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

Raudenbush, Stephen W.

ISBN 10: 076191904X / ISBN 13: 9780761919049

Editore: SAGE Publications, Inc, 2001

Riguardo questo articolo

Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Codice articolo 000527128U

Dati bibliografici

• Titolo: Hierarchical Linear Models: Applications and Data Analysis Methods (Advanced Quantitative Techniques in the Social Sciences)
• Editore: SAGE Publications, Inc
• Data di pubblicazione: 2001
• Lingua: Inglese
• ISBN 10: 076191904X
• ISBN 13: 9780761919049
• Rilegatura: hardcover
• Edizione: 2nd.
• Condizione: Good

Descrizione articolo

Riassunto

"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.

Recensione

"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 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)