Standard Gelman | Data Analysis Using Regression and Multilevel/Hierarchical Models

Readable and informative | Data Analysis Using Regression and Multilevel/Hierarchical Models | Andrew Gelman, Jennifer Hill

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	• Data Analysis Using Regression and Multilevel/Hierarchical Models Andrew Gelman, Jennifer Hill Cambridge University Press, 2006 - 648 pages average customer review:based on 11 reviews view larger image
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highly recommended

Easy to read

This book is full of examples and very well written, contains everything one needs for deep insight into multi level analysis

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.

Readable and informative

A great book for addressing how to work with data on multiple levels. It is both accessible and useful!

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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 readers. This is a book that after you read it once you will pick up time and again to enjoy the presentation of the topics and to benefit your own work. Highly recommended, in particular to those getting started with R (or Splus for that matter).

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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 R. This book is not exclusively Bayesian as both likelihood and Bayesian procedures are presented. The topics are general but the emphasis is on social science applications. It is very comprehensive and has received enthusiastic reviews from well known statisticians including Dick Deveaux, Brad Carlin and Jeff Gill. Jeff's review is here on amazon. Jeff is a colleague of mine and he has written one of the finest introductory texts on Bayesian methods including the hierarchical models. His text is now out in its second edition. Jeff also wrote his book with the social scientists in mind.

Jeff's review has been the most looked at and voted the most helpful on this site. As this topic is a specialty area for him more than it is for me, I recommend that if you are interested in the material in this book that his review is very much worth reading.

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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/

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reviews: page 1, 2, 3

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