Should I Conduct Multilevel Analysis?

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I want to study the classroom and the school effect/result on the pupil's success (or not) at school. I also want to know the effect of the age, the gender (of the pupils) and if the pupils have or not repeated 1 year school on the pupil's success (or not).

The pupil's variables are : - the age, (11 or 12) - the gender, (male or female) - if they have repeated one year, (yes or not) - the success, (yes or not)

The other variables are : - the classroom they belong to, (A or B) - the school they belong to, (C or D)

I would like to proceed by GLMM, more precisely a multilevel (hierarchical) model using R on my dataset knowing that firstly I am looking for the effect of the classroom and the effect of the school on the pupil's success (or not) and secondly I want to know the effect of the age, the gender and if the pupils have (or not) repeated one year ? Many thanks for your help !

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关键词：Multilevel Analysis conduct Analysi alysis

In a less technical and more general note: if you only have two classes and two schools, there's not much point to building a mixed model, you should probably just use a regular generalized linear model with school and classroom within school as fixed effects.

As already pointed out by Ben Bolker, I think a GLMM might not be the adequate analysis strategy for you, although it might seem appropriate at first due to the nested structure of classes in schools. However, as the lme4 faq tells you:

One point of particular relevance to 'modern' mixed model estimation (rather than 'classical' method-of-moments estimation) is that, for practical purposes, there must be a reasonable number of random-effects levels (e.g. blocks) — more than 5 or 6 at a minimum.

As you have only two levels, for your random factor school and only two or four levels for class, running a glmm will not work properly.

Simply run a binomial glm on your dv, e.g.:

m1 <- glm(success ~ age + gender + repeated + school * class, family = binomial,
      data = your.df)
summary(m1)