Data Analysis Using Regression And Multilevel / Hierarchical Models (Paperback)

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/

General

Imprint:	Cambridge University Press
Country of origin:	United Kingdom
Series:	Analytical Methods For Social Research
Release date:	December 2006
First published:	2007
Authors:	Andrew Gelman • Jennifer Hill
Dimensions:	254 x 179 x 34mm (L x W x T)
Format:	Paperback
Pages:	623
ISBN-13:	978-0-521-68689-1
Categories:	Books > Social sciences > Sociology, social studies > Social research & statistics > General Books > Science & Mathematics > Mathematics > Calculus & mathematical analysis > Vector & tensor analysis
LSN:	0-521-68689-X
Barcode:	9780521686891

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