## Data Analysis Using Regression and Multilevel/Hierarchical ModelsData 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 - LibraryThingA 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

### 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 | |

Author index | 601 |

607 | |

### Other editions - View all

Data Analysis Using Regression and Multilevel/Hierarchical Models Andrew Gelman,Jennifer Hill Limited preview - 2007 |

Data Analysis Using Regression and Multilevel/Hierarchical Models Andrew Gelman,Jennifer Hill No preview available - 2007 |

### 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 county-level 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 group-level predictors height illustrate imputation indicators individual-level instrumental variables interactions interpret 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 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 varying-intercept vector vote y.hat[i zero σα

### Popular passages

Page 592 - On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data.