Andrew GelmanCambridge University Press, 6/14/2007EAN 9780521686891, ISBN10: 052168689XPaperback, 648 pages, 25.4 x 17.9 x 3.5 cmLanguage: EnglishData 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.1. Why?2. Concepts and methods from basic probability and statisticsPart I. A. Single-Level Regression3. Linear regressionthe basics4. Linear regressionbefore and after fitting the model5. Logistic regression6. Generalized linear modelsPart I. B. Working with Regression Inferences7. Simulation of probability models and statistical inferences8. Simulation for checking statistical procedures and model fits9. Causal inference using regression on the treatment variable10. Causal inference using more advanced modelsPart II. A. Multilevel Regression11. Multilevel structures12. Multilevel linear modelsthe basics13. Multilevel linear modelsvarying slopes, non-nested models and other complexities14. Multilevel logistic regression15. Multilevel generalized linear modelsPart II. B. Fitting Multilevel Models16. Multilevel modeling in bugs and Rthe basics17. Fitting multilevel linear and generalized linear models in bugs and R18. Likelihood and Bayesian inference and computation19. Debugging and speeding convergencePart III. From Data Collection to Model Understanding to Model Checking20. Sample size and power calculations21. Understanding and summarizing the fitted models22. Analysis of variance23. Causal inference using multilevel models24. Model checking and comparison25. Missing data imputationAppendixesA. Six quick tips to improve your regression modelingB. Statistical graphics for research and presentationC. SoftwareReferences.'Data Analysis Using Regression and Multilevel/Hierarchical Models' ... careful yet mathematically accessible style is generously illustrated with examples and graphical displays, making it ideal for either classroom use or self-study. It appears destined to adorn the shelves of a great many applied statisticians and social scientists for years to come.' Brad Carlin, University of Minnesota
