Introduction

Up to now, the explanation of the principles of multilevel analysis has been limited to simple analysis. In this chapter, the models to be analysed will be extended with some covariates. Let us go back to the result of one of the analyses performed in Chapter 2 (see Output 2.5). In this analysis a two-level structure was considered, in such a way that patients were clustered within medical doctors (see Figure 2.4), and the relationship between age and total cholesterol was investigated. The independent age variable was centred in order to facilitate the interpretation of the (variance of the random) intercept when a random slope for age was allowed. The example with age centred is chosen as a starting point because the magnitude of the variance of the random intercept is going to be used in the next part of this section. The conclusion of the analysis performed in Chapter 2 was that there was a highly significant positive relationship between age and total cholesterol. It was further shown that in the two-level data structure in which the patients were only clustered within the medical doctors, the ‘best’ way to analyse this relationship was a model with a random intercept as well as a random slope for age at the medical doctor level. Output 5.1 shows (again) the results of this analysis, which is used as starting point for the explanation of multilevel modelling.