Lets say I have
v2 and I have 400 data points for each variable. I expect them to be negatively correlated, so a simple approach here would be to calculate the correlation
However, the 400 pairs of data points came from 40 different people, where each person only offered 10 pairs of datapoints
One way I could approach this would be to predict
v2 and additionally treat
person as a random effects variable, so something like
v1 ~ v2 + (1|person)
I'm concerned about only have 10 datapoints per person. On the one hand a straight correlation doesnt consider the fact that the data is likely clustered due to multiple rows coming from the same individual. On the other hand, I only have 10 datapoints for an individual which may skew the correlation value for any given individual...
Is that a sufficient amount of data per level to run a multilevel model like this? Or does the N per level not matter, but rather the total N (400) being the only important factor?