CiNii 図書 - Multilevel modeling

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 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

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Multilevel modeling

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