• 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 ...

Mixture and Latent Class Models in Longitudinal and Other Settings
Mixture and latent class models in longitudinal and other settings
RyanP.Browne and PaulD.McNicholasDepartment of Mathematics and Statistics University of Guelph, Canada
20.1 Introduction

In this chapter, we see how mixture models can be an important tool for multilevel models. We place some focus on longitudinal data while also trying to give a fairly broad coverage of the literature. The structure of this chapter is as follows. We begin by looking at how mixture models and latent class analysis fit into the multilevel modeling framework. Then we examine and discuss the relationship between mixture models and latent class models. In Section 20.2.2, latent variable models are reviewed and linked with finite mixture model-based clustering and classification. In Section 20.2.3, ...

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