Data Analysis Using Regression and Multilevel/Hierarchical Models

ISBN-13: 9780521867061 / Angielski / Twarda / 2006 / 648 str.

Andrew Gelman

Opis

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/

Recenzje

'Data Analysis Using Regression and Multilevel/Hierarchical Models is the book I wish I had in graduate school. … The text is an obvious candidate for use in courses or course modules on multilevel modeling, especially in Part 2. Beyond that, where should it be used? Instructors of first-year graduate methods courses should consider complementing their texts with material from Part 1. Many use Kennedy's A Guide to Econometrics (2003) to provide an alternative take in the essentials. Data Analysis is better suited for taking on this role. Students will find its coverage less redundant of what they get from standard texts, and the use of non-economics based examples should also help sell quantitative research to skeptical incomers into the profession.' The Political Methodologist

Spis treści

1. Why?; 2. Concepts and methods from basic probability and statistics; Part I. A. Single-Level Regression: 3. Linear regression: the basics; 4. Linear regression: before and after fitting the model; 5. Logistic regression; 6. Generalized linear models; Part I. B. Working with Regression Inferences: 7. Simulation of probability models and statistical inferences; 8. Simulation for checking statistical procedures and model fits; 9. Causal inference using regression on the treatment variable; 10. Causal inference using more advanced models; Part II. A. Multilevel Regression: 11. Multilevel structures; 12. Multilevel linear models: the basics; 13. Multilevel linear models: varying slopes, non-nested models and other complexities; 14. Multilevel logistic regression; 15. Multilevel generalized linear models; Part II. B. Fitting Multilevel Models: 16. Multilevel modeling in bugs and R: the basics; 17. Fitting multilevel linear and generalized linear models in bugs and R; 18. Likelihood and Bayesian inference and computation; 19. Debugging and speeding convergence; Part III. From Data Collection to Model Understanding to Model Checking: 20. Sample size and power calculations; 21. Understanding and summarizing the fitted models; 22. Analysis of variance; 23. Causal inference using multilevel models; 24. Model checking and comparison; 25. Missing data imputation; Appendixes: A. Six quick tips to improve your regression modeling; B. Statistical graphics for research and presentation; C. Software; References.