I have a logistic HLM model with one level-1 predictor and without level-2 predictors. Random variance components are significant for intercepts, but far from significant (p>.5) for slopes. In my understanding this means that there is some variation in the level-1 intercepts but no or very little variation in the level-1 slopes. Hence, if I add a level-2 predictor to the model, this predictor could predict level-1 intercepts but not level-1 slopes (because there is no variation to predict, right?).
However, if I add a level-2 predictor, I find that both the intercepts and the slopes are significantly predicted. The cross-level interaction looks very nice and is very much in line with my hypotheses. But how can there be a significant cross-level interaction with no varation in level-1 slopes? How can I interpret this result?
In this related thread (which was very satisfyingly answered) I cannot find an answer to my question: Can I probe cross-level interactions without random slope in a hierarchical linear model?