I've run an experiment where 120 participants (
PP) viewed 40 quotes (
Item) each (presented in Facebook format) and were asked to rate them on a scale (1 to 7) (
Rating is my DV).
Font (2 levels: Hard, Easy) denotes in what font the quotes were presented. This was between subjects, so participants only viewed one type of font (equally split into two groups).
The 40 quotes were of two types -
QuoteType (2 levels: Good, Bad) across both
Font conditions, so every participant was exposed to both types.
Metrics variable (2 levels: High, Low) denoted the amount of endorsement on each quote. This was also across both
In sum, I created a total of 160 variations of stimuli (40 items (20 Good + 20 Bad) x 2
Font x 2
Metrics). Four exposures were created to allow the
Metrics variable to be counterbalanced and avoid a participant having to see the same quote listed with each Metric manipulation.
My assumption is that this is a nested design due to the
Font variable. My hypothesis is that the Hard
Font can reduce ratings on the Bad
QuoteType, so I'm looking for a
Font*QuoteType interaction. A secondary hypothesis is looking for a significant effect of Metrics (High should get higher ratings than Low). I used a linear mixed-effects model using R's
My original model was specified like so:
lmer.model=lmer(Rating~Font*QuoteType + Metrics + (1+QuoteType|PP) + (1|Item), data = myData)
I'm also uncertain about how I have specified the random effects. I assume that every participant has a different intercept for QuoteType, since all participants viewed all 40 items and therefore both quote types, hence
(1+QuoteType|PP). I don't think this can be said for
Font as participants only viewed one type of font. I also assumed that
Item would have its own random effect.
Is this a nested design and if so, should I change the way my model is currently specified?
Thanks in advance.