Multilevel Model or Simple Correlation Coefficients I am interested in the relationship of several variables (questionnaire score = q1 (0-24); physiological measures = phys) across consecutive conditions (block = 4 consecutive conditions) and between experience groups (exp = 2 groups as a dummy variable). I have an overall sample size of N = 27 (12 = high exp., 15 = low exp), which all completed the 4 conditions.
My main interest now is whether a multilevel model is appropriate here to investigate whether the association between q1 and phys differs significantly across block and experiene groups.
The model would look something like this:
q1 ~ 1 + block * exp * phys + (1 | id)

Furthermore, I have several physiological measures. Does it make sense to incorporate them all in one model or should I rather go with a model that includes only one physiological predictor?
I am a little afraid that it would be easier to simply calculate the correlation coefficient for each subgroup at each block and compare those coefficients?
I am grateful for any sugggestions!

As mentioned in the comments, there are repeated measures within id, so fitting a mixed model with random intercepts for id would be a good idea.
Splitting the data into subgroups and comparing correlations is not a good idea, since it loses statistical power (since each test is on a smaller sample size) and you would also need to handle multiple testing issues.
