Description
| Product ID: | 9780521686891 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | US |
| Series: | Analytical Methods for Social Research |
| Title: | Data Analysis Using Regression and Multilevel/Hierarchical Models |
| Authors: | Author: Andrew Gelman, Jennifer Hill |
| Page Count: | 648 |
| Subjects: | Social research and statistics, Social research & statistics, Calculus and mathematical analysis, Calculus & mathematical analysis |
| Description: | 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. It introduces and demonstrates a variety of models and instructs the reader in how to fit these models using freely available software packages. 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. |
| Imprint Name: | Cambridge University Press |
| Publisher Name: | Cambridge University Press |
| Country of Publication: | GB |
| Publishing Date: | 2006-12-18 |
