Data Analysis Using Regression and
Multilevel/Hierarchical Models by
Jennifer Hill
An Excellent Presentation Of Hierarchical Models
Data 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/
Personal Review: Data Analysis Using Regression and
Multilevel/Hierarchical Models by Jennifer Hill
I am reading this book for two reasons: improving my understanding of
some statistical issues and becoming more proficient with modern
statistical techniques. The book has been helpful on both fronts, often
providing new (to me) points of view for looking at a problem and giving
very accessible entry points to more advanced techniques. I have enjoyed
very much reading the book and am looking forward to the opportunity to
test some of the techniques.
In my opinion, the authors have chosen a good set of examples and have
managed to keep me hooked on them. Initially I was a bit reluctant to buy a
'statistics for social sciences' type of book (I come from natural
resources/genetics), but the material can be easily transferred to other
settings.

The book requires some previous knowledge of statistics (it is no 'linear
models for dummies'), targeting readers that already have some
experience working with linear regression. Some previous experience with
hierarchical models would not hurt either.
Concerning the use of R as the statistical language for the book I think it is
a great choice. R is becoming the lingua franca of statistical computing, it
is free and the authors do a good job at introducing the language. Even if
you are using other language (SAS or SPSS for example) the book will still
provide good theoretical explanations and useful comments on data
analysis.
I have seen some comments on typos, I have seen some, but are not that
egregious as to distract from reading the book.
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