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 |
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 |
| 575 | |
| 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 level assumptions Bayesian Bayesian inference Bugs code Bugs model causal effect causal inference Chapter classical regression code R code coef.est coef.se Intercept compared complete pooling compute constant term corresponding county-level data points dataset defined deviance display dnorm dunif election estimate example Figure fit the model fitted model function Gelman Gibbs sampler graph group-level predictors height illustrate imputation indicators individual-level instrumental variables interactions interpret interval likelihood linear models linear regression lmer logistic regression matrix mean measurements model fit multilevel model n.sims no-pooling normal distribution observed outcome output overdispersion plot Poisson regression population posterior pre-test precincts prior distribution probability propensity score radon level random regression coefficients regression line regression model replicated rnorm sample scale sigma.y simple simulation slope standard deviation standard error statistically significant switching test scores topcoding treatment effect uncertainty values variation varying-intercept vector vote zero μα σα
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