Hello everyone,

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 â€žsignificantâ€ś.

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!

Yours

Simon