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Thank you for the kind responses, I realize I made a simple error in my first question -- and yes the DFs were in fact correct. A bit of a brain malfunction there.... However, I still have some concerns about using Pooled vs Partitioned error ANOVAs. I have run my analysis both ways, and although they give the same pattern of results over the brain, the statistics fluctuate dramatically. What is highly significant in the Pooled Error ANOVA is not quite so for the other (specifically for the FWE stats). As Rik mentions in this post (https://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=ind06&L=SPM&P=R149076) it is possible this arises from the fact that RFT is rather conservative for low error df's (<~12, though I have 18[?)]), and using all the data to estimate a pooled error could help. There seems to be a lot assumptions inherent in pooling your error term (e.g. only a single source of error!), and unless one is ready to understand and accept them, would it not be best to go the classical route of the more conservative partitioned error ANOVA? What do most of you out there use? Thank you again for your input! Conor