ngaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and Bayesian approaches include hypothesis testing, replication, analysis of variance, calculation of effect sizes, regression, time series analysis, and more. Students also get a complete introduction to the open-source R programming language and its key packages. Throughout the text, simple commands in R demonstrate essential data analysis skills using real-data examples. The companion website provides annotated R code for the book's examples, in-class exercises, supplemental reading lists, and links to online videos, interactive materials, and other resources.
• Playful, conversational style and gradual approach; suitable for students without strong math backgrounds.
• End-of-chapter exercises based on real data supplied in the free R package.
• Technical Explanation and Equation/Output boxes.
• Appendices on how to install R and work with the sample datasets.
“What do R and traditional and Bayesian statistics have in common? They allow us to answer questions that are important for science and practice. Stanton has produced a wonderful book that will be useful for students as well as established scholars.”
—Herman Aguinis, PhD, Avram Tucker Distinguished Scholar and Professor of Management, George Washington University School of Business
“Reasoning with Data takes a careful and principled approach to guiding readers gracefully from the traditional moorings of frequentist statistics into Bayesian analyses and the functionality and frontiers of the R platform. Stanton provides a range of clear explanations, examples, and practice exercises, fueled by his unbounded enthusiasm and rock-solid expertise. This book is an indispensable resource for undergraduate and graduate students across disciplines—as well as researchers—who want to extend their thinking and their research into where the future is headed.”
—Frederick L. Oswald, PhD, Department of Psychology, Rice University
About the Author:
Jeffrey M. Stanton, PhD, is Associate Provost for Academic Affairs and Professor in the School of Information Studies at Syracuse University. Dr. Stanton's interests center on research methods, psychometrics, and statistics, with a particular focus on self-report techniques, such as surveys. He conduct research on a variety of substantive topics in organizational psychology, including the interactions of people and technology in institutional contexts. He is the author of numerous scholarly articles and several books, including Information Nation: Education and Careers in the Emerging Information Professions and An Introduction to Data Science. Dr. Stanton’s background also includes more than a decade of experience in business, both in established firms and startup companies.