Degrees of freedom in my mixed model I am having a serious issue trying to figure out why my degrees of freedom are screwy when running my proc mixed model.  I have nested data for a MLM analyses, but SAS isn't providing the correct df and I'm not sure why.
I have 50 individuals with 3 or 4 time sensitive datapoints each.  Measuring their physical activity per day for 4 days, I have 177 observed instances of physical activity, however SAS keeps outputting 77 df when running ddfm=bw.  
I have cleaned the data and reimported, but I'm just at a loss for what to do now.  I'd really appreciate any help.
Thanks!
 A: Likely SAS has done nothing wrong at all. DF for mixed models isn't straightforward. See the SAS STAT manual for PROC MIXED:

It is computed by dividing the residual degrees of freedom into
  between-subject and within-subject portions. PROC MIXED then checks
  whether a fixed effect changes within any subject. If so, it assigns
  within-subject degrees of freedom to the effect; otherwise, it assigns
  the between-subject degrees of freedom to the effect (see Schluchter
  and Elashoff 1990). If there are multiple within-subject effects
  containing classification variables, the within-subject degrees of
  freedom are partitioned into components corresponding to the
  subject-by-effect interactions.

SAS will also let you specify your own DF using the DDF = option, and has many choices besides BW, each of which will give different numbers.
I have even seen it recommended (I forget in which book) that the DF for the denominator not be reported. 
Update by StasK: the FAQ on the degrees of  freedom for a comparable mixed model R package lmer points to a post on R-help mailing list by Douglas Bates, the author of the package, explaining the controversies surrounding these degrees of freedom.
