Hierarchical Linear Models Applications and Data Analysis Methods

Name: Hierarchical Linear Models Applications and Data Analysis Methods
Price: 29.76 USD
Availability: InStock
ISBN: 9780761919049

ISBN-10: 076191904X

ISBN-13: 9780761919049

Edition: 2nd 2002 (Revised)

Authors: Stephen W. Raudenbush, Anthony S. Bryk

List price: $129.00

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"This is a first-class book dealing with one of the most important areas of current research in applied statisticsthe methods described are widely applicablethe 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.

Book details

List price: $129.00
Edition: 2nd
Copyright year: 2002
Publisher: SAGE Publications, Incorporated
Publication date: 12/19/2001
Binding: Hardcover
Pages: 512
Size: 6.25" wide x 9.25" long x 1.25" tall
Weight: 1.958
Language: English

He is a Professor of Education at University of Michigan.

He is a Professor of Education at the University of Chicago and Director of its Center for School Improvement.




The Logic of Hierarchical Linear Modeling


Series Editor 's Introduction to Hierarchical Linear Models


Series Editor 's Introduction to the Second Edition



Introduction



The Logic of Hierarchical Linear Models



Principles of Estimation and Hypothesis Testing for Hierarchical Linear Models



An Illustration



Basic Applications



Applications in Organizational Research



Applications in the Study of Individual Change



Applications in Meta-Analysis and Other Cases where Level-1 Variances are Known



Three-Level Models



Assessing the Adequacy of Hierarchical Models



Advanced Applications



Hierarchical Generalized Linear Models



Hierarchical Models for Latent Variables



Models for Cross-Classified Random Effects



Bayesian Inference for Hierarchical Models



Estimation Theory and Computations



Estimation Theory


Summary and Conclusions


References


Index


About the Authors