• 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 ...

Complexities in Error Structures within Individuals
Complexities in error structures within individuals
VicenteNúñez-AntónThe University of the Basque Country, UPV/EHU, Spain
DaleL.ZimmermanThe University of Iowa, USA
10.1 Introduction

Many types of data have a hierarchical or clustered structure. For example, trees growing in the same area or descended from the same genetic line tend to be more alike than trees chosen at random from the population at large. Individuals or subjects in a study may be further nested within geographical areas. Multilevel data structures also arise in longitudinal studies where the same individual's responses over time are correlated with each other (see also Chapter 9). This is the more natural setting where within-individual correlation or covariance structures that can model specific data behaviors arise. Some of the characteristics that need ...

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