I have a repeated-measures design where participants were measured 4 times each on 2 consecutive days. There were 2 conditions, randomly attributed to day 1 and day 2 for each participant.
So, this is how the head of my data 'dat' look:
'subject' is just the IDs, 'DV' is my outcome/dependent variable.
I want to know how 'day', condition ('cond'), and 'measurement' influence 'DV' using R's lme package. Even after a rather excessive search, I'm still not sure how to set the random effects...
So far, I've done something like:
m_base <- lme(DV ~ 1, random = ~1|subject/cond/measurement, data = dat, method = "ML")
m_base <- lme(DV ~ 1, random = ~1|subject/day/measurement, data = dat, method = "ML")
and then stepwise added my variables as fixed effects using
update() and compared the models using
Still, I'm not sure about how to set the random effects, since 'measurement' is somehow nested within both 'day' and 'cond'. Does anyone know a correct way of implementing my question?