Data Analysis Using Regression and Multilevel/Hierarchical Models

All too frequently, statistics books are dense and difficult to understand. Gelman and Hill are wonderfully clear and helpful writers. This book makes hierarchical modeling and regression analysis very clear and they structure the book to facilitate the reader working through their examples and thinking about the decisions they make. This book is a pure pleasure to read and the diversity of the examples (from the geology of radon concentrations to patterns of voting) gives the reader a good introduction to the breadth of problems the ideas and techniques presented here can be applied to. The book covers both theoretical considerations and also practical matters of how to effectively use software tools (R and BUGS, although the material is easily adapted to JAGS and the new STAN tool).

I'm a social sciences PhD student and this is the book I keep going back to. There are a huge number of texts that you will find useful, but this one stands out for being particularly useful from cover to cover. A few of the advantages: - theoretically rigorous, but done by example and counter-example vs. mathematical proofs - tremendous number of examples with code and interpretation - didactic approach yet organized for quick reference - oriented toward practice vs. theory Some other things I like that others might not: Gelman is not a big fan of NHST inference and so he does not emphasize it. Nor does he stress jargony interpretation of tables of regression coefficients. Rather he emphasizes interpretation by simulation and counterfactuals. In that way he lays the groundwork for Bayesian analysis. Gelman is one of the developers of the R package lmer which estimates multilevel models. As such, it is the best reference for doing multilevel models in R. But realize that it is so much more. They spend the first half of the book reviewing single-level (?) regression and so the transition to multilevel is intuitive. You will understand it as an extension of what you already know. And (I keep saying this) you will find yourself going back to reference their coverage of regression when you have a question. The book is brilliant.

Pros: They tackle a complex topic from many different angles. They present enough code and theory to get people up and running with the techniques, assuming some prior familiarity with likelihood based inference and R. Otherwise you might need to dig through some of the references to understand everything. Regardless, this book is a valuable reference to keep in your library. They use matrix notation sparingly and this helps the reader focus on the important concepts of multilevel modeling. I am not even remotely a statistician so my attention would have been lost if I had to sort through a bunch of matrix transpositions and inversions in addition to all of the multilevel notation. The authors provide many useful references that help reinforce difficult ideas/concepts and that elaborate on topics that are not explored in depth. I had no prior experience using WinBUGS and the authors provided enough information for me to successfully execute some models that integrate R and WinBUGS. That is no small feat and the authors should be commended because somehow I understood what was going on. Cons: The organization of the book seems scattered and could be a little more consistent. On pp 245-246, the authors go on a diatribe about "fixed" and "random" effects terminology, claim that much of the literature that applies these terms does so inconsistently, disown these terms by saying they will avoid using them entirely, and then continue using these terms throughout the book. The website needs some work. You need to already know how to use R to open different types of files (and maybe some basics of variable assignment)in order to reproduce all of their examples. This book will not hold your hand through the steps like many R books.

This an excelent book for getting the concepts behind fitting "standard" and multilevel models without diving directly into equations. Perfect for biologists unfamiliar with math like me.

Excellent book, covers all of the bases for mixed effect models, Bayesian modelling in Bugs and even general statistical concepts and pointers. Intermediate-advanced but in addition to being very thorough it's well written and not excessively technical.

bought the book to refresh my regression analysis skills and was positively surprised. It offers more than a normal textbook would offer. I can only recommend it

This book walks you through regression models one step at a time, starting from the very basics of classical regression, thus making it easy to follow. It presents a lot of examples that are accessible to public from any scholarly discipline, and offers tips and ready-to-use code for the statistical package R. The book focuses on methodological caveats to bear in mind in research design and result interpretation. Ideal for anybody who wants to study and model relationships between variables, whether causal or not.

Thorough and accessibly written.