Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman is known for his expertise on Bayesian statistics. Based on that knowledge he wrote a book in multilevel regression using R and WINbugs. This book aims to be a thorough description of (multilevel) regression techniques, implementation of these techniques in R and bugs, and a guide on interpreting the results of your analyses. Shortly put, the books excels on all three subjects.


Admittedly, this review has been written based on first impressions on the book. But, a sunny day in the park reading this book (literally) left me to believe that I have some understanding on what this book is trying to achieve. I bought this book in order to have an overview on fitting multilevel regression models using R. Starting to read the book, I soon found out that that is indeed what it has to offer me, but it offers me a lot more. After some introductory chapters, the book starts off with an introduction to both linear regression as well as introducing the reader to R software, by showing how to fit linear regression models in R. This is readily expanded to logistic regression and generalized regression models. All is illustrated lushly with many examples and illustrations.

Before these ‘basic’ regression models are extended to multilevel models, Bayesian statistics are introduced. Based on simulation techniques, causal inferences, based on regression models, are made. The multilevel section of the book is set up similarly. First, ‘basic’ multilevel regression models are introduced. Throughout the book, the lmer function is used. This function is not only able to fit simple multilevel models, but logistic and generalized models as well. It can even estimate non-nested models. All in all, this forms a thorough introduction to multilevel regression analysis in itself, but the book continues here as well to introduce the reader to Bayesian statistics.

All above-mentioned models, as well as more complicated models, are fitted using WINbugs as well. This very flexible method allows the reader to estimate a greater variety of (multilevel) models. Causal inference on multilevel models, using Bayesian statistics, is described as well. The third main part of the book elaborates on the skills the reader uses to ‘just’ fitting models. It learns the reader to really think about what it going on. Topics such as ‘understanding and summarizing the fitted models’, ‘sample size and power calculations’, and most of all ‘model checking and comparison’ each receive their own chapter of the book. In this we can see that the authors of this book aimed higher than just writing instructions on how to let R fit (multilevel) regression models. The aim of this book, is to teach the reader how to analyze data the proper way. Much attention is paid to assumptions, testing theory, and interpretation of what you’re doing. To quote the authors: “If you show something, be prepared to explain it”.

This philosophy seemed to be a guideline for the authors while writing this book, as well as flexibility. The book starts off with some examples of the authors’ own research. These examples return throughout the book, resulting in some degree of familiarity with the data by the reader. Due to this, the concepts, models and/or analyses described are certainly more easy to be understood. As a reader, you start to think along with the author, when a new problem is described. The relative worth of the techniques, as well as their drawbacks, are made perfectly clear. The use of R software, as well as WINbugs, pays of well in the sense that it requires some more effort to master these programs, but in that process the reader learns to think deeply about what he really want to do and how it is done properly.

I found it not an easy book, but thanks to the many examples throughout the book it can be fully understood by people with some prior knowledge in regression techniques. All of the examples in the book can be tried yourself, since the data and syntax are available on the author’s website on the book. This helps the reader to get some feel for the more difficult subjects of the book. All in all, this seems to me as a great book for every applied researcher that has basic prior understanding of regression analysis. Due to its focus on one set of techniques, a great depth of understanding can be derived from this book.