This new edition of this classic incorporates the most recent thinking on methodology and software, as well as the latest literature on multilevel statistical models. Topics covered by multilevel models have increased in recent years, and the methods are widely applied in the social sciences as well as in areas such as epidemiology, geography, education, surveys, and medicine. This third edition includes chapters on meta analysis, factor analysis and structural equation models, and has expanded sections on MCMC methods.
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Goldstein integrates existing statistical methodologies within a consistent terminology and avoids undue statistical complexity. He explains a number of new developments, particularly in the areas of discrete response data, random cross classifications, time series and nonlinear models, measurement errors and missing data. Numerous examples and diagrams illustrate the applications of techniques and methodological derivations are included in the appendices.
It is now generally recognised in many areas of the social, medical and other sciences that statistical data typically have complex hierarchical or multilevel structures in which individuals are grouped together in communities or institutions. This grouping affects their behaviour and multilevel modelling is now the accepted statistical technique for the analysis of this type of data. An understanding of these methods is vital for researchers in fields such as education, epidemiology, geography, child growth and social surveys, among others. This new edition brings the book fully up to date, explaining important new developments such as the use of Markov Chain Monte Carlo methods, bootstrapping and mulitvariate models. The book has been completely restructured for this third edition and extra space has been given to discussion of key issues such as missing data, measurement errors and multivariate models. Real-life examples are used throughout to illustrate clearly the theoretical concepts.
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