Recently, I fitted a fitted a relatively straightforward group by time model to some trial data:   proc MIXED DATA=…; class ID cond occas; model CESD= cond occas cond*occas/ DDFM=SATTERTH SOLUTION; repeated occas / type=un Rcorr subject=ID;     I decided to reverse the time variable (cond) to improve parameter interpretability. As expected, this produced appropriately changed model parameters but fit was identical in every other way except that some of degrees of freedom for tests of fixed effects and parameter estimates, calculated according to the Satterthwaite approximation were quite different. While the differences didn't change the outcome of significance tests in this case, there could be cases where this is not the case.   I can understand that with some R matrix structures and some recodings of the time factor, the model fit would differ and df would change, but I used an unstructured matrix: essentially the same model was being fit. I've looked at the math behind the Satterthwaite approximation can't see why reversing the categories would matter.   I'd be very interested if anyone can shed some light on this.   Andrew ... View more