[R] effect sizes in lme/ multi-level models

[Spencer Graves]

	  The "eta^2" you describe looks something like an R^2 (or maybe a 
partial R^2), and CohensD looks like a Student's t, at least to me.  The 
problem with generalizing these to multi-level models is deciding which 
components of variance to include where.  If you can answer that, I 
think you can find all the pieces you need by trying 
'methods(class="lme")'.  I just got 32 items on that list, but you might 
get a different number unless you have exactly the same packages (and 
versions) attached as I did just now.  From this list of 32, I suggest 
you look first at "fixef", "ranef", and "VarCorr".

	  hope this helps.
	  spencer graves

Leo Gürtler wrote:

> Dear alltogether,
> 
> I am searching for a way to determine "effect size" in multi-level 
> models by using lme().
> Coming from Psychology, for ordinary OLS there are measures (for 
> meta-analysis, etc.) like
> 
> CohensD <- (mean_EG - mean_CG) / SD_pooled
> 
> or
> 
> (p)eta^2 <- SS_effect / (SS_effect + SS_error)
> 
> I do not intend to lead a discussion of the usefulness of such measures 
> as long as the standards of psychological journals (e.g. as defined by 
> the APA) order them.
> However, I wondered how to determine measures of effect size in lme. 
> Pinheiro&Bates (2000) do not touch that topic.
> I assume that as long as a grouping structure is present, the formular 
> of CohensD (see above) has to be corrected to give respect to the 
> grouping structure. Is there any equivalent measure like eta^2, 
> partial-eta^2, etc.?
> 
> Can anybody help me with formulas, R code or some references?
> 
> Thank you very much,
> 
> thanks in advance,
> 
> leo gürtler
>