• 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 ...

Software for Fitting Multilevel Models
Software for fitting multilevel models
AndrzejT.Gałecki and BradyT.WestUniversity of Michigan, USA
26.1 Introduction

Today's applied statistician or research analyst has the luxury of working with a variety of powerful statistical software procedures that enable users to efficiently fit many of the multilevel models covered in this volume. Developments in software for multilevel models, including linear (LMMs), generalized (GLMMs), and nonlinear (NLMMs) mixed-effects models, are propelled both by advances in statistical methodology and technological progress in meeting computational demands.

With this chapter, we aim to provide readers with some history and an overview of the current software procedures that are available for fitting these models, and the abilities of the software to accommodate the many analytic aspects that accompany this broad class of models.

This chapter ...

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