effect sizes in lme/ multi-level models
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??rtler 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??rtler