Description: Data Analysis Using Regression and Multilevel/Hierarchical Models: Analytical Methods for Social Research Andrew Gelman Jennifer Hill Published by Cambridge 2007 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/ LanguageEnglish Paperback648 pages ISBN-10052168689X ISBN-13978-0521-86706-1 Item Weight2.4 pounds Dimensions6.97 x 1.38 x 9.96 inches
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Subject Area: Social Research
Publication Name: Data Analysis Using Regression and Multilevel/Hierarchical Models
Item Length: 10.3in
Publisher: Cambridge University Press
Subject: Mathematics
Publication Year: 2007
Series: Analytical Methods for Social Research Ser.
Type: Textbook
Format: Hardcover
Language: English
Item Height: 1.6in
Author: Jennifer Hill, Andrew Gelman
Item Width: 7.3in
Item Weight: 47.3 Oz
Number of Pages: 648 Pages