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/
I got this book while working on an article that involved a hierarchical model with a binary dependent variable - after poking through Radenbush/Bryk and a variety of other texts that left me frustrated. Not only did this book teach me how to properly specify and estimate the model in R, I also learned a lot about interpretation and graphical means of presenting results. I don't think I've read another book that so effectively combines theoretical and practical information, while also being a relatively smooth read - the examples are clear and interesting! In addition to the extensive treatment of hierarchical models, Gelman and Hill also cover non-hierarchical OLS and ML models, plus a variety of other key stats topics. My only quibble is that the accompanying R code on Gelman's website isn't complete - but the fact that they have sample code available at all puts this far beyond most stats books. I wish I had had this book in grad school and look forward to referring to it for years to come.
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 this stats program easily. My advice; BUY THIS BOOK!
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 of view for looking at a problem and giving very accessible entry points to more advanced techniques. I have enjoyed very much reading the book and am looking forward to the opportunity to test some of the techniques.
In my opinion, the authors have chosen a good set of examples and have managed to keep me hooked on them. Initially I was a bit reluctant to buy a 'statistics for social sciences' type of book (I come from natural resources/genetics), but the material can be easily transferred to other settings.
The book requires some previous knowledge of statistics (it is no 'linear models for dummies'), targeting readers that already have some experience working with linear regression. Some previous experience with hierarchical models would not hurt either.
Concerning the use of R as the statistical language for the book I think it is a great choice. R is becoming the lingua franca of statistical computing, it is free and the authors do a good job at introducing the language. Even if you are using other language (SAS or SPSS for example) the book will still provide good theoretical explanations and useful comments on data analysis.
I have seen some comments on typos, I have seen some, but are not that egregious as to distract from reading the book.
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.
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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