I think, some other folks are being sloppy about effect sizes.
"Power analysis for the social sciences" is a book that
defines small, medium and large effects in terms that are
convenient and *usually* appropriate
for the *social sciences* -- it makes no pretenses
that these are universally applicable.
Similarly --
On Thu, 13 Sep 2001 18:17:54 -0500, jim clark <[EMAIL PROTECTED]>
wrote:
> Hi
>
> I found the Rosenthal reference that addresses the following
> point:
>
> On 13 Sep 2001, Herman Rubin wrote:
> > The effect size is NOT small, or it would not save more
> > than a very small number of lives. If it were small,
> > considering the dangers of aspirin, it would not be used
> > for this purpose.
>
> At http://davidmlane.com/hyperstat/B165665.html, one finds:
>
> "Rosenthal (1990) showed that although aspirin cut the risk of a
> heart attack approximately in half, it explained only .0011 of
> the variance (.11%). Similarly, Abelson (1985) found that batting
> average accounted for a trivial proportion of the variance in
> baseball game outcomes. Therefore, measures of proportion of
> variance explained do not always communicate the importance of an
> effect accurately."
Stripping down the verbiage, that says:
"[M]easures of ... variance ... do not ... communicate ... the
effect."
That is exactly what Herman asserted. A two-fold effect is
"moderate" in epidemiology, whether you need 500 or 10,000
subjects-per-group to detect it. A five-fold effect is large.
In epi, these are measured by the odds ratio (which is also
the Relative risk when the occurrence rates are low).
Epidemiologists do not use R-squared as an effect measure
because it varies 20-fold for the same 500 versus 10,000.
R^2 is robust as a *test* but it is not robust where the
"odds ratio" is robust, as a measure of effect size.
>
> The reference for Rosenthal is:
>
> Rosenthal, R. (1990). How are we doing in soft psychology?
> American Psychologist, 45, 775-777.
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
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