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 Gürtler 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 gürtler

>

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