• 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 ...

Multilevel Model Notation—Establishing the Commonalities
Multilevel model notation—establishing the commonalities
MarcA.ScottNew York University, USA
PatrickE.ShroutNew York University, USA
SharonL.WeinbergNew York University, USA
2.1 Introduction

Multilevel models (MLMs) have broad applicability, having been usefully applied to problems in the biological sciences, social policy, and health sciences, among others. In most of the disciplines there are textbooks that begin with the common language of that discipline and then introduce the multilevel framework. Many of these texts have been particularly influential, and the number of new texts continues to grow, with a rough grouping into those that emphasize analysis of longitudinal data and those that emphasize nested social groups. Some representative texts are those by Bryk and Raudenbush (1992); Diggle, Heagerty, Liang, and Zeger (2002); Fitzmaurice, Ware, and Laird (2004); Gelman and Hill ...

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