• 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 ...

Centering Predictors and Contextual Effects
Centering predictors and contextual effects
CraigK.EndersArizona State University, USA
6.1 Introduction

Multilevel regression models enjoy widespread use in many disciplines. These models are attractive because they provide researchers with a mechanism for analyzing data sets where individuals are nested within higher-order organizational units (i.e., clusters). Examples of multilevel data structures are abundant in the literature and include situations where students are nested within classrooms or schools, clients are nested within therapists, employees are nested within workgroups or companies, and individuals are nested within family units, to name a few. Unlike single-level regression models, multilevel models allow researchers to examine explanatory variables at all levels of the data hierarchy.

Specifying a multilevel model involves a number of nuances that are not germane to single-level regression ...

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