Product Description

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.

Product Details

• Amazon Sales Rank: #127290 in Books
• Published on: 2006-12-18
• Original language: English
• Number of items: 1
• Binding: Paperback
• 648 pages

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 modelling. I recommend it very warmly.' Journal of Applied Statistics

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.

Customer Reviews

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 Raudenbush for example. The issues I have with this book is that it is over long. It could easily have been made shorter. The writing is often terse and there are clearer ways to put the point across.
The real weakness with the book is the website and support materials. The website is very poorly organized and although the authors provide some R examples and code their instructions for getting up and running with these packages to work through the examples is far from clear. Contacting the authors is similarly a waste of time.
If you want to understand how to run regression models with R the best book is John Fox's The R and S-Plus Companion to Applied Regression. Whose website and customer facing skills are also markedly superior. For getting into multilevel models fox's appendix on his website is well worth reading. To understand Longitudinal models Bryk and Raudenbush or Singer book is better written.
I like the idea of using R and Winbugs but the authors just haven't packaged the practicalities well.

Another classic from Gelman
I was initially drawn to this book because Andrew Gelman's previous book on Bayesian data analysis is one of my all-time favourite stats books. This one is pitched at a lower technical level (undergraduate rather than postgraduate) for a wider audience and it succeeds marvellously. The first thing I noticed when it arrived was that it was twice as thick as I expected it to be. Great! Much of the material is already familiar to me but I learnt things nevertheless, such as the problem of regressing on an intermediate outcome. Very comprehensive and well written.

An amazing stats book
This book has to be one of the best, if not the best, book upon statistics that I have ever read. Throughout I found the descriptions of the models and the mathematical explanations clear and easy to follow. The book also builds up nicely, first discussing classical regression before going onto describing multi-level models and ultimately to fitting them using Winbugs. Also, the book explains how to interpret the co-efficients one gets from models. I really enjoyed this as some stats books do not really explain these numbers to the reader properly in my opinion.

Furthermore, as all the analyses are carried out in R, with scripts provided in the book, even seemingly complex models can easily be fit by R users. The ability to call winbugs using R is also a huge bonus and will aid researchers seeking a more Bayesian approach to statistics. For those who do not use R details of how to fit the models in other statistical packages are given in the appendix. My only criticism of the book would be this: Some of the R scripts available on the website are a bit messy and it can be difficult to find specific bits of code.

Overall I felt compelled to give this book five stars because it has taught me more about statistics then any other book on the subject.