I've run an experiment where participants (
PP) viewed 40 quotes (
Item) and rated them (
Rating). Subjects were split into two groups where the font of the quotes was manipulated (
Font). For both groups, the quotes they viewed were of two types (
QuoteType) and also of two varying metrics (
Metrics). This led to a total of four exposures - two
Font groups where
Metrics were counterbalanced, and thus 160 variations of stimuli (40 x 2 x 2).
My hypothesis is that
Rating can be predicted as a product of a
QuoteType interaction. My secondary hypothesis is that a significant effect of
Metrics should also emerge. I have no problems specifying my fixed effects - which have been contrast coded - but I am uncertain about the random effects. Here's my lmer model:
lmer(Rating~Font*QuoteType + Metrics + (1+QuoteType|PP) + (1+Font|Item), ProfJData)
(1+QuoteType|PP) as I imagined that every
PP's intercept will differ by
QuoteType, and the same for
Item. However, since
| refers to "within each" - i.e, grouping variable - and since my
Font variable is a between-subjects factor, should I not be specifying the model like so?
lmer(Rating~Condition*QuoteType + Metrics + (1+PP|Font) + (1+Font|Item), ProfJData)
Wouldn't this mean that participants are grouped into
Font? I'm uncertain about this as I usually see
PP as a grouping factor in most papers.
Any clarification would be highly appreciated.