Is there an easy way to calculate significant difference between two largely overlapping correlations from same sample? I am comparing three different measures comprised of 10-20 items each. Each of these measures is actually a short form of a longer 40-item measure and each of the short forms shares some similar items, and thus they are quite similar. I am interested in seeing how each of these short forms are correlated with the same dependent variable. 
I come up with r= .54, .56, and .58. 
How can I assess whether these correlations are significantly different? It seems doing a simple r-to-z transformation would not be appropriate since the independent variables are quite similar and the dependent variable is in fact the same for all three correlations. As such, all of these *r*s overlap quite a bit.
I found this one paper:
Zou, G. (2007). Toward using confidence intervals to compare correlations. Psychological Methods, 12(4), 399.
But it seems way over my head and I am wondering whether there is an easier way to do it...
Any guidance would be appreciated!
 A: You could fit a regression model with all three measures as predictors, then fit a new regression model with 1 or 2 dropped out and do a full-reduced model test to see if there is a significant difference in the models.  This answers the question of "do the variables in the full, but not reduced, model contribute significantly above and beyond those in the reduced model?".  As noted already given your sample size I doubt that you will see a difference, but this will give a p-value for those that feel the need for one.
A bit more meaningful may be to fit 3 regression model, each using one of the measures and your dependent variable, then plot the 3 pairwise scatterplots of the fitted(predicted) values.  This is best done with an aspect ratio of 1 in a square plot and with an $y=x$ reference line.  This can show that the 3 measures give essentially the same predictions, or if not can show where they differ.
A: I thought you were talking about less than 100.  4000 may be barely enough.  but a difference of 0.02 is not very meaningful.
