What does 'level' mean in regression modelling This is a very simple question, but I'm a bit confused. I know that in GLMMs, the random effect should have a minimum of 5 'levels'. Based on this:
"Random effect models have several desirable properties (see above), but their use comes with some caveats. First, they are quite ‘data hungry’; requiring at least five ‘levels’ (groups) for a random intercept term to achieve robust estimates of variance (Gelman & Hill, 2007; Harrison, 2015). With <5 levels, the mixed model may not be able to estimate the among-population variance accurately."
(Harrison et al., 2018)
Using a completely random example, say your random effect was "School", and you had 9 schools, with a minimum of 6 datapoints for each school - which of these is the level? Do you need to have >5 schools (fulfilled as you have 9) or do you need to have >5 datapoints within each school (fulfilled as you have minimum 6)?
I know this is very basic, but I haven't yet been able to find a straightforward explanation of this online.

In this passage the schools are the levels because those are the groups into which students divide. More precisely, the school variable, which might number or name them, has 9 possible values and each one of these is a level (of the grouping variable).
That G&H speak of levels of a variable (as above) and also levels of a model is indeed unhelpful. Talking about groups instead is much clearer.
For completeness: that second sense of level (the one in 'multilevel modeling') becomes important when you have information about schools and information about students. Then facts about students, e.g. age, grades, and SES are all at 'level 1' and facts about schools, e.g. county, budget, etc. are all at 'level 2'.
Hope that's a bit clearer.
