Data Analysis Using Regression and Multilevel/Hierarchical Models

Data Analysis Using Regression and Multilevel/Hierarchical Models

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 missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http://www.stat.columbia.edu/~gelman/arm/

List Price: $ 59.00

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The R Book

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 Meta-Analysis.
  • 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 High-level 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) 

List Price: $ 100.00

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6 comments

  1. 151 of 156 people found the following review helpful
    5.0 out of 5 stars
    Integrated Material, January 9, 2007
    By 
    Jeff Gill (Boston) –
    (REAL NAME)
      

    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.

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  2. 57 of 58 people found the following review helpful
    5.0 out of 5 stars
    Fantastic Blend of Theory and Practical Advice, February 3, 2007
    By 
    Theodore J. Iwashyna (Philadelphia, PA) –
    (REAL NAME)
      

    This review is from: Data Analysis Using Regression and Multilevel/Hierarchical Models (Paperback)
    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.

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  3. 39 of 40 people found the following review helpful
    5.0 out of 5 stars
    The best introduction to multilevel modeling out there, April 7, 2007
    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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  4. 50 of 51 people found the following review helpful
    5.0 out of 5 stars
    The best book on doing statistics with R, January 14, 2013
    By 
    Dimitri Shvorob
    (REAL NAME)
      

    This review is from: The R Book (Hardcover)
    … And I am referring to more advanced statistics than data summarization, and I am also distinguishing statistics from machine learning (or “statistical learning”, as Hastie and Tibshirani call it) methods. “The R Book” provides a reliable introduction to R, although I prefer Robert Kabacoff’s “R in Action”, and, secondly, think that “R for Everyone” by Jared Lander is a necessary intermediate read, as both “R Book” and “R in Action”, unfortunately, skip over some very useful utilities, including “ggplot2” graphics package.

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  5. 36 of 37 people found the following review helpful
    2.0 out of 5 stars
    Good content and coverage. Poorly written, August 13, 2013
    By 
    George

    This review is from: The R Book (Kindle Edition)
    I have bought 20+ books from Amazon and never written a comment and never returned for a refund … until now. I felt compelled to write something.

    It is not a bad book. It is a substantial, complete and reasonably in-depth 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 proof-read and its codes have been test-run. 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 not-so-elementary 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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  6. 17 of 19 people found the following review helpful
    4.0 out of 5 stars
    The Best One Book, but can’t be the only book, March 7, 2013
    By 

    Verified Purchase(What’s this?)
    This review is from: The R Book (Hardcover)
    The text is little different from the 1st edition, but the printing is far superior. On the whole, an adequate introduction to the stats done in R, typos excepted. Graphics still gets short shrift. Jump to Hadley’s ggplot2 book for that.

    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 half-dozen 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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