This book is an accessible introduction to quantitative data analysis, concentrating on the key issues facing those new to research, such as how to decide which statistical procedure is suitable, and how to interpret the subsequent results. Each chapter includes illustrative examples and a set of exercises that allows readers to test their understanding of the topic. The book, written for graduate students in the social sciences, public health, and education, offers a practical approach to making sociological sense out of a body of quantitative data. The book also will be useful to more experienced researchers who need a readily accessible handbook on quantitative methods.

Quantitative Data Analysis, by Donald J. Treiman, is a well-written demonstration of how to answer questions using statistics. While the preface states that the book is “designed for a course to be taken after a first-year graduate statistics course in the social sciences”, the thought processes and techniques illustrated are useful and interesting to a much wider audience. The range of techniques is broad, ranging from simple advice for making tables readily readable through linear and logistic regression to log-linear and random-effects models.

Treiman writes using clear, precise language. This style makes the book accessible to readers from many fields and especially worthwhile to statistical consultants or others who work with clients of different backgrounds. The book is highly applied—the examples stem from published papers with real datasets. Many of the examples stem from Treiman’s long history of research in applied sociology, yet these examples are still interesting and approachable to those outside the field. The main material is nicely supplemented with “callouts” containing biographical and historical background information, as well as tips on Stata usage. Treiman also takes the time and effort to explain how to avoid common pitfalls of data analysis.

Because this is an applied book, there is little derivation of the mathematics behind the statistical techniques. This is not a drawback, though, because Treiman includes references and a large bibliography, which can be followed by those curious about statistical theory. Stata was used for the computation of all statistical results, and all the Stata do-files (Stata code) and datasets are available from the web. (In the book itself there is advice about how to use Stata for some analyses, but there is no explicit Stata code.)

Quantitative Data Analysis is worth a look for those wanting to see the applications of a wide variety of statistical techniques to a variety of problems or for those who are interested in thought process behind assessing the results of techniques.