Popular in its first edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been updated to include: an intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication; a new section on multivariate growth models; a discussion of research synthesis or meta-analysis applications; aata analytic advice on centering of level-1 predictors, and new material on plausible value intervals and robust standard estimators.
PART I THE LOGIC OF HIERARCHICAL LINEAR MODELING Series Editor 's Introduction to Hierarchical Linear Models Series Editor 's Introduction to the Second Edition 1.Introduction 2.The Logic of Hierarchical Linear Models 3. Principles of Estimation and Hypothesis Testing for Hierarchical Linear Models 4. An Illustration PART II BASIC APPLICATIONS 5. Applications in Organizational Research 6. Applications in the Study of Individual Change 7. Applications in Meta-Analysis and Other Cases where Level-1 Variances are Known 8. Three-Level Models 9. Assessing the Adequacy of Hierarchical Models PART III ADVANCED APPLICATIONS 10. Hierarchical Generalized Linear Models 11. Hierarchical Models for Latent Variables 12. Models for Cross-Classified Random Effects 13. Bayesian Inference for Hierarchical Models PART IV ESTIMATION THEORY AND COMPUTATIONS 14. Estimation Theory Summary and Conclusions References Index About the Authors