Hello everyone,
I am trying to regress applicants' performance in an assessment center
(AC) on their gender (individual level) and the size of the AC (group
level) with a multi-level model:
model.0 <- lme(performance ~ ACsize + gender, random = ~1 | ACNumber,
method = "ML", control = list(opt = "optim"))
I have 1047 applicants in 118 ACs:
> length(performance)
[1] 1047
> length(levels(as.factor(ACNumber)))
[1] 118
There are five AC sizes and gender has two levels (coded as -1 for
female and 1 for male):
> length(levels(as.factor(ACsize)))
[1] 5
> length(levels(as.factor(gender)))
[1] 2
However, when I examine the model summary, the predictor on the
individual level (gender) and the predictor on group level (ACsize)
have the same degrees of freedom:
> summary(model.0)
Linear mixed-effects model fit by maximum likelihood
[...]
Random effects:
Formula: ~1 | ACNumber
(Intercept) Residual
StdDev: 0.1650112 0.8146622
Fixed effects: performance ~ ACsize + gender
Value Std.Error DF t-value p-value
(Intercept) 3.0927051 0.24573622 927 12.585467 0.0000
ACsize -0.0568915 0.02782755 927 -2.044431 0.0412
gender 0.1679830 0.02780940 927 6.040510 0.0000
[...]
Number of Observations: 1047
Number of Groups: 118
How is it possible that the group-level predictor has a df > than the
number of groups? I am a little at a loss here and would appreciate it
if someone could explain this to me... What am I missing?
Regards,
Bertolt
--
Dr. Bertolt Meyer
Senior research and teaching associate
Social Psychology, Institute of Psychology, University of Zurich
Binzmuehlestrasse 14, Box 15
CH-8050 Zurich
Switzerland
bme...@sozpsy.uzh.ch
tel: +41446357282
fax: +41446357279
mob: +41788966111
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