This article provides methods for comparing correlation coefficients in variable sets, suggesting new approaches over traditional tests.
The purpose of this article is to provide simple but accurate methods for comparing correlation coefficients between a dependent variable and a set of independent variables. The methods are simple extensions of Dunn & Clark's (1969) work using the Fisher z transformation and include a test and confidence interval for comparing two correlated correlations, a test for heterogeneity, and a test and confidence interval for a contrast among k (>2) correlated correlations. Also briefly discussed is why the traditional Hotelling's t test for comparing correlated correlations is generally not appropriate in practice
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Meng et al. (1992) studied this question.
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