Deleting or altering measured values from a data set is serious business including moral as well as analytical aspects. As previous writers have said, You have to make a strong case for doing so in any report on results. Are the outliers so large that they are impossible (e.g. child's' height = 30 m). Are they large (or small) with respect to the Gaussian assumption. After taking care to document your reasons, you may substitute the mean only as the beginning of an imputation procedure. You also may reduce the effect of outliers on data summaries by using the median rather than the mean.
Ref: Statistical analysis with Missing Data; 2nd ed. Roderick Little and Donald Rubin this ref discusses how you handle empty cells in a data table--it looks to me that using the mean replacement for "outliers" falls in the same category. Most stat packages have capability to impute data. Good luck on your thesis, you have a golden oportunity to teach your advisor some useful concepts. Dennis 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. >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? >If it is acceptable, how should I compute the mean if there are several >outliers in one group/DV, (or variable in SPSS)? > >Dennis > > > > . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
