I need help in interpreting the output of an analysis with a co-variate of
interest. I want to determine the correlation between the difference in PET
images between two conditions to the difference in a self-report covariate
collected during each condition. My question is how can the Z scores given in
the Output table be converted into correlation coefficients.
A standard Fisher Z score to R table yields correlation coefficients that seem
overly high. For example, a Z score of 2.0 yields a correlation coefficient of
0.965. That is pretty high for a behavioral co-variate, and some of the
Z-scores are > 4.0, which would be r > 0.999 !
Would it be more appropriate to take either the corrected or uncorrected (for a
priori regions) p-value and then use that to find the correlation coefficient,
using the number of subjects - 2 for the degrees of freedom. Under these
conditions, a p-value of 0.01 (corrected) and 7 subjects would equal to r =
0.8745, which seems more reasonable.
I would feel more comfortable if I could get a visual confirmation of the
strenght of association. Is there was a way to generate a scatterplot for a
particular pixel value vs. co-variate ?
Details
I set up the co-variate as suggest by Andrew Holmes in his posting of Sept 25,
1998. Briefly, the difference in the two covariate scores are mean centered,
divided by 2 and multiplied by either -1 (baseline scan) or +1 (treatment scan).
The co-variate scores are then entered as a single vector. The contrast to query
for a positive slope relation between the difference in scans and difference in
covariate scores is given by [0 0 1].
Thanks.
sg
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