Data Analysis Using Regression and Multilevel/Hierarchical Models (Google eBook)

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Cambridge University Press, Dec 18, 2006 - Mathematics
5 Reviews
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.
  

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LibraryThing Review

User Review  - Harlan879 - LibraryThing

A good comprehensive survey of the topics. But, different sections assume different levels of background knowledge, from nearly nothing to grad-level statistics theory. I like their views on the ... Read full review

Review: Data Analysis Using Regression and Multilevel/Hierarchical Models

User Review  - Jeff Carnegie - Goodreads

So far the overview of regression has been great. Read full review

Contents

Why?
1
Concepts and methods from basic probability and statistics
13
Singlelevel regression
29
before and after fitting the model
53
Logistic regression
79
Generalized linear models
109
Working with regression inferences
135
Simulation for checking statistical procedures and model fits
155
Fitting multilevel linear and generalized linear models in Bugs
375
Likelihood and Bayesian inference and computation
387
Debugging and speeding convergence
415
From data collection to model understanding to model
435
Understanding and summarizing the fitted models
457
Analysis of variance
487
Causal inference using multilevel models
503
Model checking and comparison
513

Causal inference using regression on the treatment variable
167
Causal inference using more advanced models
199
Multilevel regression
235
the basics
251
varying slopes nonnested models
279
Multilevel logistic regression
301
Multilevel generalized linear models
325
Fitting multilevel models
343
Missingdata imputation
529
A Six quick tips to improve your regression modeling
547
Software
565
References
575
Author index
601
Subject index
607
Copyright

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About the author (2006)

Andrew Gelman is Professor of Statistics and Professor of Political Science at Columbia University. He has published over 150 articles in statistical theory, methods, and computation, and in applications areas including decision analysis, survey sampling, political science, public health, and policy. His other books are Bayesian Data Analysis (1995, second edition 2003) and Teaching Statistics: A Bag of Tricks (2002).

Jennifer Hill is Assistant Professor of Public Affairs in the Department of International and Public Affairs at Columbia University. She has co-authored articles that have appeared in the Journal of the American Statistical Association, American Political Science Review, American Journal of Public Health, Developmental Psychology, the Economic Journal and the Journal of Policy Analysis and Management, among others.

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