Data Analysis Using Regression and Multilevel/Hierarchical Models
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/ more
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Data Analysis Using Regression and...
Probability and Statistics - General Mathematics - For the applied researcher performing data analysis using linear... |
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Data Analysis Using Regression and...
Pages: 648, Edition: 1, Paperback, Cambridge University Press |
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ProductReviews94/100 (13 Reviews)
Recent Reviews
- 5/5
- From: Amazon
- Posted: Jan-01-2009
- I could only dream of a book this great!
This book is now my favorite statistics text. Every question I have ever had concerning anything from basic stats, to classic regressions to HLM has been answered by this book. It includes codes to use R which enables anyone to learn...
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- 5/5
- From: Amazon
- Posted: Nov-02-2008
- An excellent presentation of hierarchical models
I am reading this book for two reasons: improving my understanding of some statistical issues and becoming more proficient with modern statistical techniques. The book has been helpful on both fronts, often providing new (to me) points...
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- 5/5
- From: Amazon
- Posted: Jun-12-2008
- 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...
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- 5/5
- From: Amazon
- Posted: May-26-2008
- Easy to read
This book is full of examples and very well written, contains everything one needs for deep insight into multi level analysis
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Selected Reviews
- 5/5
- From: Amazon
- Posted: Jan-01-2009
- I could only dream of a book this great!
This book is now my favorite statistics text. Every question I have ever had concerning anything from basic stats, to classic regressions to HLM has been answered by this book. It includes codes to use R which enables anyone to learn...
- read full review | report as inappropriate
- 3/5
- From: Amazon
- Posted: Feb-02-2007
- nice but...
It's a very useful book written in a fresh style and with many interesting examples. I have to complain for the so many typographic errors in the first printing.
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- 2/5
- From: Amazon
- Posted: Aug-26-2007
- Useful but plenty of flaws
I read this book looking for an accessible and comprehensive treatment of multilevel models. The topic of social science appealed because this area offers different examples yet has used multilevel techniques widely. See Bryk and...
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