000			01704nam a2200253Ia 4500
999			_c339 _d339
003			DE-boiza
005			20191029111310.0
008			190909
020			_a978-0-521-68689-1
040			_cIZA
100			_aGelman, Andrew _91115
100			_a Hill, Jennifer _91116
245		0	_aData Analysis Using Regression and Multilevel/Hierarchical Models
250			_a11th printing
260			_c2007 _bCambridge University Press
300			_a625 pages
340			_hC3 22
520			_aData 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.
650			_adata analysis _91118
650			_aeconomic model _91119
650			_aregression analysis _9905
856			_uhttps://www.cambridge.org/core/books/data-analysis-using-regression-and-multilevelhierarchical-models/32A29531C7FD730C3A68951A17C9D983#fndtn-information _yPublisher's website
942			_cBO _2ddc