My question is, how to get effect sizes for a linear mixed model?
I am using the following model in SPSS:
MIXED transfer BY distance training rotation sequence WITH pretest /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED = pretest rotation sequence distance training distance*training | SSTYPE(3) /METHOD = REML /REPEATED = distance | SUBJECT(ResponseID) COVTYPE(CS) .
I've also done it in R, where it looks like this:
library( nlme ) options(contrasts=c("contr.sum","contr.poly")) lm1 <- lme(transfer ~ training * distance + rotation + sequence + pretest, random=~1|ResponseID, method="REML", data=wide.data ) fit <- anova.lme(lm1,type='marginal') print( fit )
The dv, transfer, measures pretest to posttest improvement for a given level of distance. Distance is a categorical within-subjects variable with either 2 or 3 levels depending on the dataset. Training is a categorical between-subjects variable with either 2 or 4 levels depending on the dataset. Rotation and sequence are binary categorical between-subjects variables. Pretest is a covariate with different values for each level of distance.
I've already submitted the results of the analysis for publication, and got a reviewer comment asking for odds ratios. Ideally I'd like to get odds ratios for each effect in the model. If I can't get odds ratios, some other measure of effect sizes might be OK. Most important, I need to get it for the training * distance interaction and the covariate (pretest) because these are the only significant effects.
Ideally I'd get it straight from SPSS but as far as I can see, SPSS can't do it. I couldn't figure out how to do it in R either. Second best would be to put the output of one of these into some other software that could calculate it for me. I found some free software that could compute eta-squared for regular mixed ANOVA but not for linear mixed model.
Other questions about effect sizes seem to relate to other models, not LMMs. I did see another similar question about LMMs which is currently unanswered.