I am in Psychology and trying to explore the utility of mixed modeling for analyzing my repeated-measures data in a factorial experiment. The primary reason for using mixed models is that I would like to avoid the common practice of averaging data collected in the same experimental condition. My understanding is that it's typically required for repeated-measures ANOVA that there is only one observation per condition per subject. What if you have several replications of the same condition for the same subject?
To be more concrete, I have two conditions a between-subjects factor A (2 levels) and a within-subjects factor B (3 levels). There are 4 repetitions of each level of B for a total of 12 randomized order trials per subject. Usually, I would simply average across these 4 to get an estimate for the performance of the subject in the condition and then run, but it seems that this way I'm throwing valuable information about variability. How to deal with such data using mixed modeling in R (I've been using lmer function). Maybe including trial number as another variable would work? I tried including trial # as a random factor together with the subject, but its estimated variance is very low compared to error and subject.