On 9 Jul 2003 10:51:02 -0700, [EMAIL PROTECTED] (Dale Glaser) wrote:
> In regards to normalizing variables in a repeated measures design, > I recall having a discussion about the proper strategy. For example if > there are four repeated measures, and one of the measures does not > approximate the Gaussian distribution, then if normalization is > appropriate, one colleague suggested that the same transformation > needs to take place for ALL of the other repeated measures so as to > maintain the same metric. However, I found this advice confusing in [ snip, rest ] You are rather defeating the nature of the "repeated measures" if you stick in some measure that is different. Or some measure that is on a different scale. Or some measure that has been 'normalized' differently from the others. You might be able to do it, if you are simply wiping out all the between-period differences by the normalizations; but I'm not sure why you would do it. Do you have a model that explains why you could have arbitrary and different treatments of periods? And I *might* hesitate to call the resulting thing, "repeated measures ANOVA" -- I think you have to apply MANOVA tests, instead. -- 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/ . =================================================================
