When plotting contrast estimates and 90% C.L. it is probably better to choose 
the F contrast of the effects of interest, i.e. in that case eye(3) and not 
the contrast comparing the three groups. (That's actually what I meant but I 
didn'T write it...).

Thilo

On Wednesday 11 June 2008 15:58, Thilo Kellermann wrote:
> Dear Henrike,
>
> your F-contrast is in principle correct, but maybe that you loose one
> degree of freedom (but it might be that SPM automatically looks for linear
> dependencies in your f-contrast). The "typical" F-contrast would look like
> this:
> 1 -1 0
> 0 1 -1
> Of course you should get at least almost the same results. (The comparison
> between group one and three is implicitly modelled by this contrast, i.e.
> the additional vector [1 0 -1] is not linear independent from the other two
> rows).
> Question 1 and 2: Your model is appropriate and your interpretation of your
> F-contrast is right.
>
> You are right that t-contrasts as such are not suitable to be used as post
> hoc tests. But what you can do is making use of the "Mask with other
> contrasts"-feature, i.e. choose "Yes" here and enter your F-contrast at
> your desired significance level as mask. But still you will have to figure
> out first which group has the bigger contrast estimate of the two.
>
> However, the answer to question 3 is not that easy (are any other clusters
> showing up in a t-contrast "statistically real" or not?). Generally a
> single t-contrast is more sensitive because these are directional tests. In
> contrast F test look for differences as such no matter which group has
> "more". When we want to be conservative and our priority is to keep our
> type I error probability at the specified level (usually 5% but actually I
> do not know the threshold you used) you are right when keeping the null
> hypothesis for the other clusters while rejecting it only for the cluster
> showing up in the F-contrast.
>
> Chances are, however, that you commit a type II error (keeping the null
> while the alternative is true) although we cannot determine the exact
> probability without some further information. To keep it at least a little
> short: It depends on your willingness to commit a type I error.
>
> On the other hand if you have some prior information w.r.t. the question
> which of the groups should activate more than another, then it makes sense
> to report the t-contrast. (Although it should be noted that your type I
> error rate inflates with the the number of contrasts you look at).
>
> QUESTION 3: HHMMMMM......!?
>
> Question 4:
> When you have only one cluster to be described it is probably the best way
> to make a plot of the contrast estimates comparing the groups visually.
> Here I would chosse the option "Contrast estimates and 90% C.I." and choose
> your F-contrast in the next step. This gives you "nice" (my taste) and
> interpretable plots for the groups with 90% confidence limits.
>
> Hope this makes sense....
>
> Thilo
>
> On Wednesday 11 June 2008 14:58, Henrike Hemingway wrote:
> > Dear colleagues,
> >
> > unfortunately, I am sort of naive, from a statistical point of view. Some
> > simple questions:
> >
> > The experiment is the following: three different groups performed a
> > standard encoding task. I am interested in activation differences between
> > these groups. For that, I used a ANOVA design, 1 factor with 3 levels
> > (group 1, group 2, group 3). QUESTION 1: Is that appropriate?
> >
> > To look at general group differences, I used the following F-contrast
> >    1 -1 0
> >    1 0 -1
> >    0 1 -1
> > This gave one nice cluster. In my opinion, this means that at least two
> > groups have activation differences in this cluster. QUESTION 2: Is that
> > the correct interpretation?
> >
> > Now I wanted to find out which groups differ. I used, as post-hoc
> > t-tests, simple t-contrasts and found out that group 1 has more activity
> > than group 3 in the nice cluster from above. However, these t-contrasts
> > showed also other activation differences between the groups. I assume
> > however that I am not allowed to take these differences as "statistical
> > real" since they did not appear in the ANOVA F-contrast. QUESTION 3: Is
> > that correct?
> >
> > At last, I still did not know how to interpret these group differences.
> > It might be that - both groups have activation, one more than the other.
> > - both groups have deactivation, one more than the other.
> > - one group has activation, the other deactivation.
> > This is probably not unimportant to know.
> > I tried several options:
> > 1.) I used the "plot button", fitted response, against scan/time. This
> > showed me nicely that group 1 differs from group 3, but I had a bad
> > feeling: I did not know whether to use the "adjusted" or "fitted"
> > response, I did not know how to extract the data from the graph, I did
> > not know what SPN does to the data, etc. 2.) I used the "eigenvariate
> > button" to extract the first eigenvariate from all voxels within my
> > cluster. This gave totally different results. Does it only work when I
> > want to look at a real time series? 3.) I decided to go back to the raw
> > data, extracted the mean intensity value in the cluster from the
> > con-images of all subjects. Again, I got different results QUESTION 4:
> > what is the right way to proceed?
> >
> >
> > Best wishes,
> > Henrike

-- 
Thilo Kellermann
Department of Psychiatry and Psychotherapy
RWTH Aachen University
Pauwelsstr. 30
52074 Aachen
Tel.: +49 (0)241 / 8089977
Fax.: +49 (0)241 / 8082401
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