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Data collected in the social sciences often have a multilevel or clustered structure. From this we often have research questions that are of a multilevel nature, and multilevel modeling is now widely used across health, economics, demography, education, and many other areas to analyze data clustered within units at higher levels. The editors of this essential four-volume set are among the leading figures of multilevel modeling, an approach which is at once cutting-edge and well established within research methods and the social sciences.

Volume One: Linear Multilevel Models: Model formulation

Volume Two: Linear Multilevel Models: Inference, diagnostics and design

Volume Three: Generalized Linear Mixed Models

Volume Four: Complex Models and Issues

Editors’ Introduction: Multilevel Modelling
AndersSkrondal and SophiaRabe-Hesketh

Multilevel modelling has become increasingly popular since the early 1980s and now pervades quantitative research in virtually all disciplines, including the social sciences. This is reflected in the growing number of books dedicated to multilevel modelling: We found 6 books published between 1971 and 1992, and in the next three 5-year intervals 6 (1994–1998), 14 (1999–2003), and 25 books (2004–2008).

Multilevel models are used when data have a hierarchical structure with units nested in clusters. A common application is individuals nested in institutions or organizations, for example students in schools, employees in firms, or patients in hospitals. Another common type of application is panel surveys or other kinds of longitudinal data where panel waves or occasions are nested in individuals. ...

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