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.
ISBN: 9781544310299
eBook
Suggested Retail Price: $18.00
Bookstore Price: $14.40
ISBN: 9781544310299
eBook
Suggested Retail Price: $20.00
Bookstore Price: $16.00
ISBN: 9781544310299
eBook
Suggested Retail Price: $23.00
Bookstore Price: $18.40
ISBN: 9781544310299
eBook
Suggested Retail Price: $33.00
Bookstore Price: $26.40
ISBN: 9781544310305
Paperback
Suggested Retail Price: $40.00
Bookstore Price: $32.00
See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email sageheoa@sagepub.com. Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html.
For assistance with your order: Please email us at textsales@sagepub.com or connect with your SAGE representative.
SAGE
2455 Teller Road
Thousand Oaks, CA 91320
www.sagepub.com
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.