Description
Book SynopsisMultilevel 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.
Trade ReviewWith growing statistical software package costs, more researchers are using R than ever before. This book allows researchers to do more when using R.
-- Gina R. Gullo * Review *
The book offers insights and explanations from which both newcomers and seasoned experts can find benefit.
-- Timothy Ford * Review *
Because of the author’s pedagogically masterful presentation of multi-level modeling, the otherwise challenging journey to this topic now becomes not only smooth but also enjoyable.
-- Lin Ding * Reviewer *
This is a very well-written and organized book. The author uses practical examples to help the readers understand the reasoning and steps of a complex statistical approach. I have used the first edition of this book in my class, and definitely plan on using the second edition too. This is a book that I would highly recommend to clinical researchers who are interested in learning multilevel modeling.
-- Dorina Kallogjeri * Review *
Multilevel Modeling provides a thorough and accessible introduction to multilevel models. Through extensive examples, the author expertly guides the reader through the material addressing interpretation, graphical presentation, and diagnostics along the way.
-- Jennifer Hayes Clark * review *
The new second edition is even better than the first. The models presented are closely linked to an extended example that students can readily identify with.
-- Richard R. Sudweeks * Review *
Table of ContentsSeries Editor′s Introduction About the Author Preface 1. The Need for Multilevel Modeling Background and Rationale Theoretical Reasons for Multilevel Models Statistical Reasons for Multilevel Models Scope of Book Online Book Resources 2. Planning a Multilevel Model The Basic Two-Level Multilevel Model The Importance of Random Effects Classifying Multilevel Models 3. 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 4. Assessing a Multilevel Model Assessing Model Fit and Performance Estimating Posterior Means Centering Power Analysis 5. Extending the Basic Model The Flexibility of the Mixed-Effects Model Generalized Models Three-level Models Cross-classified Models 6. Longitudinal Models Longitudinal Data as Hierarchical: Time Nested Within Person Intra-individual Change Inter-individual Change Alternative Covariance Structures 7. Guidance Recommendations for Presenting Results Useful Resources References
