Why are degrees of freedom so high in repeated measures mixed models? I have been running a repeated measures mixed model in SAS and the denominator degrees of freedom for the F statistic seems to be really high. In this case I have roughly 200 individuals and three measurements for each individual (the repeated statement) and degrees of freedom are over 500. So it seems to be roughly reflecting the number of observations. I checked a few articles and found the same pattern elsewhere, but as a stats beginner I'm not sure if it is ok; or is it pseudoreplication? I also noted that changing the covariate structure has a large effect on degrees of freedom.


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*Why are degrees of freedom so high in such designs, or is it likely that there is something seriously wrong with my model?

*What might be some suggested reading on repeated measures mixed models to help me understand this?

 A: Unless I'm reading you incorrectly you would have approximately this many degrees of freedom in a standard repeated measures ANOVA.  The ANOVA error is the interaction between subjects and the effect and requires degrees of freedom from both.  However, if you are making multiple measures at each time interval then yes, the degrees for freedom are much higher for the mixed effects model.
It's not the standard pseudoreplication problem you'd have if you were doing a repeated measures ANOVA.  With the ANOVA you are only modelling the effects in question and should aggregate your data to get better estimates of each effect for each S.  With mixed effects modelling you are potentially modelling each data point, even those pseudoreplicated measurements you take for accuracies sake.  You can do that because you're explicitly saying these individual measures are grouped within subjects and within this factor, etc.  Therefore, you can often have more degrees of freedom.  Although, as I stated before, it doesn't sound like that's the case here anyway.
