02-16-2015 11:01 AM
Mmm. Haven't seen any macros (which is what it would take) on this one. The key here is that the effect size estimates will be different for each imputation, and I don't believe that eta squared or omega squared are "additive" in the sense that you could take the average of the imputational values as applying to the results from MIANALYZE. If I were searching for this on Uncle Google's website, I might use key terms like "model averaging effect size SAS" to see what might pop up.
02-16-2015 11:25 AM
That macro is quite good for getting the MIANALYZE results, but as far as effect sizes, there is still something missing. However, since it does calculate the mean squares, one could get the LSMEANS, and calculate effect sizes in a data step. Cohen's d would be simple, eta squared and omega squared somewhat more difficult.
And, as always, I have a difficult time moving from MIXED to MIANALYZE with things like repeated measures (getting the matrix to be of the correct form). I generally appeal to the fact that maximum likelihood estimates under missing at random are optimal, and that imputation really adds little in that situation.