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"This is a first-class book dealing with one of the most important areas of current research in applied statistics…the methods described are widely applicable…the standard of exposition is extremely high."
--Short Book Reviews from the International Statistical Institute

"The new chapters (10-14) improve an already excellent resource for research and instruction. Their content expands the coverage of the book to include models for discrete level-1 outcomes, non-nested level-2 units, incomplete data, and measurement error---all vital topics in contemporary social statistics. In the tradition of the first edition, they are clearly written and make good use of interesting substantive examples to illustrate the methods. Advanced graduate students and social researchers will find the expanded edition immediately useful and pertinent to their research."
--TED GERBER, Sociology, University of Arizona

"Chapter 11 was also exciting reading and shows the versatility of the mixed model with the EM algorithm. There was a new revelation on practically every page. I found the exposition to be extremely clear. It was like being led from one treasure room to another, and all of the gems are inherently useful. These are problems that researchers face everyday, and this chapter gives us an excellent alternative to how we have traditionally handled these problems."
--PAUL SWANK, Houston School of Nursing, University of Texas, Houston

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

While the first edition confined its attention to continuously distributed outcomes at level 1, this second edition now includes coverage of an array of outcomes types in Part III:

* New Chapter 10 considers applications of hierarchical models in the case of binary outcomes, counted data, ordered categories, and multinomial outcomes using detailed examples to illustrate each case
* New Chapter 11 on latent variable models, including estimating regressions from missing data, estimating regressions when predictors are measured with error, and embedding item response models within the framework of the HLM model
* New introduction to the logic of Bayesian inference with applications to hierarchical data (Chapter 13)

The authors conclude in Part IV with the statistical theory and computations used throughout the book, including univariate models with normal level-1 errors, multivariate linear models, and hierarchical generalized linear models.

 
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

"The text is authoritative, well laid out, and extremely readable. For the target audience, this book is highly recommended."

Short Book Reviews- Publication of the International Statistical Institute

"This book is very well written and the applied part is well balanced with technical details. I think that it will be useful not only for social and behavioral researchers but also for applied statisticians, practitioners and students analyzing data with hierarchical-type structures"

Zentralblat

"The book is clearly written, well organized, and addresses an important topic. I would recommend this book to the readers of Personnel Psychology. If you want to learn more about these techniques, the new advances, the controversial points, potential links between HLM and meta-analysis, structural equations modeling, item response theory, and so forth , this book is a feast."

Robert G. Jones
Southwest Missouri State University
Personnel Psychology Book Review Section

"This book makes good use of examples to introduce readers to HLM and the issues surrounding their application. In fact, I think the book does a wonderful job by using lots of examples with lots of details. This is definitely one of its strengths as it makes it much easier for the reader to follow the text and understand the capabilities of the HLM approach. This Second Edition should come highly recommended. I think it gives a very good and thorough overview of HLM, and it does so in a manner that is easy to follow."

Organizational Research Methods

Excellent and self-exploratory book

Mr Fatih Koca
COLLEGE OF EDUCATION, TEXAS TECH
July 3, 2013

Great text. This really is the rosetta stone of multilvel modeling.

Dr Henry May
School Of Education, University of Delaware
September 12, 2012

Required for phd students who use the ecological approach and plan to analyze data with nested structure(s)

Dr Marie-Claude Jipguep
Sociology Anthropology Dept, Howard University
December 29, 2011

Great text for teaching and understanding multilevel modeling. It's technical, thus having a good grasp on statistics will be helpful for readers.

Professor Joy Gayles
Adult Community Coll Ed Dept, North Carolina State University
July 8, 2010
Key features

While the first edition confined its attention to continuously distributed outcomes at level 1, this second edition now includes coverage of an array of outcome types in Part III:

New Chapter 10 considers applications of hierarchical models in the case of binary outcomes, counted data, ordered categories, and multinomial outcomes using detailed examples to illustrate each case.
 
New Chapter 11 on latent variable models, including estimating regressions from missing data, estimating regressions when predictors are measured with error, and embedding item response models within the framework of the HLM model.

New introduction to the logic of Bayesian inference with applications to hierarchical data (Chapter 13)

Other Features:

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


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Hardcover
ISBN: 9780761919049
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