine Merz
>
>
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