Re: st: effect size in nonlinear regression

[Maarten buis <maartenbuis@yahoo.co.uk>]

--- On Tue, 16/11/10, Airey, David C wrote:
> In linear regression or ANOVA, effect size can be eta^2 or
> omega^2, the amount of explained variation in the sample or
> population, respectively. Do these concepts translate to
> nonlinear regression? Does anyone have any favorite
> references discussing statistical power and effect size
> measures for nonlinear regression? I'm guessing these
> concepts don't translate to nonlinear regression, because I
> see some cautions about interpreting R^2 in nonlinear
> regression. I'm trying to understand how one expresses a
> change in a nonlinear regression parameter in terms of some
> kind of standardized effect size.

Standardized effect sizes aren't very common in my discipline,
but the one time I came across them was when I had a look at
meta-analysis. For binary dependent variables the default (in
my course) was to use the odds ratio. It makes sense to me that
one can see it as a standardized effect: standardization often
boils down to some kind of relative effect, and a ratio is a
relative effect.

Hope this helps,
Maarten

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany

http://www.maartenbuis.nl
--------------------------


      

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