Data Analysis Using Regression and Multilevel/Hierarchical Models Author: Andrew Gelman | Language: English | ISBN:
B009019R3G | Format: EPUB
Data Analysis Using Regression and Multilevel/Hierarchical Models 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.
- File Size: 5488 KB
- Print Length: 648 pages
- Simultaneous Device Usage: Up to 4 simultaneous devices, per publisher limits
- Publisher: Cambridge University Press; 1 edition (December 25, 2006)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B009019R3G
- Text-to-Speech: Enabled
X-Ray:
- Lending: Not Enabled
- Amazon Best Sellers Rank: #149,649 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
- #95
in Kindle Store > Kindle eBooks > Nonfiction > Science > Mathematics > Applied > Probability & Statistics - #99
in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Professional Science > Mathematics > Applied > Statistics
- #95
in Kindle Store > Kindle eBooks > Nonfiction > Science > Mathematics > Applied > Probability & Statistics - #99
in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Professional Science > Mathematics > Applied > Statistics
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.
By Jeff Gill
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.
By Theodore J. Iwashyna
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