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Hardcover Hierarchical Linear Models: Applications and Data Analysis Methods Book
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ISBN: B00A2PGUGG

ISBN13: 9780761919049

Hierarchical Linear Models: Applications and Data Analysis Methods

(Book #1 in the Advanced Quantitative Techniques in the Social Sciences Series)

by and

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

Recommended

Format: Hardcover

Condition: New

$142.00
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Related Subjects

Medical Medical Books Reference Social Science Social Sciences

Customer Reviews

4 ratings
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THE Book - dense but important

Published by Thriftbooks.com User , 14 years ago
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

Published by Thriftbooks.com User , 16 years ago
This is a must-have book for anyone who is serious about understanding multilevel/hierarchical linear modeling.

pre-req: mid-level stats experience

Published by Thriftbooks.com User , 18 years ago
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

Published by Thriftbooks.com User , 20 years ago
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.
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