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

by Anthony S. Bryk (Author), Dr. Stephen W. Raudenbush (Author) "Much social research involves hierarchical data structures..." (more)
Key Phrases: Summary of Terms Introduced, Variance Random Effect Component, Head Start (more...)

Editorial Reviews

Review

"No other introductory text on hierarchial 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." 

-- Review

Review

"No other introductory text on hierarchial 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 )

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Product Details

• Hardcover: 265 pages
• Publisher: Sage Publications, Inc (March 3, 1992)
• Language: English
• ISBN-10: 0803946279
• ISBN-13: 978-0803946279
• Product Dimensions: 9.2 x 6.5 x 0.8 inches
• Shipping Weight: 1.4 pounds
• Average Customer Review: 3.6 out of 5 stars See all reviews (5 customer reviews)
• Amazon.com Sales Rank: #676,351 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

9 of 9 people found the following review helpful:

3.0 out of 5 stars Good but sometimes skipping ahead too fast, March 9, 2006

By	A. R. H. Fischer "Astro" (Utrecht, Netherlands) - See all my reviews (REAL NAME)

This review is from: Hierarchical Linear Models: Applications and Data Analysis Methods (Advanced Quantitative Techniques in the Social Sciences) (Hardcover)

This book gives a detailed description of the use of an advanced method to deal with nested data sets.
At a general level the constructs and ideas are well written and can be followed reasonably easily.
However the mathematics is often written very dense, which makes reading and understanding complex.
My main problem with the book, is that in many of the examples they provide, the given formula's, and data skip rapidly to the solution. Thus it is often not insightfull at all, how the data led to the numerical outcome (and I and several of my colleagues could not reproduce all of the example outcomes). A more extensive discussion and a more step-by-step construction of the examples would have been helpful there.

So in short: Conceptually this book is fine, but for practical use mathematics are too dense, and examples are too hard to follow

16 of 19 people found the following review helpful:

4.0 out of 5 stars Useful, but need solid background in stats, June 5, 2004

By	W Boudville (Terra, Sol 3) - See all my reviews (TOP 50 REVIEWER)    (REAL NAME)

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 m<n, then all the items in Jk have that value k. They are not independent in this dimension.

To 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.

6 of 6 people found the following review helpful:

4.0 out of 5 stars pre-req: mid-level stats experience, July 11, 2006

By	Russell A. Carleton - See all my reviews (REAL NAME)

This review is from: Hierarchical Linear Models: Applications and Data Analysis Methods (Advanced Quantitative Techniques in the Social Sciences) (Hardcover)

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.