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Data Analysis Using Regression and Multilevel/Hierarchical Models

Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman
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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 instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.
Cambridge University Press; December 2006
651 pages; ISBN 9780511266836
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Title: Data Analysis Using Regression and Multilevel/Hierarchical Models
Author: Andrew Gelman; Jennifer Hill
 
ISBNs
0511266839
9780511266836
9780521867061
9781139234962