I am trying to statistically measure the effect of 2 binary factors (sex and a developmental factor) and their interaction on the correlations between 7 phenotypic traits.

Tools (such as cocor package) only allow for pairwise comparison of correlations (based on correlation coefficient and sample sizes) and then won't let me measure the interactive effects of my factors on the correlations.

The only solution I found so far is to run linear models and, to test the influence of the factor (F) on the correlation between 2 traits (T1 and T2), I use one of the trait as the response variable and test for the interaction between the other trait and the factor (supposed to show me how this factor affects the quality of the regression between my two traits). This solution is great because it also allows me to test for the interactive influence of my 2 factors on the relation between the different traits.

My main problem is that the result of the interaction in the linear models differs when I switch T1 and T2 in the models while, fundamentally, whether or not my factor significantly affects the quality of a regression should have the same answer if I test :

T1 = FxT2

or

T2 = FxT1

Any explanation or trick to go through this problem, or any suggestion for a better statistical approach of question?

Thanks

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