Does anyone know if the lmer function of lme4 works fine for unbalanced designs? I have the examination results of 1000 pupils on three subjects, one score every term. So, I have three scores for English (one for every term), three scores for maths etc. However, not everybody was examined in maths, not everybody was examined in English etc, but everybody was in effect examined on four subjects. I also have information abouit the school. Would this model hive the right results for the variance components?

mod_3_f  <- lmer(SCORE ~ GENDER + (1 |ID ) + (1 | TERM) + (1 | SUBJECT)  , Dataset)

Linear mixed-effects model fit by REML 
Formula: SCORE ~ GENDER + (1 | ID) + (1 | TERM) + (1 | SUBJECT) 
   Data: Dataset 
    AIC    BIC  logLik MLdeviance REMLdeviance
 247882 247926 -123936     247871       247872
Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept) 5.97288  2.44395 
 TERM     (Intercept) 5.10307  2.25900 
 SUBJECT  (Intercept) 0.25943  0.50934 
 Residual             4.41673  2.10160 
number of obs: 53978, groups: ID, 5695; TERM, 4; SUBJECT, 4
Fixed effects:
               Estimate Std. Error t value
(Intercept)    14.30352    1.15870   12.34
GENDER[T.Male] -1.01776    0.06885  -14.78
Correlation of Fixed Effects:
Warning in x$symbolic.cor : $ operator not defined for this S4 class, returning NULL
            (Intr)
GENDER[T.M] -0.023

How do I interpert the results?

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


		
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