Hi,
I'm reposting this under a different heading, as the original thread has gone in a different direction. Basically, I'm confused as to how to best analyze a PET study.
I have two (unbalanced, N=21 and N=23) groups. Each subject was scanned (PET) before and after treatment. Initially, I modeled this using a full factorial, with only group and treatment factors. I specified that the condition should be a dependent factor. Thus, the resultant covariance matrix had compound symmetry. It was my understanding that the degrees of freedom would be adjusted to reflect the dependencies between scans, but the effective degrees of freedom (SPM.xX.erdf) is equal to (2*(21+23))-4 = 84 - which is what I would expect for the degrees of freedom if there were no dependencies. Does that mean that the degrees of freedom *aren't* corrected, and I must use a flexible factorial with subjects as a factor?
I also tried modeling this with a flexible factorial, incorporating subject as a factor. I have another issue here. The covariances calculated between the before and after treatment images are much smaller than those calculated in the full factorial model, which I found to be very odd, and I'm hoping someone can explain this to me. (I think this was pointed out in an earlier post: http://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind0701&L=SPM&P=R47197).
Which covariances are correct?
Thanks for all your help!
Allison
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