Differences between outcomes glmer models
I ran two mixed effects logistic regressions: Model 1 contains only main effects (let’s say of A, B, C, D, E, F). Model 2 contains the same variables + interactions between the variables. In Model 1, all variables except one are significant predictors. In Model 2, no interactions are significant. However, two of the main effects are no longer significant in Model 2. Also, the variable that was not significant in Model 1 is significant in Model 2.
I know not everything should be about p-values, but can someone explain whether such a shift in significance is common and what can be possible causes for this? I detect no multicollinearity.
I read somewhere that Model 2 takes the interactions into account: if I have an interaction A x F, then the ‘main effect’ of A holds for F = 0. Is that true? If so, I have multiple interactions containing the same variables (for example, A x E and A x F), so how do I figure out how to interpret the ‘main effect’ of A in Model 2?