A handful of critical choices in multilevel modelling

Multilevel models are a key tool for the analysis of clustered data in a wide range of fields. The paper discusses a handful of critical choices in multilevel modelling. Some choices are peculiar of the multilevel setting, like the specification of the multilevel structure of the model, cluster-mean centering of the covariates, fixed versus random effects, and the specification of the distribution of the random effects. The paper also considers some choices which are more complicated in the multilevel setting, namely sample size requirements, accounting for the survey design, and handling missing values. Each issue is briefly outlined, referring to the current literature for details and further discussion.