## 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/ |

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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

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Useful.

### 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 Limited preview - 2006 |

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

### Common terms and phrases

actual allow analysis approach arsenic assigned assumptions average Bugs called causal Chapter classical coefficients compared comparison complete compute consider constant corresponding dataset defined described discuss display dnorm earnings effect election error estimate example expected experiment factors Figure function given graph group-level height illustrate imputation income indicators individual inferences inputs interactions intercept interest interpret interval linear logistic regression matrix mean measurements multilevel model normal observed outcome overdispersion parameters perform person plot pooling population positive possible predictive predictors prior distribution probability problem proportion radon random range reasonable regression model represent residual sample scale score sense separate shows simple simulation slope squares standard deviation standard error statistical switching term treated treatment treatment effect uncertainty understand units values variables variance variation varying vector vote zero

### Popular passages

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