I am new, both to bayes statistic in general (using brms) and this community. So I am sorry if (a) I did not use the right tags and (b) the answer is too obvious.
I have data from 3 groups (n = 30 each). Each individual completed 24 trials. The 24 trials consisted of 4 scenarios (6 trials each). These 6 trials consisted of 3 types (2 trials each). These 2 different trials differed in the sequence in which stimuli were presented.
I am primarily interested in the group * scenario interaction.
So I could fit:
dependent_variable ~ 1 + group*scenario + (1 | ID)
However, I thought that actually there are not only trials nested in participants. Actually, sequences are nested in types which are nested in scenarios which are nested in participants (see above). So I tried:
dependent_variable ~ 1 + group*scenario + (1 | sequence/type/ID)
Note that I did not model scenario as random, as its already included in the fixed effects. In the second model the results are far more „significant“.
My questions are:
- Which model should I prefer? On the one side I want to account for the data structure as good as I can. On the other side I am not sure, if it makes sense the way I did it, especially because the results are so much better.
I am very grateful for your answers!