On Wed, 9 Jul 2003 11:22:04 +0200, "Dennis" <[EMAIL PROTECTED]> wrote:
> 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. > 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. > If it is acceptable, how should I compute the mean if there are several > outliers in one group/DV, (or variable in SPSS)? -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html "Taxes are the price we pay for civilization." Justice Holmes. . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
