On 15 Oct 2001 07:44:33 -0700, [EMAIL PROTECTED] (Warren) wrote: > Dear group, > It seems to me that the one issue here is that when we > measure something, then that measure should have some > meaning that is relevant to the study hypotheses. > And that meaning should be interpretable so that the width > of the CI does have meaning...why would you want to estimate > the mean if it is "meaningless"?
This reminds me that data analysts sometimes can help to make outcomes more transparent. "Scaled scores" of Likert-like items are intelligible when presented as Item-averages, instead of being Scale-totals for varying numbers of items. You can describe the relevant anchors for 2.5 versus 3.0, for group contrasts, change scores, or contrasts between different scales -- and not get into confusion of how many items were added for each total. I do think that standardized outcomes are usually appropriate to communicate the size of the outcome effect -- even when the measure is fairly well known. This discussion leads me to conclude that you absolutely need to describe your sample *more* thoroughly when you are stuck with using standardized measures to describe all your outcomes. How variable is this group? How extreme is this group and sample (if it is a clinical trial)? A small N is especially problematic, since you do want to show how narrowly (or not) it was selected. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html ================================================================= Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =================================================================
