Data Analysis Using Regression and Multilevel/Hierarchical Models

Abstract

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.

Keywords

Multilevel modelComputer scienceRegression analysisMarginal modelLogistic regressionImputation (statistics)Missing dataRegressionCausal inferenceRegression diagnosticData miningHierarchical database modelMultivariate adaptive regression splinesInferenceCross-sectional regressionLinear regressionMachine learningStatisticsArtificial intelligencePolynomial regressionMathematics

Affiliated Institutions

Columbia University US

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Publication Info

Year: 2006
Type: book
Citations: 13674
Access: Closed

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APA Style

                            
                                    Andrew Gelman, 
                                
                                    Jennifer Hill
                                
                            (2006). 
                            Data Analysis Using Regression and Multilevel/Hierarchical Models. 
                            Cambridge University Press eBooks
                            
                            .
                            https://doi.org/10.1017/cbo9780511790942

Identifiers

DOI: 10.1017/cbo9780511790942