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Data Analysis Using Regression and Multilevel/Hierarchical Models
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Binding: Paperback
Dewey Decimal Number: 519.536
EAN: 9780521686891
Edition: 1
ISBN: 052168689X
Label: Cambridge University Press
Manufacturer: Cambridge University Press
Number Of Items: 1
Number Of Pages: 648
Publication Date: December 18, 2006
Publisher: Cambridge University Press
Studio: Cambridge University Press
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Editorial Review:
Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http://www.stat.columbia.edu/~gelman/arm/
Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces and demonstrates a wide variety of models and instructs the reader in how to fit these models using freely available software packages.
Customer Reviews
Average Rating: 
Rating:  - very broad coverage of data analysis with hierarchical models
Andrew Gelman is a top researcher in Bayesian statistics as well as an excellent writer. He has written an excellent text on Bayesian data analysis that uses the Markov Chain Monte Carlo methods for dealing with hierarchical linear models. This book starts out on an introductory level covering a wide variety of statistical modeling problems including logistic regression and multilevel logistic regression, generalized linear models and causal inference. The MCMC methods are taught using BUGS and ... Read More
Rating: - Easy to read
This book is full of examples and very well written, contains everything one needs for deep insight into multi level analysis
Rating: - Readable and informative
A great book for addressing how to work with data on multiple levels. It is both accessible and useful!
Rating: - A great achievement!
Andrew Gelman has written an excellent book about regression models, with examples solved in the R language. He provides enlightning views of even complex subjects, such as mixed-effects models. A reader not familiar with R, should probably acquire some knowledge of R before he/she can fully benefit from the book, but this in itself is a worthwhile investment. (R is freely available; see [...]). Although it is an introductory book, the author manages to convey valuable new insights to more advanced ... Read More
Rating: - Standard Gelman
Like all of Gelman's stuff, damn fine work. Nowhere near as advanced as his Bayesian pubs - and, hopefully, the next book will address HLM Bayesian models in a rigorous manner - it's where the world is moving.
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