Hierarchical Linear Models: Applications and Data Analysis Methods

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...

• Format: Hardcover
• Language: English
• ISBN: 0803946279
• ISBN13: 9780803946279
• Release Date: March 1992
• Publisher: Sage Publications, Inc
• Length: 265 Pages
• Weight: 1.35 lbs.
• Dimensions: 6.0" x 9.0"

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.