Luke D.A. Multilevel Modeling

SAGE Publications, Incorporated, 2020. — 129 p.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.Praise for the Second Edition
About the Author
Series Editor's Introduction
Preface
The Need for Multilevel Modeling
Background and Rationale
Theoretical Reasons for Multilevel Models
Statistical Reasons for Multilevel Models
Scope of This Book
Online Book Resources
Planning a Multilevel Model
The Basic Two-Level Multilevel Model
The Importance of Random Effects
Classifying Multilevel Models
Building a Multilevel Model
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 a Multilevel Model
Assessing Model Fit and Performance
Estimating Posterior Means
Centering
Power Analysis
Extending the Basic Model
The Flexibility of the Mixed-Effects Model
Generalized Models
Three-Level Models
Cross-Classified Models
Longitudinal Models
Longitudinal Data as Hierarchical: Time Nested Within Person
Intraindividual Change
Interindividual Change
Alternative Covariance Structures
Guidance
Recommendations for Presenting Results
Useful Resources
References
Index