Quote:> 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

two-step procedure.

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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