I am designing an Experiment and am struggling with the random effects structure of my lme4 model. In short, I measure the Evaluation on 48 items (100-point scale) for each of 65 participants. The Evaluation of each item is measured before (t0) and after (t1) a Training.
The 96 items are for each participant chosen from a universe of 140 items in total, and might therefore (but do not necessarily) differ for each participant.
In the training, each item is assigned 1 out of 3 attentional cueing conditions (factor, 3 levels[0,1,2]). Each cell contains 16 items.
I am interested in the difference in evaluation of each item from t0 to t1, and whether it depends on the attentional cueing condition. My suggested model (in lme4 Syntax) is:
lmer(evaluation ~ time*cueing + (1 + time*cueing | participant) + (1 | item)).
However, I am not sure whether this is the right way to specify that I am interested in the difference in Evaluation per individual item. To be clear, I am not interested in fixed effects for items, but I wonder whether it is important for the model to compare item 1 at t0 with item1 at t0 within each participant. Do I maybe have to add random slopes on the item Level, even if the items are not the same for each participant and might be assigned to different conditions for each participant?
Therefore, I wonder whether I would have to nest the items within each participant with
(1 + time*cueing | item:participant).
Does anyone have any suggestions?
Thanks in advance.