Thomas J. Linneman
Social Statistics: Managing Data, Conducting Analyses, Presenting Results, 2nd Edition

Many fundamentally important decisions about our social life are a function of how well we understand and analyze DATA. This sounds so obvious but it is so misunderstood. Social statisticians struggle with this problem in their teaching constantly. This book and its approach is the ally and support of all instructors who want to accomplish this hugely important teaching goal.

This innovative text for undergraduate social statistics courses is, (as one satisfied instructor put it), a “breath of fresh air.” It departs from convention by not covering some techniques and topics that have been in social stat textbooks for 30 years, but that are no longer used by social scientists today. It also includes techniques that conventional wisdom has previously thought to be the province of graduate level courses.

Linneman’s text is for those instructors looking for a thoroughly “modern” way to teach quantitative thinking, problem-solving, and statistical analysis to their students…an undergraduate social statistics course that recognizes the increasing ubiquity of analytical tools in our data-driven age and therefore the practical benefit of learning how to “do statistics,” to “present results” effectively (to employers as well as instructors), and to “interpret” intelligently the quantitative arguments made by others.


At a recent Charter Day celebration, author Tom Linneman was awarded the Thomas Jefferson Teaching Award, the highest award given to young faculty members at the College of William and Mary. The citation for his award noted that Linneman has developed a reputation among his students as a demanding professor – but one who genuinely cares about them.

Keenan A. Pituch and James P. Stevens
Applied Multivariate Statistics for the Social Sciences: Analyses with SAS and IBM’s SPSS, Sixth Edition

Now in its 6th edition, the authoritative textbook Applied Multivariate Statistics for the Social Sciences, continues to provide advanced students with a practical and conceptual understanding of statistical procedures through examples and data-sets from actual research studies. With the added expertise of co-author Keenan Pituch (University of Texas-Austin), this 6th edition retains many key features of the previous editions, including its breadth and depth of coverage, a review chapter on matrix algebra, applied coverage of MANOVA, and emphasis on statistical power. In this new edition, the authors continue to provide practical guidelines for checking the data, assessing assumptions, interpreting, and reporting the results to help students analyze data from their own research confidently and professionally.

Features new to this edition include:

NEW chapter on Logistic Regression (Ch. 11) that helps readers understand and use this very flexible and widely used procedure

NEW chapter on Multivariate Multilevel Modeling (Ch. 14) that helps readers understand the benefits of this “newer” procedure and how it can be used in conventional and multilevel settings

NEW Example Results Section write-ups that illustrate how results should be presented in research papers and journal articles

NEW coverage of missing data (Ch. 1) to help students understand and address problems associated with incomplete data

Completely re-written chapters on Exploratory Factor Analysis (Ch. 9), Hierarchical Linear Modeling (Ch. 13), and Structural Equation Modeling (Ch. 16) with increased focus on understanding models and interpreting results

NEW analysis summaries, inclusion of more syntax explanations, and reduction in the number of SPSS/SAS dialogue boxes to guide students through data analysis in a more streamlined and direct approach

Updated syntax to reflect newest versions of IBM SPSS (21) /SAS (9.3)

A free online resources site with data sets and syntax from the text, additional data sets, and instructor’s resources (including PowerPoint lecture slides for select chapters, a conversion guide for 5th edition adopters, and answers to exercises).

Ideal for advanced graduate-level courses in education, psychology, and other social sciences in which multivariate statistics, advanced statistics, or quantitative techniques courses are taught, this book also appeals to practicing researchers as a valuable reference. Pre-requisites include a course on factorial ANOVA and covariance; however, a working knowledge of matrix algebra is not assumed.

Statistics for Engineers: An Introduction

This practical text is an essential source of information for those wanting to know how to deal with the variability that exists in every engineering situation. Using typical engineering data, it presents the basic statistical methods that are relevant, in simple numerical terms. In addition, statistical terminology is translated into basic English.
In the past, a lack of communication between engineers and statisticians, coupled with poor practical skills in quality management and statistical engineering, was damaging to products and to the economy. The disastrous consequence of setting tight tolerances without regard to the statistical aspect of process data is demonstrated.

This book offers a solution, bridging the gap between statistical science and engineering technology to ensure that the engineers of today are better equipped to serve the manufacturing industry.

Inside, you will find coverage on:

the nature of variability, describing the use of formulae to pin down sources of variation;
engineering design, research and development, demonstrating the methods that help prevent costly mistakes in the early stages of a new product;
production, discussing the use of control charts, and;
management and training, including directing and controlling the quality function.
The Engineering section of the index identifies the role of engineering technology in the service of industrial quality management. The Statistics section identifies points in the text where statistical terminology is used in an explanatory context.

Engineers working on the design and manufacturing of new products find this book invaluable as it develops a statistical method by which they can anticipate and resolve quality problems before launching into production. This book appeals to students in all areas of engineering and also managers concerned with the quality of manufactured products.

Academic engineers can use this text to teach their students basic practical skills in quality management and statistical engineering, without getting involved in the complex mathematical theory of probability on which statistical science is dependent.

Rita Green
Statistical Analyses for Language Testers

‘SALT’ provides a step-by-step approach to the most useful statistical analyses for language test developers and researchers based on the programs IBM SPSS, Winsteps and Facets. Each chapter focuses on one particular type of analysis, for example, analysing how items in a test are performing or investigating the relationship between two variables. Each chapter begins with an introduction as to why this particular analysis is important for the language tester and then provides explanations about the terms and concepts which the reader will meet in the chapter.

The method for carrying out each analysis is then described in a systematic manner guiding the reader through the procedure for that particular analysis. The main aspects of the output files are then investigated and the results explained. Eleven of the book’s appendices provide further opportunities for the reader to repeat many of the statistical procedures on different data sets. Questions guide the reader in checking their understanding of the statistical procedures and output tables.

Richard McElreath
Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Draft)

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.

The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.

By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.

Web Resource
The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

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