Data Analysis Using Regression and Multilevel/Hierarchical Models

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/

Science & Math

Andrew Gelman

Jennifer Hill

Mathematics

Cambridge University Press

On the night of the 2000 presidential election, Americans sat riveted in front of their televisions as polling results divided the nation's map into red and blue states. Since then the color divide has become a symbol of a culture war that thrives on stereotypes--pickup-driving red-state Republicans who vote based on God, guns, and gays; and elitist, latte-sipping blue-state Democrats who are woefully out of touch with heartland values. Red State, Blue State, Rich State, Poor State debunks these and other political myths.
With wit and prodigious number crunching, Andrew Gelman gets to the bottom of why Democrats win elections in wealthy states while Republicans get the votes of richer voters, how the two parties have become ideologically polarized, and other issues. Gelman uses eye-opening, easy-to-read graphics to unravel the mystifying patterns of recent voting, and in doing so paints a vivid portrait of the regional differences that drive American politics. He demonstrates in the plainest possible terms how the real culture war is being waged among affluent Democrats and Republicans, not between the haves and have-nots; how religion matters for higher-income voters; how the rich-poor divide is greater in red not blue states--and much more.
Red State, Blue State, Rich State, Poor State is a must-read for anyone seeking to make sense of today’s fractured American political landscape.
Myths and facts about the red and the blue:
Myth: The rich vote based on economics, the poor vote "God, guns, and gays." Fact: Church attendance predicts Republican voting much more among rich than poor.
Myth: A political divide exists between working-class "red America" and rich "blue America." Fact: Within any state, more rich people vote Republican. The real divide is between higher-income voters in red and blue states.
Myth: Rich people vote for the Democrats. Fact: George W. Bush won more than 60 percent of high-income voters.
Myth: Religion is particularly divisive in American politics. Fact: Religious and secular voters differ no more in America than in France, Germany, Sweden, and many other European countries.

Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do

Politics & Social Sciences

David Park

Boris Shor

Joseph Bafumi

Jeronimo Cortina

Social Sciences

Princeton University Press

Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science Book 106)

John B. Carlin

Hal S. Stern

Donald B. Rubin

David B. Dunson

Aki Vehtari

Chapman and Hall/CRC

Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include: <ul>
<li>Stronger focus on MCMC</li> <li>Revision of the computational advice in Part III</li> <li>New chapters on nonlinear models and decision analysis</li> <li>Several additional applied examples from the authors' recent research</li> <li>Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more</li> <li>Reorganization of chapters 6 and 7 on model checking and data collection</li>
</ul>
Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)

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Books by Andrew Gelman