Hi, I have a follow-up question to this post. Are the outputs from this correlational analysis (below) still interpreted as t-statistic for the correlation between each group and age, the corresponding incorrect 1-p value, and the corresponding corrected 1-p value? If so, I have some strange results I would like feedback on.
contrasts:
> [0 0 1 0 0 0] -- patients
> [0 0 0 1 0 0] -- controls
The resulting tstat map looks randomly distributed around .8 and has a range of -2.5-4. The p stat map ranges from 0-1 but has all frequency (#6000 voxels) near 1. The corrp map ranges from 0-1 and has the highest frequency near 1 (#2000 voxels).
Does this seem plausible? How could the 1-p value for the correlation between age and FA be so highly significant for almost every voxel?
Thank you!
Mark
On May 6, 2010, at 4:51 AM, DRC SPM wrote:
Hi Amelia,
If your design is:
> EV1=Patients
> EV2=Controls
> EV3=Patients_age_demeaned (I subtracted the mean age of all patients from
> each patient’s age. I put 0s for all Controls in this EV.)
> EV4=Controls_age_demeaned ((I subtracted the mean age of all controls from
> each controls’s age. I put 0s for all Patients in this EV.)
> EV5=gender_demeaned (I subtracted the mean gender of all subjects - both
> patients and controls - from each subject's gender.)
> EV6=handedness_demeaned (I subtracted the mean handnessness of all subject -
> both patients and controls - from each subject's handedness.)
Then you can test for relationships with age within each group with:
[0 0 1 0 0 0] -- patients
[0 0 0 1 0 0] -- controls
and test for the slope with age being steeper in one group than the other with
[0 0 1 -1 0 0] -- patients > controls
[0 0 -1 1 0 0] -- controls > patients
where an F-test over either of these will test for different age
slopes between the groups.
Note that these can't be interpreted as stronger or weaker
age-correlations between the groups. In a simple or multiple
regression model, a t-contrast with a single 1 over a variable is
equivalent to testing the (partial) correlation with that variable,
but in more complicated models, you can't assume intuitively similar
equivalences. For example, you could have a steeper slope in patients,
but a lower correlation, if the patients are more variable around the
slope; the contrast above tests just for the steeper slope, not for
the different correlation.
I hope that helps,
Ged
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