Interpretation of the magnitude of the effect of interaction terms I have a model with interaction terms:
$Y = a + b X_1 + c X_2 + d Z + f X_1 Z + g X_2 Z + error$,
where $X_1$ and $X_2$ are (0,1) indicator variables and $Z$ is a continuous variable (with a distribution whose standard error is 0.25).
After an OLS regression, I get that $a$, $c$ and $f$ are the only significant coefficients. Specifically, $f$ is equal to 0.05.
Can I interpret the effect of $X_1$ on $Y$ using the standard deviation of $Z$? Specifically, is it correct to state that, if $X_1 = 1$, a one standard increase in $Z$ (0.25) at the mean increases the value of $Y$ in 0.05 * 0.25 = 0.0125?
At the same time, if $c$ is equal to 2, can I simply conclude (given the significant coefficients) that, for $X_2 = 1$, $Y$ increases in 2?
Thanks in advance.
 A: Interpreting interaction terms is actually quite controversial and the literature is loaded with contradictions. My comments are based on a close reading of Aiken and West's book Multiple Regression which has one of the best methodological breakdowns for dealing with interaction terms that I've ever read. More recent papers (e.g., Understanding Interaction Models: Improving Empirical Analyses by Brambor, Clark and Golder, Political Analysis, 2006) aren't anywhere near as rigorous, contradict solidly documented A&W findings and add to the existing confusion. 
First off, introducing an interaction shifts the interpretation from a main effects only model interpreted at the means of the Xs to a model with interactions that are interpreted at zero. This suggests mean centering your variables before taking an interaction term. The coefficients and std devs don't change, but the interpretation does. 
Next, the main effects for the X's serve only to adjust the location of the intercept on the y-axis. It doesn't matter for the interaction term whether the X's are 0 or 1.
This is all a long way of saying that your suggested calculations need more information. In other words, it's not enough to assume that Y changes by '2' if the X2 coefficient, 'c,' is 2 since the effect of X2 is a conditional adjustment to the intercept. So, the impact on Y of X2 is not '2' when X2=1, it's 'a + 2'. Similarly for the calculation of the interaction between X1 and Z, it's not about shifting Z by 0.25 SDs. The full equation for estimating Y when X1=1 and Z=0.25 would be:
Ytilda = a + b + (0.5 * 0.25)

which would not equal 0.0125.
