I was wondering if someone could explain this to me.
I've carried out a hierarchical regression, entering 9 independent variables at Step 1 (Block 1), and 8 interaction terms at Step 2 (Block 2). The 8 interaction terms relate to X1 interacting with each of the other Xs. X1 is dummy coded, X2 and onwards are centred.
I'm confused by the results. Model 1 is significant, but Model 2 isn't. From what I understand, this means none of the interactions are significant enough to interpret in Model 2, and so I should focus on Model 1. Please correct me if I'm wrong.
Surprisingly, Model 1 shows 2 out of 9 Xs are significant, when really only 1 should be (i.e. X1, the dummy coded experimental conditions). Purely out of interest, I used a simple regression to check X2 (the surprisingly significant main effect) against my dependent variable, and it came back F(1, 162) = .174, p = .677, with an R2 of .001. The "significant" independent variable in Model 1 isn't significant outside of the Model. Is there any reason why an IV would be significant when grouped with other IVs but not on its own?
Can anyone explain where I go from here, now that Model 2 is non-significant. Theoretically, at least one of the interactions 'could' have moderated the relationship, though the literature's murky on it. I'm considering using Hayes' Process to check each interaction separately, but using Bonferroni to correct for the repeated tests will reduce my alpha considerably.