>>Tests of correlations are notoriously sensitive to the normality
assumption. So any solutions may be subject to that problem. One
approach might be to use the Fisher z transformation for the
correlations and then test to see if each is 0 or not. I'd be cautious
about any p-value citations. To compare correlation A1 with A10 you
would use z(A1)-z(A10) and go from there.<<
Yes, be wary.
Fisher Z helps as differences are much more linear on the Fisher Z
scale.
You may also want to set the problem up as an SEM and use SEM software,
e.g., the free MX by Mike Neale. http://www.vcu.edu/mx/ If your sample
size is large, you could use AWLS.
Bootstrapping is another possibility.
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