Stats beginner here.
I have a dataset composed of observations taken from 16 separate
experimental panels, each nested into one of 4 conditions (Treatment A
Level 1, Treatment A Level 2, Treatment B Level 1, Treatment B Level 2; see
photo: http://imgur.com/ZbzFPNq). There are 100 observations of the
dependent variable for each of the 16 panels (1600 total obs).
I'm trying to determine main effects of both treatment types, and any
interaction effect, accounting for the within-panel variation. I'm trying
to determine if the DV differs significantly across conditions.
Is the appropriate model a mixed model with panel-groups as a random
factor? Eg:
glmer(DV~TreatmentA*TreatmentB + (1|panel.group))
how should I be constructing the code?
or... is it more appropriate to treat panels as a fixed factor?
Thanks!
KC
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