I am once again running some moderation models and in addition to moderation I am interested in simple main effects. On the output I get the coefficients for the product term and its components.
I am wondering if the coefficient for components gives the simple main effects or if I need to run separate analysis for this. In other words, I have:
COND MDD CONDXMDD
Does the coefficient for COND gives the simple main effect for that variable or is that coefficient "conditional." I ask because when I run simple main effects outside of the context of interaction (aka in separate runs for COND and MDD), I get different mean difference or coefficients for COND and MDD. I hope I am clear in what I am asking. Thanks very much.
What do you mean by partial regression coefficients as they should be? If I wanted to report the main effects, do I report these partial ones? Or do I run the model again without the interaction? I ask because the coefficients are different in the two runs and I just don't know which output to report the main effects from. If I use the coefficients from the interaction model the partial coefficient stay significant after i apply the Holm modified Bonferroni to correct for experiment wise error because I am running multiple tests. But I run it without the interaction -- by looking at simple main effects at each level of the moderator variable then the effect is no longer significant when I apply the Holm modified Bonferonni.
Thank you. Please help me with one more thing. I received the answer below in response to a question I posed regarding moderation. At the time, I thought I "got it" but I'm afraid I'm not certain I am doing this correctly. Esentially, I have 3 regression paths.
I did X - 0 = Interaction for GROUP = 0 and MDD = 0. I then replaced X in the equation and got what I think it is the mean for GROUP and MDD present. I don't know how to get the means for FAMILY AND MDD PRESENT AND FAMILY AND MDD ABSENT.
You don't need 4 runs - you have all the estimates you need in the run with the interaction. You just combine the estimates in 4 different ways in line with regular dummy variable regression:
- the intercept gives the mean when both dummies (and therefore their interaction) are zero
- the intercept plus one slope gives the mean for the cells where you have 0, 1 or 1,0
- the intercept plus both means gives you the mean for the cell where both dummies, and therefore their interaction, are 1.