• 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 ...

The Multilevel Model Framework
The multilevel model framework
JeffGillWashington University, USA
AndrewJ.WomackUniversity of Florida, USA
1.1 Overview

Multilevel models account for different levels of aggregation that may be present in data. Sometimes researchers are confronted with data that are collected at different levels such that attributes about individual cases are provided as well as the attributes of groupings of these individual cases. In addition, these groupings can also have higher groupings with associated data characteristics. This hierarchical structure is common in data across the sciences, ranging from the social, behavioral, health, and economic sciences to the biological, engineering, and physical sciences, yet is commonly ignored by researchers performing statistical analyses. Unfortunately, neglecting hierarchies in data can have damaging consequences to subsequent statistical inferences.

The frequency of nested data structures in ...

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