Hierarchical Linear Models: Applications and Data Analysis Methods

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Popular in its first edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been updated to include: 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; a new section on multivariate growth models; a discussion of research synthesis or meta-analysis applications; aata analytic advice on centering of level-1 predictors, and new material on plausible value intervals and robust standard estimators.

Author(s): Stephen W. Raudenbush, Anthony S. Bryk
Series: Advanced Quantitative Techniques in the Social Sciences 1
Edition: 2
Publisher: SAGE Publications
Year: 2002

Language: English
Pages: 510
Tags: Математика;Теория вероятностей и математическая статистика;Математическая статистика;Прикладная математическая статистика;