Data Analysis Using Regression and Multilevel/Hierarchical Models
 Used Book in Good Condition
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 missingdata imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http://www.stat.columbia.edu/~gelman/arm/
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The R Book
Hugely successful and popular text presenting an extensive and comprehensive guide for all R users
The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research.
This edition:
 Features full colour text and extensive graphics throughout.
 Introduces a clear structure with numbered section headings to help readers locate information more efficiently.
 Looks at the evolution of R over the past five years.
 Features a new chapter on Bayesian Analysis and MetaAnalysis.
 Presents a fully revised and updated bibliography and reference section.
 Is supported by an accompanying website allowing examples from the text to be run by the user.
Praise for the first edition:
‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008)
‘The Highlevel software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)
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Integrated Material,
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 noneconomic 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.
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Fantastic Blend of Theory and Practical Advice,
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 realstatistics 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 musthave for the shelf, and I am sure I will come back to it repeatedly.
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The best introduction to multilevel modeling out there,
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 wellwritten, as if it has been taught to nonstatisticians 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 printouts. 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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The best book on doing statistics with R,
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Good content and coverage. Poorly written,
It is not a bad book. It is a substantial, complete and reasonably indepth book with broad coverage. There are only two points I want to make:
(1) Lots of typos. Well, not only typos. As another customer wrote, this book looks like it has never been proofread and its codes have been testrun. Sometimes, it is written in a way serves to remind but not to explain.
(2) Yes, it covers all essential topics to get you started, but does not present them in a logical order. For example, in the example given in explaining Boolean vector multiplying a scalar (a very elementary topic), guess what “scalar” the author made up? Instead of just a simple number, he used “runif( )” which is a uniformly distribution random number, only to find out that the function “runif( )” has not been introduced until several chapters later. In the very chapter that introduces string data type, he came up with examples involving the notsoelementary functions “lapply( )” and “sapply( )” without explaining what they are.
Simply stated, it cannot be the first book in learning R. It simply doesn’t work out.
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The Best One Book, but can’t be the only book,
If you have the 1st and don’t care about the physical presentation, then don’t bother. If you’re looking for an overview of stats and R (either from the ground up, or as a refresher), this does the best job of the halfdozen or so I’ve read. It won’t get you the guns to argue with a Ph.D math stat, but no intro book will.
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