I have a simple model:
$A$ is hypothesized to be a predictor / regressor / explanatory / input variable
$B$ is hypothesized to be the response / regressand / explained / outcome variable
So, the relationship looks something like:
$A\longrightarrow B$
Additionally, $C$ is hypothesized to be a moderator of the relationship between $A$ and $B$.
When I run the regression by including all the variables ("enter" procedure in SPSS), none of the relationships are significant.
When I use "step-wise" regression, and let SPSS choose the variables to include, $C\times A$ has a statistically significant effect on $B$, but SPSS stops the "step-wise" regression procedure before including $A$. I suppose one can assume that $A$ doesn't have a statistically significant effect on $B$ (after the inclusion of $C\times A$ in the model).
Thus, I have a statistically significant moderation term $(C\times A)$ , but the main term $(A)$ has not been included in the model.
What can I do with such a result? I was taught that moderation effects are not valid if main effects were not included in the model. Is there a way around that admonition? Is there some way I could still employ this result profitably?
Thus, I have a statistically significant moderation term (C×A), but the main term (A) has not been included in the model.
What SPSS command you use? Linear regression command does not create interactions internally. Did you compute AC youself first and include three variables A, C, AC? $\endgroup$