Measure the influence of variable A on variable B's impact on variable C I have three variables. I know that variable B has a correlation with variable C. I'm interested in whether variable A impacts variable B's relationship with variable C, and not on whether variable A directly impacts variable C.
Multiple regression seems not appropriate for this question as this would be measuring each of variable A and B impact on C, which is not what I want.
My other thought was to simply multiple A by B and then correlate with C in order to determine whether this has increased / decreased the correlation.
Any other ideas to determine whether A impacts B's relationship with C?
Or thoughts on why either of the above is or isn't useful?
 A: The best approach is to combine both any "direct" effect of A along with the interaction represented by the product of A times B in a multiple regression. The general form for such a model, as it's represented in R syntax, would be:
C ~ B + A + A:B

That model (if you use the standard treatment coding) will provide:

*

*an intercept--the value of C when A and B are both at their reference
values (0 for a continuous predictor),


*individual coefficients for A and B, representing how much C changes
with respect to each of them when the other predictor is at its
reference level, and


*a coefficient for the interaction A:B (which is just the product of A
times B for continuous predictors), representing how much the
individual associations of each of A and B change as the other one of
the pair changes.
For this to be most interpretable, you should include A in your model in addition to the interaction coefficient even if you don't care specifically about its individual association with C.
