We conducted a longitudinal trial with 6 points of measurements using a pretty simple design: Each of the 24 participants completed 16 items at each time of measurement (i.e., 24 participants x 16 items x 6 points of measurements).
For analysis, I want to fit linear mixed effects models using lme4, but I'm still pretty new to this approach (especially regarding nested models). In any case, I would include random effects for subject and item:
model_1 <- lmer(dependent_variable ~ time + (1|participant) + (1|item), data = trial_data)
(Note that just using random intercepts (without random slopes) appears to sufficient based on initial model exploration).
My question: Is the model specification above sufficient or is it neccessary to account for the "nestedness" of the data (i.e., items are nested within points of measurements)?
Thank you very much for any helpful feedback!
Edit: I read a little more on the topic and based on this I assume that the data is by definition not nested. Still, any feedback is welcome.