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/

Data Analysis Using Regression and Multilevel/Hierarchical Models (Analytical Methods for Social Research)

Science & Math

Hill, Jennifer

Gelman, Professor in the Department of Statistics Andrew

Mathematics

Cambridge University Press

See the best price to sell, buy, or rent books by Gelman, Professor in the Department of Statistics Andrew. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Gelman, Professor in the Department of Statistics Andrew

See the best price to sell, buy, or rent books by Gelman, Professor in the Department of Statistics Andrew. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Data Analysis Using Regression and Multilevel/Hierarchical Models (Analytical Methods for Social Research)