Popular in the First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as:
* 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 in Chapter 3
* New section on multivariate growth models in Chapter 6
* A discussion of research synthesis or meta-analysis applications in Chapter 7
* Data 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