ABSTRACT
The multilevel regression model has become known in the research literature under a variety of names, such as ‘random coefficient model’ (de Leeuw & Kreft, 1986; Longford, 1993), ‘variance component model’ (Longford, 1987), and ‘hierarchical linear model’ (Raudenbush & Bryk, 1986, 1988). Statistically oriented publications tend to refer to the model as a mixed-effects or mixed model (Littell, Milliken, Stroup, & Wolfinger, 1996). The models described in these publications are not exactly the same, but they are highly similar, and I will refer to them collectively as ‘multilevel regression models’. They all assume that there is a hierarchical data set, with one single outcome or response variable that is measured at the lowest level, and explanatory variables at all existing levels. Conceptually, it is useful to view the multilevel regression model as a hierarchical system of regression equations. In this chapter, I will explain the multilevel regression model for two-level data, and also give an example of three-level data. Regression models with more than two levels are also used in later chapters.
