Dear Roman,
It is hard to know how much the effect is due to correlation or just whether
there is a slight change in statistic to be above/below threshold in the two
cases. It is true that FIRST still cannot take general contrasts and so you
need to have your effect of interest in a single EV, and that it also demeans
all EVs for you.
If the mean age between your different groups is actually different then
you could easily be in the situation where an age EV *on its own* could
show an effect driven by group difference. However, if you include both
age *and* a group difference EV then this should not happen as you
should only see the *unique* parts of the effect and not whatever is shared.
In the case that there is a mean age difference between your groups
then there is no way to actually separate the effect beyond just putting
both EVs in the analysis (group difference and age) and seeing what
unique effects are found by the GLM. Anything else that you do is
artificially forcing the difference to be treated only in one way. For
example, you can demean age within each group and then put this
into one EV, but it is artificially saying that any difference seen between
the groups could not, under any circumstances, be due to age and must
be only due to group difference. It is hard to know how this could be
justified, as can you really be sure that the differences are not due to age?
The safest and truest thing is just to put both in and see what the GLM
gives you.
One thing with FIRST is that you cannot get the equivalent of a joint
F-test in the standard GLM, where it shows you the effects that could
be due to one or other or *both* of the EVs in the model. If you do
an analysis without one of the EVs then you get some of this information
and combining across both gives a reasonable estimation of what this
combined effect is like (although it is not exact). This might help you to
see what is jointly driven by age *and* group difference, and you should
only look at these in terms of what they are like when taken together
and not consider them separately.
Sorry for the rather long answer.
Correlated regressors is never easy, and the current setup in FIRST
makes it a little more difficult.
By the way, the best way of dealing with this issue is to only recruit
subjects so that the mean age in each group is matched! Alternatively,
you could exclude certain individuals to reduce the age difference
between the groups if the age difference is not large and you have
enough subjects. But nothing beats designing the experiment so that
there is never a correlation between effects of interest and effects of
no interest.
I hope this helps though.
All the best,
Mark
On 19 Aug 2011, at 03:55, Roman M wrote:
> Dear FSLers,
>
> I am having a little problem with a vertex analysis. I have 2 groups (e.g., control/patients) and several covariates. Here is the issue: when I put BOTH control and patients in one analysis, and use age as a covariate, I see an effect of age. Yet, if I look at controls alone and patients alone, the effect of age isn't there at all, which tells me it's not a real thing. Indeed, I see age and group are correlated -- so is age "stealing" some of the variance that should go in the Group effect?
>
> 1. What is the right way of orthogonalizing (e.g., ortogonalize age with group, or viceversa?) I'm not sure I appreciate which is the correct way of doing this.
>
> 2. Is still not possible to specify 2 different groups (in the Glm) for a vertex analysis, and is it still the case that I don't need to de-mean the covariates (e.g., age), since first_utils does it automatically?
>
> Any other/better way of dealing with the correlation issue?
>
> Thank you
>
> Roman
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