I'm not entirely sure of fitting the model for experiment we've made. The variables and relevant description are as follows:
- ID - participant ID
- Trial - 60 for each participant
- Memory - between subject binary factor
- State - within subject binary factor
- Correct - whether classification a participant made was correct or not
- Rating - the judgement made after each trial on four point Likert scale
Procedure brief: each participant (N=60) was randomly assigned to experimental or control group (Memory) and had 120 Trials (60 for State = 0 and 60 for State = 1). Each trial composed of perceptual classification (Correct) and judgment of how easy it was (Rating). The classification problem was randomly selected from two groups each trial (State).
I would like to calculate what impacts the performance (Correct) most - is it memory, state, a specific rating on a scale or any combination of above? I'm not interested in between subject variance, on the oposite, it is a random factor here. Also, it appears that there is bias in responses on Likert scales, so that part of variance should be excluded too.
The way I was thinking to approach this is generalized mixed linear model, but I'm not sure I'm doing it right; there is what I've got so far:
model = glmer(Correct ~ (1|ID/Rating) + Memory * State * Rating, data, family=binomial, control = glmerControl(optimizer="bobyqa", optCtrl = list(maxfun=100000)))
Is this approach correct? I'll appreciate your input.
Relevant resources I used: