this question is about a way of presenting the results of an interaction I thought of, and I'm looking to see if it makes sense. If there is a better way to do what I'm trying to do (or if what I'm trying to do is not advisable), please let me know.
To simplify, let's say I've got a multiple linear regression equation with two dichotomous predictors (dummies) and an interaction between the two--let's say the DV is test score, predictor 1 is gender (M/F) and predictor 2 is course type (experimental vs. traditional). I understand that in such equations, the coefficient for each non-product term assumes the value of the other non-product term is zero. For the special case of dichotomous predictors only, this means that each coefficient assumes one of the two values of the other: so for example, the coefficient for gender applies to either the experimental or the traditional course, but not both as it would in an equation without an interaction. Which value of the other term is assumed depends on how the data are coded.
So, my question is: if gender is the focal predictor and course type is the moderator, and assuming the interaction is significant, would it be valid to run the model once with course type coded as (0=experimental, 1=traditional) and again with these values switched to (0=traditional, 1=experimental)? Then I could report two coefficients for gender, each of which corresponds to one value of course type. This would be helpful because it would demonstrate the distinct relationships of gender to test score within each course, whereas a single coefficient would describe this relationship in only one course.
From what I understand about interactions, this seems to make sense. But I have no idea whether this is something that's ever done, or if there's a better way to show the coefficients of a single dummy variable at the two levels of another. Thanks in advance!