> Hi all
> I would like to remove outliers from my repetitive measures design, however
> making it missing removes the whole case of the subject.
Your first issue here is "What do you do with outliers" - which
depends on what you can say about them. Sometimes, a simple
transformation is justified by the nature of measurement: square
root for counts, log for biological assays, reciprocal for distances.
If you have *good* scaling already, the reasonable thing might
be to write an essay on each outlier, and remove that S from
the sample. If you have half-good measurements, like the ones
that I usually see, you might want to Windsordize -- pull in the
most extreme values to whatever was at (say) the 95th percentile.
Quote:> I've heard that it's possible to replace outliers with the mean of the
> group. I am wondering if it's a standard practice (to use for my thesis),
> and are there any good references?
Replacing the outlier with the <some mean> is, IMHO, a
First, you justify that the outlier should be regarded as 'missing.'
Second, you figure *which* mean is appropriate to stand in
for something missing. The usual initial rationale is that you
use a mean that disturbs the statistical test the least.
- You don't want to increase the tested Mean-square.
- In Repeated Measures, that could be the Subjects mean;
but that can raise a problem if you have much that is missing
because you also don't want to decrease the Mean-square
of the error term.
If you still want it, there are books written on Missing data,
and I would use Google: for instance, look for college courses
and what they list in their Suggested References.
Quote:> If it is acceptable, how should I compute the mean if there are several
> outliers in one group/DV, (or variable in SPSS)?
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