Is there any possible method to calculate effect size in mixed models? I run MIXED command for mixed model analysis of repeated measured data.
However, there is no option or menu for estimate power(like partial eta in GLM) in mixed analysis.
Is there any method to calculate effect size in mixed model?
 A: The estimates are the effect sizes as for simple regression. For example in following output in R: 
Linear mixed model fit by REML ['lmerMod']
Formula: Reaction ~ Days + (Days | Subject)
   Data: sleepstudy

REML criterion at convergence: 1743.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.9536 -0.4634  0.0231  0.4634  5.1793 

Random effects:
 Groups   Name        Variance Std.Dev. Corr
 Subject  (Intercept) 612.09   24.740       
          Days         35.07    5.922   0.07
 Residual             654.94   25.592       
Number of obs: 180, groups:  Subject, 18

Fixed effects:
            Estimate Std. Error t value
(Intercept)  251.405      6.825   36.84
Days          10.467      1.546    6.77

Correlation of Fixed Effects:
     (Intr)
Days -0.138

In above output, the effect size for Days can be taken as 10.5. If multiple predictors are present, their estimates will reflect effect sizes for each.
A: I found myself two options.
One is calculation based on correlation, based on the book of Rosenthal, Rosnow, & Rubin.
"Contrasts and Effect Sizes in Behavioral Research A CORRELATIONAL APPROACH"
Another option is use SAS and calculate f squared.
