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Data Analysis Using Regression and Multilevel/Hierarchical Models, by Andrew Gelman, Jennifer Hill
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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/
- Sales Rank: #47713 in Books
- Brand: Brand: Cambridge University Press
- Published on: 2006-12-18
- Original language: English
- Number of items: 1
- Dimensions: 9.96" h x 1.38" w x 6.97" l, 2.45 pounds
- Binding: Paperback
- 648 pages
- Used Book in Good Condition
Review
"Data Analysis Using Regression and Multilevel/Hierarchical Models ... careful yet mathematically accessible style is generously illustrated with examples and graphical displays, making it ideal for either classroom use or self-study. It appears destined to adorn the shelves of a great many applied statisticians and social scientists for years to come."
Brad Carlin, University of Minnesota
"Gelman and Hill have written what may be the first truly modern book on modeling. Containing practical as well as methodological insights into both Bayesian and traditional approaches, Data Analysis Using Regression and Multilevel/Hierarchical Models provides useful guidance into the process of building and evaluating models. For the social scientist and other applied statisticians interested in linear and logistic regression, causal inference, and hierarchical models, it should prove invaluable either as a classroom text or as an addition to the research bookshelf."
Richard De Veaux, Williams College
"The theme of Gelman and Hill's engaging and nontechnical introduction to statistical modeling is 'Be flexible.' Using a broad array of examples written in R and WinBugs, the authors illustrate the many ways in which readers can build more flexibility into their predictive and causal models. This hands-on textbook is sure to become a popular choice in applied regression courses."
Donald Green, Yale University
"Simply put, Data Analysis Using Regression and Multilevel/Hierarchical Models is the best place to learn how to do serious empirical research. Gelman and Hill have written a much needed book that is sophisticated about research design without being technical. Data Analysis Using Regression and Multilevel/Hierarchical Models is destined to be a classic!"
Alex Tabarrok, George Mason University
"a detailed, carefully written exposition of the modelling challenge, using numerous convincing examples, and always paying careful attention to the practical aspects of modeling. I recommend it very warmly."
Journal of Applied Statistics
"Gelman and Hill's book is an excellent intermediate text that would be very useful for researchers interested in multilevel modeling... This book gives a wealth of information for anyone interested in multilevel modeling and seems destined to be a classic."
Brandon K. Vaughn, Journal of Eductional Measurement
"With their new book, Data Analysis Using Regression and Multilevel/Hierarchical Models, Drs. Gelman and Hill have raised the bar for what a book on applied statistical modeling should seek to accomplish. The book is extraordinarily broad in scope, modern in its approach and philosophy, and ambitious in its goals... I am tremendously impressed with this book and highly recommend it. Data Analysis Using Regression and Multilevel/Hierarchical Models deserves to be widely read by applied statisticians and practicing researchers, especially in the social sciences. Instructors considering textbooks for courses on the practice of statistical modeling should move this book to the top of their list."
Daniel B. Hall, Journal of the American Statistical Association
"Data Analysis Using Regression and Multilevel/Hierarchical Models is the book I wish I had in graduate school."
Timothy Hellwig, The Political Methodologist
About the Author
Andrew Gelman is Professor of Statistics and Professor of Political Science at Columbia University. He has published over 150 articles in statistical theory, methods, and computation, and in applications areas including decision analysis, survey sampling, political science, public health, and policy. His other books are Bayesian Data Analysis (1995, second edition 2003) and Teaching Statistics: A Bag of Tricks (2002).
Jennifer Hill is Assistant Professor of Public Affairs in the Department of International and Public Affairs at Columbia University. She has co-authored articles that have appeared in the Journal of the American Statistical Association, American Political Science Review, American Journal of Public Health, Developmental Psychology, the Economic Journal and the Journal of Policy Analysis and Management, among others.
Most helpful customer reviews
12 of 12 people found the following review helpful.
If I were shipwrecked ...
By Dr Joseph A. Bulbulia
If I were shipwrecked and had only one statistics book with me,* this would be the one.
Why?
