Introduction

In the foregoing chapters, multilevel analysis was explained with examples from studies with continuous outcome variables (i.e. linear multilevel analysis). One of the biggest advantages of multilevel analysis is that it can be used for the analysis of other kinds of outcome variables as well. Logistic multilevel analysis can be used for dichotomous outcome variables, multinomial logistic multilevel analysis can be used for categorical outcome variables, and Poisson multilevel analysis can be used for so-called ‘count’ outcome variables. Furthermore, it is possible to perform a multilevel survival analysis, although the necessary software has not yet been fully developed for this type of analysis.

Logistic multilevel analysis

The general principles behind logistic multilevel analysis (i.e. multilevel analysis with a dichotomous outcome variable) are the same as those described in Chapter 2 for linear multilevel analysis. So, in general, multilevel analysis with a dichotomous outcome variable is a logistic regression analysis in which an additional correction can be made for categorical variables, such as medical doctor or school. It should be realised that the estimation of the random variances, in particular, is mathematically quite difficult, and that different software packages use different estimation procedures. Unfortunately, however, these different procedures often lead to different results (see Chapter 9 for further information about the use of the various software packages).