I'm trying to run a multilevel model where I have approximately 30 individuals and anywhere from 20-50 time points per individual. I can cluster them by the individual since the dataset is longitudinal in nature, but there's also reason to believe that each time point is different from the others due to changing environments. With this in mind, would one double-cluster by both individual and time? Or would this introduce biases in the parameter estimates?
I'm not well-versed in multilevel models so I'm not sure if just clustering by individual is enough to account for changing environments associated with time (in my case, every individual is subjected to the same changes).
I already tried running the model with double-clusters and the model runs perfectly fine, so there isn't any issue as stated in this question here. However, I'm just concerned that whatever results I am getting is biased or has errors in it.