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Data Analysis Using Regression and Multilevel/Hierarchical Models - Softcover
Book 5 of 15: Analytical Methods for Social Research
Synopsis
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 and demonstrates a wide variety of models and instructs the reader in how to fit these models using freely available software packages.
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About the Authors
Andrew Gelman is Professor of Statistics and Professor of Political Science at Columbia University. He has published over 150 articles in statistical theory, methods, and computation, and in applications areas including decision analysis, survey sampling, political science, public health, and policy. His other books are Bayesian Data Analysis (1995, second edition 2003) and Teaching Statistics: A Bag of Tricks (2002).
Jennifer Hill is Assistant Professor of Public Affairs in the Department of International and Public Affairs at Columbia University. She has co-authored articles that have appeared in the Journal of the American Statistical Association, American Political Science Review, American Journal of Public Health, Developmental Psychology, the Economic Journal and the Journal of Policy Analysis and Management, among others.
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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Cambridge University Press, 2006
ISBN 10: 052168689X
ISBN 13: 9780521686891
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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ISBN 13: 9780521686891
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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ISBN 10: 052168689X
ISBN 13: 9780521686891
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Data Analysis Using Regression and Multilevel/Hierarchical Models
Published by
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ISBN 10: 052168689X
ISBN 13: 9780521686891
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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Cambridge University Press (edition 1), 2006
ISBN 10: 052168689X
ISBN 13: 9780521686891
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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ISBN 10: 052168689X
ISBN 13: 9780521686891
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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Cambridge University Press, 2006
ISBN 10: 052168689X
ISBN 13: 9780521686891
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Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
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Paperback. Condition: Very Good. 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. Seller Inventory # 00107139220
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Data Analysis Using Regression and Multilevel/Hierarchical Models
Published by
Cambridge University Press, 2006
ISBN 10: 052168689X
ISBN 13: 9780521686891
Used
Paperback
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: Good. 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. Seller Inventory # 00109549817