OpenIntro Statistics

Reviewed by Greg McAvoy, Professor, University of North Carolina at Greensboro on 12/5/16

Comprehensiveness rating: 4

The book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression. The authors make effective use of graphs both to illustrate the subject matter and to teach students how to construct and interpret graphs in their own work. Examples from a variety of disciplines are used to illustrate the material.

The discussion of data analysis is appropriately pitched for use in introductory quantitative analysis courses in a variety of disciplines in the social sciences . However, to meet the needs of this audience, the book should include more discussion of the measurement key concepts, construction of hypotheses, and research design (experiments and quasi-experiments). These are essential components of quantitative analysis courses in the social sciences.

Content Accuracy rating: 5

The book covers familiar topics in statistics and quantitative analysis and the presentation of the material is accurate and effective.

Relevance/Longevity rating: 3

One of the real strengths of the book is the many examples and datasets that it includes. Some of these will continue to be useful over time, but others may be may have a shorter shelf life. In particular, examples and datasets about county characteristics, elections, census data, etc, can become outdated fairly quickly.

Clarity rating: 4

Given that this is an introductory textbook, it is clearly written and accessible to students with a variety of disciplinary backgrounds. The purpose of the course is to teach students technical material and the book is well-designed for achieving that goal.

Consistency rating: 5

Like most statistics books, each topic builds on ones that have come before and readers will have no trouble following the terminology as they progress through the book.

Modularity rating: 5

One of the real strengths of the book is that it is nicely separated into coherent chapters and instructors would will have no trouble picking and choosing among them. For example, the authors have intentionally included a chapter on probability that some instructors may want to include, but others may choose to excludes without loss of continuity.

Organization/Structure/Flow rating: 5

The book does build from a good foundation in univariate statistics and graphical presentation to hypothesis testing and linear regression. There are separate chapters on bi-variate and multiple regression and they work well together. The chapter on hypothesis testing is very clear and effectively used in subsequent chapters.

Interface rating: 5

The formatting and interface are clear and effective. There are lots of graphs in the book and they are very readable. There are also pictures in the book and they appear clear and in the proper place in the chapters.

Grammatical Errors rating: 5

There are no issues with the grammar in the book.

Cultural Relevance rating: 2

The authors present material from lots of different contexts and use multiple examples. They have done an excellent job choosing ones that are likely to be of interest to and understandable by students with diverse backgrounds.

The supplementary material for this book is excellent, particularly if instructors are familiar with R and Latex. The code and datasets are available to reproduce materials from the book. And, the authors have provided Latex code for slides so that instructors can customize the slides to meet their own needs.