Mathematical approaches to analysing area-level effects on health

Abstract

This chapter discusses how and why multilevel modelling is a flexible and powerful tool for analysing data with a hierarchical structure. Such data are often found in social science and public health research (e.g. when analysing pupils who are nested in classes or schools, patients in clinics or hospitals). The aim of multilevel modelling is to integrate the regression equation at a lower level of data grouping (usually individuals) with that at higher levels (such as classes, schools, neighbourhoods) into one regression equation and to incorporate covariates at appropriate levels. By using multilevel models, it is possible to adjust for similarity of the lower level units belonging to the same group of a higher level and to make overall inferences about relationships between lower level as well as higher level characteristics and the outcome of interest. Using example data sets, we explain step by step how to conduct linear or logistic multilevel modelling. Additionally, we provide syntax commands for several software packages and demonstrate how to interpret the results of multilevel analyses.