I have a sample with 400 cases. When I run my full model, which includes 13 predictors, the interaction term is non-significant. However, when I run a model only including the three variables involved in the interaction (two main effects + interaction effect), the interaction effect is significant. I've tracked the problem down to one of the other IVs in the full model, which is extremely highly correlated with one of the items in the interaction term (r=.75), although collinearity tests indicate no problems with multicollinearity. Both the items included in the interaction term and the one which is highly correlated are composite scales, consisting of between four and nine items each.
My question is: When reporting results from my analyses, I have a hypothesis about the interaction. Is it okay to talk about the significant interaction effects that exist in the absence of controlling for this other IV, which while not theoretically important is still important to include in the regressions. If so, how do I justify the inclusion?