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
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Hardcover. Etat : Fair. Spuren von Feuchtigkeit / Nasse. This first-class resource addresses crucial areas in applied statistics, featuring widely applicable methods and high-quality exposition. The new chapters (10-14) enhance its value for research and instruction by covering models for discrete level-1 outcomes, non-nested level-2 units, incomplete data, and measurement error--essential topics in contemporary social statistics. Written clearly and supported by engaging examples, this expanded edition is particularly beneficial for advanced graduate students and social researchers.Chapter 11 showcases the versatility of mixed models with the EM algorithm, offering new insights and practical solutions to common research challenges. The book is reorganized into four parts, with significant expansions and clarifications. Part I discusses the logic of hierarchical linear modeling, while Part II focuses on basic applications, including an intuitive summary of estimation and inference procedures, multivariate growth models, and research synthesis applications.Part III introduces diverse outcome types, with Chapter 10 exploring hierarchical models for binary outcomes, counted data, ordered categories, and multinomial outcomes. Chapter 11 delves into latent variable models and missing data, while Chapter 13 presents Bayesian inference logic applied to hierarchical data. Part IV concludes with statistical theory and computations, covering univariate models, multivariate linear models, a. N° de réf. du vendeur 0659d090-958c-419d-906e-85ba61f8d954
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