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December, 2019 | SAGE Publications, Inc
Second Edition
Douglas A. Luke
- Washington University in St. Louis, USA
128 pages | December, 2019 | SAGE Publications, Inc
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eBook
ISBN: 9781544310299
Paperback
ISBN: 9781544310305
$40.00
Instant Access!
eBook
ISBN: 9781544310299
Multilevel Modeling is a concise, practical guide to building models for multilevel and longitudinal data. Author Douglas A. Luke begins by providing a rationale for multilevel models; outlines the basic approach to estimating and evaluating a two-level model; discusses the major extensions to mixed-effects models; and provides advice for where to go for instruction in more advanced techniques. Rich with examples, the Second Edition expands coverage of longitudinal methods, diagnostic procedures, models of counts (Poisson), power analysis, cross-classified models, and adds a new section added on presenting modeling results. A website for the book includes the data and the statistical code (both R and Stata) used for all of the presented analyses.

Series Editor's Introduction
About the Author
Preface

Background and Rationale
Theoretical Reasons for Multilevel Models
Statistical Reasons for Multilevel Models
Scope of Book
Online Book Resources

The Basic Two-Level Multilevel Model
The Importance of Random Effects
Classifying Multilevel Models

Introduction to Tobacco Voting Data Set
Assessing the Need for a Multilevel Model
Model-building Strategies
Estimation
Level-2 Predictors and Cross-Level Interactions
Hypothesis Testing

Assessing Model Fit and Performance
Estimating Posterior Means
Centering
Power Analysis

The Flexibility of the Mixed-Effects Model
Generalized Models
Three-level Models
Cross-classified Models

Longitudinal Data as Hierarchical: Time Nested Within Person
Intra-individual Change
Inter-individual Change
Alternative Covariance Structures

Recommendations for Presenting Results
Useful Resources
References

A website for the book includes the data and the statistical code (both R and Stata) used for all of the presented analyses.