Crossed or nested random effects in a repeated measures and a between-subject design? 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!
 A: From your description, these are crossed random effects.
For a factor, A, to be nested within another, B, this means that for any particular level of A, this occurs within one, and only one level of B.
So, in your study, for example, if signal 1 occurs only in day 1, and signal 2 occurs only in day1, while signal 2 occurs only in day 3 etc, then we would say that signal is nested in day. This does not appear to be the case, because each signal appears in each day, and on each day there were multiple signals - that is, they are crossed.
Also for example, if day 1 occurs only within subject 1, while day 2 occurs only in subject 3, day 4 in subject 3, etc, again we would say that day is nested in subject, and again this does not appear to be the case because each subject was measured on each day, and on each day, multiple subjects were measured; hence they are crossed.
So your 1st model would seem to be appropriate:
lmer1 <- lmer(Affect1 ~ COVID*AgeGroup + (1|IDNO) + (1|DAY) + (1|SIG), data = df1)

See this answer for further details:
Crossed vs nested random effects: how do they differ and how are they specified correctly in lme4?
