I conducted a memory study and used
glmer to find if there was an interaction between
condition, i.e., if the slopes were different between conditions. It is a repeated measures analysis. I ran this model:
MODEL <- glmer(accuracy ~ time * condition + (1 + time|subject) + (1|item), data = data, family = "binomial", control = glmerControl(optimizer = "bobyqa"))
I found that the difference was not significant, so I was asked to make an analysis based on participants' performance, i.e., if the subjects with good
accuracy at time 1, regardless of the
condition, have different slopes than the ones with bad
accuracy at 1. In other words, if there is a correlation between the slopes and the intercepts by subject.
After reading about the interpretation of
glmer, I understood that this is accounted for by
(1 + time|subject), but I am not sure, because I was asked to create a new variable (
performance) and split subjects between good and bad, and then run a model like this:
MODEL2 <- glmer(accuracy ~ time * performance + (1 + time|subject) + (1|item), data = data, family = "binomial", control = glmerControl(optimizer = "bobyqa"))
I am not sure if this makes sense, or if I'm good to go with the first model. If the first model is correct, where in the output of
summary(MODEL) can I find if there is a significant correlation between the intercept and the slope of the subjects?
I am afraid that if I remove
condition from the model, the model will explain very little of the data, but adding both
condition seems a bit redundant.
Thank you in advance from your answers!