After reading a lot of material on nested and crossed effects, I am still unsure on whether the random effects in my design are nested or crossed. I would really appreciate advice from some more seasoned linear mixed model users!
Design: Two independent groups of Participants (before and after event) completed questions several times a day for several days. Within each of these two groups (before and after), there are two age groups.
For each question, I would like to run a linear mixed model with event (before and after) and Age group as fixed effects (and their interaction) to ask whether affect significantly changed before and after event and whether this is different for the two age groups.
As each participant contributed up to 35 data points, I would like to account for within-person variance as well as the day number (1-7) and signal number (1-5 each day).
I am trying to figure out whether these random effects should be specified as crossed or nested random effects. As far as I understand, here are some of the possibilities, where subject = IDNO, day number = DAY and signal number = SIG:
lmer1 <- lmer(question1 ~ event*AgeGroup + (1|IDNO) + (1|DAY) + (1|SIG), data = df1) lmer1 <- lmer(question1 ~ event*AgeGroup + (1|IDNO/DAY/SIG), data = df1)
From the design specified above, which random effect structure makes more sense? Or does another specification make more sense?
Any help with this would be greatly appreciated after lots of independent research which has left me unsure!