[R] effect size
henning.bumann at gmail.com
Fri Jul 29 17:21:52 CEST 2011
I understand, that eta^2 presents a useful measure of effect sizes.
However, the power-calculation-tool G*Power I intend to use, requires an
"Effect size f" which is calculated like this: "Variance explained by
special effect" / "Variance explained by special effect" + "Variance within
I run a within-subject design with 3 experimental conditions and a certain
number of subjects. RTs are usually aggregated within the design cells, but
Which Sum of Suqare in the aov()-procedure do I have to use to compute this
"Effect size f" properly?
and a related question:
Is it in any way meaningful to standardize the expected effect (eg. 20 ms
difference between experimental conditions) with the variance of a
represenative distribution (eg. of reaction times within one design-cell,
eg. 200ms sd) to retrive some measure of effect size (eg. 0.1)?
Bruno L. Giordano wrote:
> For ANOVA one option is the partial eta squared \eta^2_p:
> For multiple regression (continuous predictors) you might use the
> standardized parameter estimate, the regression coefficient you obtain
> standardizing the predictor: the larger the absolute value, the larger the
> size of the effect.
> You might take a look at:
> S. Olejnik and J. Algina, (2003), Generalized eta and omega squared
> statistics: measures of effect size for some common research designs,
> Psychol Methods. 8(4):434-47.
> and, if you have repeated measures:
> R. Bakeman (2005), Recommended effect size statistics for repeated
> designs, Behavior Research Methods, 37 (3), 379-384.
> ----- Original Message -----
> From: "Matthew Bridgman" <m.bridgman at sbcglobal.net>
> To: <r-help at stat.math.ethz.ch>
> Sent: Wednesday, June 21, 2006 5:01 PM
> Subject: [R] effect size
>> Does anyone know a simple way of calculating effect sizes?
>> R-help at stat.math.ethz.ch mailing list
>> PLEASE do read the posting guide!
> R-help at stat.math.ethz.ch mailing list
> PLEASE do read the posting guide!
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