Searching previous threads has not provided me with an answer. I am modelling data from an experiment. The setup was as follows: Stimulus set of 110 images. 454 participants. Each participants rated 75 images (random from the pool of 110) and reported visual appeal.
Two measures of the images (visual clutter and colourfulness) are shown to have moderate to high correlation with visual appeal. I therefore want to include them in the model. I want to use both subjectID and stimulus (filename of stimulus) as a random effect. In R, using lme4, I enter:
ae.model = lmer(ae_rating ~ clutter_se + colourfulness + (1|subjectID) + (1|stimulus), data=lmm.data)
and get "Error: number of levels of each grouping factor must be < number of observations:". There is probably something I am not completely understanding about using mixed effects models. Does the above code somehow nest the random effects? Then I would understand the error (as subjects*stimuli > observations).