Data Analysis Using Regression and Multilevel/Hierarchical ModelsData 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/ |
Contents
Why? | 1 |
Concepts and methods from basic probability and statistics | 13 |
7 | 23 |
Singlelevel regression | 29 |
8 | 49 |
3 | 55 |
5 | 61 |
Logistic regression | 65 |
varying slopes nonnested models | 279 |
Multilevel logistic regression | 301 |
Multilevel generalized linear models | 325 |
Fitting multilevel models | 343 |
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 |
5 | 85 |
8 | 94 |
Generalized linear models | 110 |
Working with regression inferences | 135 |
3 | 142 |
5 | 151 |
5 | 157 |
1 | 167 |
4 | 174 |
Causal inference using more advanced models | 199 |
Multilevel regression | 235 |
the basics | 251 |
Understanding and summarizing the fitted models | 457 |
Analysis of variance | 487 |
Causal inference using multilevel models | 503 |
Model checking and comparison | 513 |
Missingdata imputation | 529 |
A Six quick tips to improve your regression modeling | 547 |
Software | 565 |
References | 575 |
Causal inference using regression on the treatment variable | 582 |
| 601 | |
| 607 | |
Other editions - View all
Data Analysis Using Regression and Multilevel/Hierarchical Models Andrew Gelman,Jennifer Hill No preview available - 2006 |
Data Analysis Using Regression and Multilevel/Hierarchical Models Andrew Gelman,Jennifer Hill No preview available - 2007 |
Common terms and phrases
analysis ANOVA arsenic average predictive comparison Bayesian Bayesian inference Bugs model causal inference Chapter classical regression code Bugs code code R code coef.est coef.se compared complete pooling compute constant term corresponding county-level covariates data points dataset defined deviance display dnorm dunif estimate example factor Figure finite-population fit the model fitted model function Gelman Gibbs sampler graph group-level predictors illustrate imputation indicators individual-level inputs instrumental variables interactions interpret interval likelihood linear models linear regression log radon level logistic regression matrix mean measurements multilevel model n.sims no-pooling non-nested noninformative normal distribution observed outcome overdispersed partial pooling plot Poisson Poisson regression population posterior precincts prior distribution propensity score random regression coefficients regression line regression model replicated residual rnorm sample scale sigma.y simple simulation standard deviation standard error test scores topcoding treatment effect uncertainty values variance parameters variation varying intercepts vector vote weight y.hat zero