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/
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