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Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman

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Description for Data Analysis Using Regression and Multilevel/Hierarchical Models paperback. This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models. Series Editor(s): Alvarez, R. Michael; Beck, Nathaniel L.; Wu, Lawrence L. Series: Analytical Methods for Social Research. Num Pages: 648 pages, 160 exercises. BIC Classification: JHBC; PBK. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 254 x 179 x 37. Weight in Grams: 1120. Series: Analytical Methods for Social Research. 648 pages, 160 exercises. For the applied researcher performing data analysis using linear and nonlinear regression and multilevel models. Cateogry: (P) Professional & Vocational; (U) Tertiary Education (US: College). BIC Classification: JHBC; PBK. Dimension: 254 x 179 x 37. Weight: 1132. Series Editor(s) :Alvarez, R. Michael; Beck, Nathaniel L.; Wu, Lawrence L.
Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, 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 ... Read more

Product Details

Publisher

Cambridge University Press

Number of pages

648

Format

Paperback

Publication date

2006

Series

Analytical Methods for Social Research

Condition

New

Weight

1147g

Number of Pages

648

Place of Publication

Cambridge, United Kingdom

ISBN

9780521686891

SKU

V9780521686891

Shipping Time

Usually ships in 7 to 11 working days

Ref

99-8

About Andrew Gelman

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 ... Read more

Reviews for Data Analysis Using Regression and Multilevel/Hierarchical Models

'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 ... Read morewritten 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 'Data Analysis Using Regression and Multilevel/Hierarchical Models is the book I wish I had in graduate school. … The text is an obvious candidate for use in courses or course modules on multilevel modeling, especially in Part 2. Beyond that, where should it be used? Instructors of first-year graduate methods courses should consider complementing their texts with material from Part 1. Many use Kennedy's A Guide to Econometrics (2003) to provide an alternative take in the essentials. Data Analysis is better suited for taking on this role. Students will find its coverage less redundant of what they get from standard texts, and the use of non-economics based examples should also help sell quantitative research to skeptical incomers into the profession.' The Political Methodologist Show Less

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Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman

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