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Dear SPMers, In our centre we recently discussed the differences between the flexible and full factorial model in SPM5. We wondered what benefit might be gained from a flexible factorial design (over a full factorial one). The obvious advantage seems to be that the flexible factorial design gives more degrees of freedom in case only a few specific effects are considered. However, we also wondered whether the subject effect is modeled differently in the full and flexible factorial models. In our comparisons we came to some surprising results. This is what we tried and found: We have a design with 2 factors of interest (GROUP and CONDITION), each with 2 levels. Each group has 26 subjects (52 different subjects in total). In the full factorial model, we entered GROUP as an independent factor (unequal variance), and CONDITION as a dependent factor (equal variance). In the flexible factorial design we modeled SUBJECT, as well as GROUP and CONDITION as above. W added the 2 x 2 interaction as a contrast to the flexible factorial model. Then we compared the results from the two models. When looking at the main effect of CONDITION, we basically got (almost) identical results in the full factorial and flexible factorial models. This was as expected. However, when we compared the main effect of GROUP between the two models, it turned out that the flexible model was much more sensitive than the full factorial. This was surprising, because the subject effect is also (implicitly) modeled in the full factorial design. We then checked how the subject factor is modeled in the full factorial design. To our horror, we found that the factor subject (sF1) only ranged from 1 to 26 levels (instead of 52). We can only infer that subject nr. 1 from GROUP 1 and subject nr.1 from GROUP 2 are modeled together as one subject. This is obviously not how it should be. This erroneous modeling of the subject factor might have lead to the inferior sensitivity of the full factorial model, with respect to the flexible model. Our questions are as follows: 1. How is the subject factor modeled in the full factorial design? When comparing two groups with different subjects, how come that not all subjects (over groups) are modeled separately? How can this be solved? 2. How to deal with mixed designs within one model? Is it possible at all to use the full factorial model correctly, or should it be avoided at all times? If it should be avoided, is the flexible factorial model the best solution? Or should we use multiple regression analyses, or perform all within-subjects contrasts at the first level? Related issues were raised in the below thread, but we feel that the above questions remain unsolved: - questions on performing 2 x 2 within-subjects ANOVA in SPM5 http://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind0801 <http://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind0801&L=SPM&P=R11681&I=-3> &L=SPM&P=R11681&I=-3 Any feedback would be highly appreciated. Matthijs, Laura, Lennart, Rene & Rick ---------------------------------------------------------------- Radboud University Nijmegen F.C. Donders Centre for Cognitive Neuroimaging P.O. Box 9101 6500 HB Nijmegen The Netherlands