Re: imputation

Thom wrote:
> In theory imputation should always reduce noise ... I'm not sure throwing
> away observations would ever help imputation because where imputation works
> it is using the information about what is missing and how much is missing.
> Assuming, I think, that the MAR assumption is tenable.
>
> Thom
>

Thom,
Yes, this is the sort of thing I was wondering about (i.e. whether
there was some theoretical argument that records should never be
removed). But I guess it would have to depend on MAR or MCAR
assumptions. But it's the results of subsequent analyses that really
concerns me. I normally deal with noisy data where the noise has been
added deliberately (for disclosure control purposes). It can have
significant effects on e.g. Type 1 error rates if no adjustment is made
to the test statistics. Including a record with very few known values
and many imputed values, just seems to me like adding a little
information and a little noise; and I don't see that this is
unquestionably a good idea. The easiest thing for me to do (in terms of
satisfying referees that I've done something sensible) is probably just
to impute. But there are apparently 3 types of missing data in the
dataset I've been supplied with, some can be imputed with certainty
(done that), many almost certainly take a specific value and the rest
are (possibly) MAR. The latter two types have been coded identically.
Lovely. Cheers.

Duncan
.

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