Data analysis using regression and multilevel/hierarchical models
Author(s)
Bibliographic Information
Data analysis using regression and multilevel/hierarchical models
(Analytical methods for social research)
Cambridge University Press, 2007
- : hbk
- : pbk
Available at 78 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
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  United Kingdom
  Germany
  Switzerland
  France
  Belgium
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  United States of America
Note
Includes bibliographical references (p. 575-600) and indexes
Description and Table of Contents
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. 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.
Table of Contents
- 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.
by "Nielsen BookData"