Interaction / Moderation effect linear regression


I have a question regarding a moderation effect with linear regression. I have hypothesized for a significant main effect and another hypothesis for a significant interaction effect. My tutor told me to create 2 models. In the first model without interaction I have one variable (which is hypothesized for significance) that is insignificant and in the second model with interaction, this variable is significant, but the hypothesized interaction term is not. How do I interpret this? Do i reject h1 and reject h2 or do I accept h1 based on the second model and reject h2?



Omega Contributor
What is this for? An internal project or something you plan to try to publish?

So a priori you say hey X! is associated with Y and also X1*X2 is associated with Y. I was checking before to make sure you had the main terms in your interaction based model and you did.

So the slight possible conundrum would have been that typically if your interaction term is significant, you no longer interpret your main terms, in that their individual interpretation is no longer of interest, since the variable's effect is conditional on another term.

So X2 is not significant either? I don't see a problem with writing this up and reporting the X1 model and also reporting the interaction model was null. It is important to just report what your original intentions were and to report null results, so other's know your intent.


Fortran must die
Just because you have interaction does not mean you are not interested in the main effects. It means you are interested in the main effects at some specific level of the other main effect. This is commonly called simple effects in ANOVA.