This question already has an answer here:
I have a repeated measures design with two factors (A,B). For each subject, variable C is measured 7 to 10 times in each combination of A and B.
What I usually did was do first calculate the mean across for each combination of A and B for each participants and calculated my model with:
lmer(C~A+B+(1|Subject) # I assume A and B to not interact with each other
However, one reviewer asked me to use individual trials instead of the mean for each subject and combination of A and B.
But I don't know how to create such a model. Is it the same as the one written above but without calculating the mean first? Or do I have to specify the trials as some kind of random factor? I do not assume that there is a trend over time in my trials for each combination of A and B. So at best their variation (I am not sure if the assumption can be made, that they are normally distributed, but might be the case) should somehow be included for each subject and each combination of A and B.