[R-sig-ME] R-sig-mixed-models Digest, Vol 28, Issue 4

[Iasonas Lamprianou]

Dear all,
I'll re-send this request since I got no reply to the first one. 
It is an issue which I face currently with lmer and MLWin and SPSS. This problem makes me feel very undomfortable. I have one standardized variable which represents the academic performance of children,  and I also have information about their school and their class.  I run the model with SPSS and lmer and I get the same result (both use REML). Then I use MLWin and I get different (but more reasonable results). MLWin uses IGLS and RIGLS and MCMC (all three methods agree when I use MLWin). I hereby present my numbers:

I run the following model:

Linear mixed model fit by REML 
Formula: Zmg_Arxiki ~ 1 + (1 | school) 
   Data: data 
   AIC   BIC logLik deviance REMLdev
21693 21714 -10844    21684   21687
Random effects:
Groups   Name        Variance Std.Dev.
school   (Intercept) 0.37043  0.60863 
Residual             0.74465  0.86293 
Number of obs: 8448, groups: school, 47

Fixed effects:
            Estimate Std. Error t value
(Intercept) -0.13515    0.08971  -1.506

However, this is NOT reasonable because the variable is a standardized variable and the variance should be 1.0!! SPSS gives the same results.
If I run the same model in MlWin, I correctly get 

S2 for the school level=0.077
S2 (error) = 0.923
and the total is 1.0 (correct)!
Could anyone please let me know what happens? Any help is welcome.

Dr. Iasonas Lamprianou
Department of Education
The University of Manchester
Oxford Road, Manchester M13 9PL, UK
Tel. 0044  161 275 3485
iasonas.lamprianou at manchester.ac.uk