Hierarchical Linear Models: Applications and Data Analysis Methods (Advanced Quantitative Techniques in the Social Sciences)

By Stephen W. Raudenbush and Anthony S. Bryk

Book Overview

"This monograph offers a careful introduction to models designed to deal with interactions between individual and contextual effects . . . . This book is an important contribution to the analysis of hierarchical data. It presents the material in sufficient depth without ignoring the demands of nonspecialists."

--American Journal of Sociology

"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, Publication of the International Statistical Institute

"No other introductory text on hierarchical or multilevel models attempts to take the reader through a carefully structured set of examples, and so this book is certainly welcome . . . . I would recommend it to those who would like an introduction to the topic and a glimpse of some of the potential power of multilevel models.

--Journal of the American Statistical Association

Much social and behavioral research involves hierarchical data structures. The effects of school characteristics on students; how differences among countries in governmental policies influence demographic relations within them; and how individuals exposed to different environmental conditions develop over time are but a few examples. However, past analysis of such data has been fraught with problems. Recent developments in the statistical theory of hierarchical linear models now afford an integrated set of methods for such applications.

Now a best-seller, Hierarchical Linear Models launched the Sage series, Advanced Quantitative Techniques in the Social Sciences. This introductory text explicates the theory and use of hierarchical linear models (HLM) through rich, illustrative examples and lucid explanations. The presentation remains reasonably nontechnical by focusing on three general research purposes--improved estimation of effects within an individual unit, estimating and testing hypotheses about cross-level effects, and partitioning of variance and covariance components among levels. This innovative volume describes use of both two-and three-level models in organizational research and studies of individual development and meta-analysis applications and concludes with a formal derivation of the statistical methods used in the book.

• Format: Hardcover
• Language: English
• ISBN: 0803946279
• ISBN13: 9780803946279
• Release Date: March 1992
• Publisher: SAGE Publications, Inc
• Length: 265 Pages
• Weight: 1.35 lbs.
• Dimensions: 9.3" x 0.8" x 6.5"

Customer Reviews

THE Book - dense but important

Basically if you buy this book, you don't need anything else on HLM. It's comprehensive, as the technique stands. But you can't learn HLM from this book - you'll need a teacher.

The classic text on Hierarchical Linear Modeling

This is a must-have book for anyone who is serious about understanding multilevel/hierarchical linear modeling.

pre-req: mid-level stats experience

I had taken a class in HLM before, and I bought this book to refresh myself on the details. It takes a good deal of attention to detail and concentration to really get the full measure from this book, although it's all in there. Despite the authors' best efforts, there is a good bit of stats jargon in the book, so a reader who is not familiar might have some difficulty. If you're at a point where learning HLM is a logical next step, you'll be fine and I recommend this book. However, if your over-eager faculty advisor told you to learn HLM, despite your minimal experience in stats, you're better off enrolling in a class or workshop.

Useful, but need solid background in stats

This book describes important advances in statistical analysis of social science data, circa 1992. Much of this data has a natural hierarchical grouping. But traditional statistical methods proved inadequate at coping. The biggest drawback was the failure of the assumption of independence. If at the lowest level, Items I1,...,In are grouped into sets J1,...,Jm, where mTo handle this, Hierarchical Linear Models were developed. The book gives a detailed treatment. A very comprehensive discussion. Including the handling of meta-analysis, where we wish to combine results across different studies. Which then involves using empirical Bayesian estimates. This method has also seen important usage in evaluating medical studies, conducted by different researchers on the same topic. The book also illustrates the essential development of non-trivial computer programs to perform the gruntwork. You will need a solid background in statistics to find this book useful. At a minimum, a year of statistics at the undergraduate level.