1. For most applied uses of multi-level (mixed effects) regression in the social sciences, this book is appropriately comprehensive. You will want for little.
2. The book is R oriented. Though R might not be sufficient for all your needs, it is necessary. R has become the coordination point.
3. The book deals with basic concepts in probability, simulation, inference, and causation. The focus is on understanding what you are doing, not simply applying standard recipes. That's important because you can't competently apply the tools you will learn from this book without understanding these basic concepts. There are no shortcuts.
4. Nevertheless, the book contains a bunch of recipes, which I found helpful, for example, when learning how to simulate in R. Also, the authors write using a compact coding style. I'm grateful to have learned some simplifying tricks.
5. The authors focus on graphical tests and visualizing data. That's how you ought to be exploring data and testing/interpreting your results.
6. The book is oriented to generalized linear mixed effects (multi-level) modelling. When you learned ordinary regression you learned a special case. If you haven't learned ordinary regression, start with GLMMs.
7. The book is oriented to Bayesian statistics. Whether you use Bayesian statistics is up to you, however you owe it to yourself not to make embarrassing objections. Gelman and Hill do a fine job of explaining the motivations.
Downsides
1. The individual sentences of this book are clear, however I felt that some sections could have had fuller explanations. Perhaps I'm a slow learner, but I had to move even slower than usual in some places, not because there was a thicket of mathematical detail (there's refreshingly little extraneous maths) but because the explanations were brief. For example, the sections on simulating data took me a couple of reads.
2. Software development is moving fast, and this book is already a little stale. That said, it is far from outdated. All the tools still work, and *most* are the same you'd be using now. That said some very good new statistical and graphical packages are available now (such as MCMCglmm, ggplot2, Rstan, blme, and others) and many will want to be running and interpreting their models using these. Note Gelman is involved in developing the latter two, and a bunch of others. No matter. This book is all most applied researchers will ever need, and again, you need to know the conceptual underpinnings. The tools will always be changing.
*.. and with me: my compute, power, the motivation to work, abundant coffee, a fine cafe to work in... & etc.
7 of 7 people found the following review helpful.
I like it so much I have two copies.
By James Savage
I bought this book for the first time in 2011, before taking an extended break from work. While on break, I read it from cover to cover, twice, and worked through all the examples. I'm almost certain I scored my current job, which I love, because of the skills I learned from this book.
Since the first reading, I've been proselytizing this book to all my colleagues and our interns. Consequently, most now own it and use it frequently. Because I've loaned it out so much, I bought a second copy.
Why do I like it? Simply, I am a quantitatively capable person without much patience. I can't sit down for hours with Greene and feel like I've learned much. I need examples, good writing, arguments, and lots of practice data/code. This book has those things.
Who is this book not for? I'd not recommend it to people who get a lot out of mathematical statistics, engineers, and the like. If you enjoy formalism, you'll get frustrated at the authors' desire for practicality.
6 of 6 people found the following review helpful.
Nice addition to the library
By D. Lamb
Overall, I really like this book. I think there are certainly flaws, but the vast majority is quite useful. I'm not a statistician, but have taken a couple of classes in statistics, through multivariate linear models and some logistic regression. This book was definitely at my level, and didn't require a background in mathematical statistics. I found the parts where the authors went through building linear and logistic models and interpreting the results to be very informative, even after taking a few classes.
The book covers a lot of material, and so nothing is delved into very deeply. There is more than one section you will need to look elsewhere in order to supplement the one or two sentences dedicated to a topic.
You should be experienced with using R in order to get much out of the examples. For example, they use the arm library a lot, but never show in the code samples to load the library. If you've never used R (I have) then you might be lost. The most frustrating thing I'm finding is the data that accompanies the book. It is not documented and not formed to match the examples in the book. For example, the height and earnings data uses 1 and 2 classifications for sex, but I couldn't find it documented which one was for male and female. I little trial and error and I eventually figured it out. Little things like that don't make it easy to work with. The data comes in numerous formats too (Stata, DAT, asc) instead of a generic csv file.
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