Data Analysis Using Regression and Multilevel Hierarchical Models 1st Edition by Andrew Gelman, Jennifer Hill ISBN 0511266839 9780511266836

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ISBN 10: 0511266839

ISBN 13: 9780511266836

Author: Andrew Gelman, Jennifer Hill

Data Analysis Using Regression and Multilevel/Hierarchical Models 1st Edition: 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.

Data Analysis Using Regression and Multilevel/Hierarchical Models 1st Edition Table of contents:

• Part 1A: Single-level regression

  • Chapter 3: Linear regression: the basics
  • Chapter 4: Linear regression: before and after fitting the model
  • Chapter 5: Logistic regression
  • Chapter 6: Generalized linear models

• Part 1B: Working with regression inferences

  • Chapter 7: Simulation of probability models and statistical inferences
  • Chapter 8: Simulation for checking statistical procedures and model fits
  • Chapter 9: Causal inference using regression on the treatment variable
  • Chapter 10: Causal inference using more advanced models

• Part 2A: Multilevel regression

  • Chapter 11: Multilevel structures
  • Chapter 12: Multilevel linear models: the basics
  • Chapter 13: Multilevel linear models: varying slopes, non-nested models, and other complexities
  • Chapter 14: Multilevel logistic regression
  • Chapter 15: Multilevel generalized linear models

• Part 2B: Fitting multilevel models

  • Chapter 16: Multilevel modeling in Bugs and R: the basics
  • Chapter 17: Fitting multilevel linear and generalized linear models in Bugs and R
  • Chapter 18: Likelihood and Bayesian inference and computation
  • Chapter 19: Debugging and speeding convergence

• Part 3: From data collection to model understanding to model checking

  • Chapter 20: Sample size and power calculations
  • Chapter 21: Understanding and summarizing the fitted models
  • Chapter 22: Analysis of variance
  • Chapter 23: Causal inference using multilevel models
  • Chapter 24: Model checking and comparison
  • Chapter 25: Missing-data imputation

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