Hierarchical Linear Models: Applications And Data Analysis Methods (Advanced Quantitative Techniques In The Social Sciences)  Stephen W. Raudenbush, Anthony S. Bryk, Bryk, Anthony S (2001) 

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"This is a firstclass 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." "The new chapters (1014) improve an already excellent resource for research and instruction. Their content expands the coverage of the book to include models for discrete level1 outcomes, nonnested level2 units, incomplete data, and measurement errorall 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." "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." 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 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 The authors conclude in Part IV with the statistical theory and computations used throughout the book, including univariate models with normal level1 errors, multivariate linear models, and hierarchical generalized linear models. 


Notes 
Bryk's name appears first on the previous edition. 