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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: #19904 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
166 of 171 people found the following review helpful.
Integrated Material
By Jeff Gill
Gelman and Hill have put together a fabulously well-integrated look at general modeling with a focus on hierarchical structures. The book starts with simple modeling principles and continues well into material that would satisfy a third semester course in many social science grad programs. This book does something that is extremely hard: presenting serious technical ideas without overwhelming language and detail, making the chapters unusally easy to read and digest. They also do a very nice job of balancing Bayesian and traditional approaches without denigrating or over-promoting either. This should considerably broaden the appeal. Furthermore, the emphasis on R and WinBugs means that readers can immediately (and for free) run through the techniques.
I see this book as primarily a teaching tool, although many will use it as a reference. In this light, it is without peer right now in terms of coverage (basically all of the standard/basic regression models that get taught to social science grad students), price/page ratio (0.15366), and accessibility. Many of us have used econometric texts for such purposes over the years, living with a slightly mismatched set of criteria to rely on the quality of these works (Greene, Mittlehammer et al., etc.), but now there is a competitor that fits much more nicely with non-economic methods training (less of a fixation with asymptotics, no need for 200 named flavors of each model, and so on). Finally, the practical advice and admonitations that accompany the model descriptions will be immensely helpful to practitioners.
62 of 63 people found the following review helpful.
Fantastic Blend of Theory and Practical Advice
By Theodore J. Iwashyna
I came to this text with a very pragmatic need: I needed power calculations of a multi-level model, and I needed them fast. I skipped directly to Chapter 20, which is the most accessible treatment of multi-level power-calculations I have ever read. A few hours later, I had the calculations I needed done. (Take home point: this book has a wonderfully practical side.)
To my surprise, I also really understood what I had done, why I had done it, and other approaches that I might have taken. That is, the text very effectively provides the broader theoretical overview, gives a concise real-statistics treatment, and pragmatically teaches you how to actually do the analyses you need to do. Gelman & Hill have that rare ability to both teach the abstract and directly help you do the practical. (Fans of Paul Allison's books will love this one, too.) This is a must-have for the shelf, and I am sure I will come back to it repeatedly.
45 of 47 people found the following review helpful.
The best introduction to multilevel modeling out there
By Shaking&Aching
I have to qualify this review by saying that I proceeded from the 11th chapter since the first ten were more or less review. Also, I am not a statistician by any stretch of the imagination. My math background is pure math and economics degrees with some too-practical econometrics. In spite of that, I understood this book quite well. Hence my positive review. Compared to other comprehensive treatments of HLM, such as Singer and Willett or Hox, this book is in a universe all its own. I actually took Hox's course from him and still barely understood HLM, yet got the highest marks in the class. That's not a good thing. I felt like I wasted my time.
I actually learned a great deal from this book, and more than practical method (which I have since used), I actually understood what it was I was doing. The few R examples I did were worth it, and I would try them out if you can. In the past I have made two abortive runs at learning MLM/HLM, but this time it stuck. This book is extraordinarily well-written, as if it has been taught to non-statisticians a number of times. This is perhaps due to the presence of Hill as coauthor. Her public affairs students are not likely to value the math for its own sake. I alotted myself a month to master the latter chapters, some of which were completely new to me and it took me less than a week.
Drawbacks:
Typos: None of these were in substantive portions of the text such as equations and data print-outs. Still, a few in the wording were present. Mine is a first printing, however, so these might not be in your copies.
Program use: I think that they should also have offered SAS, SPSS, or Stata excercises. I only incidentally learned R, but would prefer to use a more standard software package for the excercises.
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