I got a research question, where the hypothesis (derived from theory) postulates, that the relationship between predictor X and outcome Y is moderated by W (X+W+X*W -> Y). All variables are sums of likert-scaled items and treated as interval/metric. N=34.
Ignoring stepwise inclusion of variables, I just did a moderation analysis according to the hypothesis and found that everything is hypothesis conform. R-Squared is .20 which is low but expected because other, not included factors are known.
But when I just check the "lower" regressions, i.e. X -> Y, W -> Y and X+W -> Y, then all coefficients are close to zero and none is significant (p-values are incredibly high, p>0.6) and R-Squared is very low also.
What is happening here? Is the expected outcome of the high level regression with interaction term just a statistical artefact? I would like to understand what may be the reasons for this cumbersome result. And also I would like to know if/how I can interpret the results.
EDIT: To avoid misunderstandings: I meant that in the model with interaction term, the coefficients of X, W, and X*W are significant. So there are main effects of X and W as well. But this is cumbersome, because in the model without interaction, neither the coefficient of X nor the coefficent of W are significant and the coefficient values itself are pretty small as you can see in the output:
So if there are main effects in the interaction model, these have to appear in the model without interaction as well, isn't it? Maybe in the interaction model, have too much terms regarding the small sample size of N=34. I am still very curious about what's happening here and how I can interpret findings.