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Am Freitag, den 11.07.2008, 14:20 +0100 schrieb Matthijs Noordzij: ... > We therefore ran Volkmar’s batch and a couple of issues remain unclarified. > > Volkmar wrote that > >In a full factorial design one would usually not model a subject >factor > >at all. The assumption is, that subjects within each group are >random. > > We agree. We didn’t mean to imply that we wanted or actually did include a > subject factor explicitly in the Full Factorial. > > >If I do the modelling as you describe (see attached batch), then I get > >(correctly) sF2 and sF3 modelled, and factors 1 and 4 set to all 1's. > > Here we got lost. The batch you specified is not a mixed design, as > condition is set to independent, instead of dependent (as we had it). This > does, however, not affect the Files and Factors specification (though of > course it does affect the results). We get 3 Factors (sF1, sF2, sF3), not 4 > as you say (or do you consider the “image” column as a separate factor?). > sF2 and sF3 indeed indicate the levels of your group and condition factors > and sF1 is the subject factor (which in your case is all 1’s because you > only have one image). > I didn't set any dependency option, but this would change neither factors nor levels. It may be that I only got sF2 and sF3 because sF1 is all one in the one image case. However, sF1 is not a subject factor, it is a 'replication' factor and is not considered, as you found out. > For our design we had 52 different subjects (26 for each group). Yet, this > sF1 only runs from 1 to 26 (again the subject factor was NOT explicitly > specified in the design, identical to your batch). After discussing this > together we reached the conclusion that for the full factorial the <explore > files and factors> option in spm5 simply gives you distorted info in that > the sF1 is meaningless in the case of independent measures. We reached this > conclusion because we get the same Files and Factors specification (with > the same 3 factors and levels) also for a 2x2 design with both factors set > to independent. Is this conclusion correct? > > Therefore, in the light of a mixed design, can the full factorial be viewed > a simply being more conservative than a flexible factorial, or as plain > wrong? > > If our initial confusion about the full factorial was simply cosmetic > (related to the <explore files and factors> option) then both options are > in a sense correct. However, the tutorial of Glascher and Gitelman (2008) > clearly indicates the superiority (in terms of sensitivity) of flexible > factorial designs over full factorial designs for Group and Group x > Condition effects. If you want to explain away inter-subject variance, you need the flexible factorial design. If you consider inter-subject variance within each group random, you would go for a full-factorial design. Volkmar -- Volkmar Glauche - Department of Neurology [log in to unmask] Universitaetsklinikum Freiburg Phone 49(0)761-270-5331 Breisacher Str. 64 Fax 49(0)761-270-5416 79106 Freiburg http://fbi.uniklinik-freiburg.de/