Say we have a variable A (some interaction network metric, continuous, N=20), and we want to see if A varies across B (categorical variable, 2 levels) depending on the levels of a third categorical variable C (3 levels).
So I am interested in exploring whether the differences in my interaction network metrics (A) vary across environmental gradients (B), depending on the way I measure it (C) (i.e. Do I get different results regarding differences to environmental gradient depending on the way y measure it?).
An interaction model tells me the differences between every condition (every level combination) but I just want to know the differences of A against B conditional to the three C levels.
Another option is to calculate the differences between A and B, and use it as the dependent variable to build a model.
Is there another way to model this relationship with the raw data so that I do not have to calculate a new variable?