In my dataset time is nested within person. I have done a multilevel analysis with time on the first level and subject on the second level. There is a significant fixed effect of time on the dependent variable. I interpret this as that there is a significant increase in Y over time. There is also a fixed effect of a second level variable (which is continuous and I will call the variable V at the moment). So, the higher people are in V, the higher the dependent variable.

In the random slope model, the fixed effect of time and of variable V are both still significant but there is also a significant cross-level interaction with time and variable V (the same second level variable that is continuous). The estimate of the cross-level interaction is negative. I interpreted this as people high in V have less growth in Y over time than people low in V.

Often you should not interpret the main effects when the interaction is significant. Though my question is, should I interpret both the interaction and the main effects? I think the main effect of V says something about the mean variance in Y, while the cross-level interaction says something about the variance of the slope between time and Y. So, the main effect is about the mean level of Y during the whole experiment while the interaction is about the growth of Y during the experiment.

I have read a lot about this and searched for it but I could not find the answer. Any help is much appreciated.


I think you already have a very good understanding of your models and the interpretation. Just to ensure I understood you right, you found:

  1. A positive fixed effect of time (level 1) on your outcome Y
  2. A positive fixed effect of V (level 2) on Y
  3. A negative cross-level interaction of V*time

I f that is correct I see no reason why you should not interpret all effects. That means:

  1. The general mean effect of time across all participants is positive indicating a possitive association between time and your outcome.
  2. People scoring high on V also have higher mean values of Y
  3. For People scoring high on V the association between time and Y is less positive. That means, they deviate from the overall fixed effect of time from 1.

Before you enter the interaction term you could check a random slope model for time. There should be a significant random effect of time indicating that individuals deviate from the overall trend. Your interaction term then seems to be capable of explaining this individual deviations.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.