Hierarchical Linear Models: Applications and Data Analysis Methods Second Ed

Description: GREAT CONDITION NO MARKINGS NO WRITING SHIPS USPS MEDIA MAIL * 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 * 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.

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Location: Huntington Park, California

End Time: 2025-01-07T07:19:47.000Z

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