Data Analysis Using Regression and Multilevel/Hierarchical Models (Google eBook)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 missingdata imputation. Practical tips regarding building, fitting, and understanding are provided throughout. 
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LibraryThing Review
User Review  Harlan879  LibraryThingA good comprehensive survey of the topics. But, different sections assume different levels of background knowledge, from nearly nothing to gradlevel statistics theory. I like their views on the ... Read full review
Review: Data Analysis Using Regression and Multilevel/Hierarchical Models
User Review  Jeff Carnegie  GoodreadsSo far the overview of regression has been great. Read full review
Contents
1  
13  
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  
Author index  601 
607  
Common terms and phrases
aﬀect analysis arsenic level Bayesian Bayesian inference Bugs code Bugs model causal eﬀect causal inference Chapter classical regression coef.est coef.se Intercept compared complete pooling compute constant term corresponding countylevel covariates data points dataset deﬁned deviance diﬀerent diﬃcult discuss display dnorm dunif earnings estimate example Figure ﬁrst ﬁt ﬁtted model ﬁtting ﬁxed function Gelman Gibbs sampler graph grouplevel predictors height illustrate imputation indicators individuallevel instrumental variables interactions interpret likelihood linear models linear regression lmer logistic regression matrix mean measurements model ﬁt multilevel model n.sims nopooling normal distribution observed outcome output overdispersion plot Poisson regression population posterior precincts prior distribution probability propensity score random regression coeﬃcients regression line regression model replicated rnorm sample scale Section sigma.y simple simulation slope speciﬁc standard deviation standard error statistically signiﬁcant switching tau.y test scores treatment eﬀect uncertainty values variation varyingintercept vector vote y.hat[i zero σα
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Page 592  On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data.