• Summary
  • Contents
  • Subject index

In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling.

The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field.

Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference; Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models; Part III includes discussion of missing data and robust methods, assessment of fit and ...

Lack of Fit, Graphics, and Multilevel Model Diagnostics
Lack of fit, graphics, and multilevel model diagnostics
GerdaClaeskensKatholieke Universiteit Leuven, Belgium
24.1 Model Assumptions

Multilevel models rely on several assumptions which we can group into the following three types:

  • Assumptions about the distribution of the error terms and about the distribution of the random effects. For linear mixed models it is quite common to assume normality for the errors and the random effects; see Chapter 13.
  • Assumptions stating that all of the observations obey the specified model and hence that there are no aberrant or outlying observations. In cases where this assumption does not hold, and robustness of the estimators is deemed important, robust estimation methods may be used; see Chapter 22.
  • Assumptions on the form of the mean ...
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