Dennis......yes, the effect size index may be arbitrary, but for argument sake, say I have a measure of 'self-esteem', a 10 item measure (each item a 5-pt. Likert scale) that has a range of 10-50; sample1 has a 95% CI of [23, 27] whereas a comparison sample2 has CI of [22, 29]. Thus, by maintaining the CI in its own unit of measurement, we can observe that there is more error/wider interval for sample1 than sample2 (for now assuming equal 'n' for each sample). However, it is problematic, given the inherent subjectivity of measuring self-esteem, to claim what is too wide of an interval for this type of phenomenon. How do we know, especially with self-report measures, where indeed the scaling may be arbitrary, if the margin of error is of concern? It would seem that by standardizing the CI, as Karl suggests, then we may be able to get a better grasp of the dimensions of error.......at least I know the differences between .25 SD vs. 1.00 SD in terms of magnitude..........or is this just a stretch?!!!
Dale N. Glaser, Ph.D.
Pacific Science & Engineering Group
6310 Greenwich Drive; Suite 200
San Diego, CA 92122
Phone: (858) 535-1661 Fax: (858) 535-1665
http://www.pacific-science.com
-----Original Message-----
From: dennis roberts [mailto:[EMAIL PROTECTED]]
Sent: Tuesday, October 09, 2001 1:52 PM
To: Wuensch, Karl L; edstat (E-mail)
Subject: Re: Standardized Confidence Intervals
At 03:45 PM 10/9/01 -0400, Wuensch, Karl L wrote:
>Some of those who think that estimation of the size of effects is more
>important than the testing of a nil hypothesis of no effect argue that we
>would be better served by reporting a confidence interval for the size of
>the effect. Such confidence intervals are, in my experience, most often
>reported in terms of the original unit of measure for the variable involved.
>When the unit of measure is arbitrary, those who are interested in
>estimating the size of effects suggest that we do so with standardized
>estimates. It seems to me that it would be useful to present confidence
>intervals in standardized units.
why? you only get further away from the original data scale/units you are
working with ...
in what sense ... is ANY effect size indicator anything BUT arbitrary? i
don't see how trying to standardize it ... or any confidence interval ...
makes it anything other than still being in arbitrary units ...
i would argue that whatever the scale is you start off using ... that is as
CLOSE as you can get to the real data ... even if the scale does not have
any "natural" or "intuitive" kind of meaning
standardizing an arbitrary variable does NOT make it more meaningful ...
just like converting raw data to a z score scale does NOT make the data
more meaningful
standardizing a variable may have useful properties but, imputing more
meaning into the raw data is not one of them
==============================================================
dennis roberts, penn state university
educational psychology, 8148632401
http://roberts.ed.psu.edu/users/droberts/drober~1.htm
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