Jeffrey M. Stanton

323E Hinds Hall
Phone: 315.443.2879
Jeffrey Stanton

Jeffrey M. Stanton, Ph.D. (University of Connecticut, 1997) is a Professor in the School of Information Studies at Syracuse University. Stanton’s academic speciality is applied data science. Dr. Stanton served as action editor for the journal Human Resource Management from 2004-2011 and he currently serves on the editorial board of Organizational Research Methods, the premier methodological journal in the field of management.

Stanton has published research in job satisfaction, work-related stress, psychometrics, and statistics with a focus on self report techniques. He has conducted projects that have applied the principles of behavioral science and organizational research towards understanding the interactions of people and technology in institutional contexts. His background also includes years of experience in start-up companies. For example, Stanton worked as a human resources analyst for Applied Psychological Techniques, an HR consulting firm based in Darien, Connecticut. His projects at this firm included the creation, implementation, and assessment of a performance appraisal system, development of a selection battery for customer service representatives, and the creation of a job classification and work standards system for over 350 positions in the public utilities industry.

He has written four books: Reasoning with Data: An Introduction to Traditional and Bayesian Statistics with RAn Introduction to Data Science with lead author and fellow iSchool professor Jeffrey Saltz, Information Nation: Education and Careers in the Emerging Information Professions, with Dr. Indira Guzman and Dr. Kathryn Stam; and The Visible Employee: Using Workplace Monitoring and Surveillance to Protect Information Assets Without Compromising Employee Privacy or Trust, with Dr. Kathryn Stam. Here is additional information about each book:

Reasoning with Data (Guilford)

This book teaches readers how to use inferential statistical thinking to check assumptions, assess statistical evidence, communicate results accurately, and avoid over-interpreting 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.

Publisher page:

Companion Website:

Azure Notebooks with R Code:

An Introduction to Data Science (with Dr. Jeff Saltz; Sage)

This book provides an easy-to-read, gentle introduction for advanced undergraduate, certificate, and graduate students coming from a wide range of backgrounds into the world of data science. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using the R programming language.

Publisher page:

Student Study Site:

Azure Notebooks with R Code:

Information Nation (with Dr. Kathryn Stam and IDr. ndira Guzman)

This book presents three years of NSF-supported research on students and workers in the information professions, highlighting barriers to inclusion and retention of U.S. students in information-related fields. The book analyzes the forces that prevent high school and college students from getting the interdisciplinary skills they need to help the U.S. regain and retain leadership in the world of information—and tells the stories of a diverse group of students who are thriving in new majors and new jobs that have the potential to revitalize our economy.

Publisher page:

The Visible Employee (with Dr. Kathryn Stam)

This book reports the results of a four-year, NSF-supported research project, covering a range of security solutions for at-risk organizations as well as the perceptions and attitudes of employees toward monitoring and surveillance. The result is a wake-up call for business owners, managers, and IT staff, as well as an eye-opening dose of reality for employees.

Publisher page:


My interests in statistics have led directly into research on data mining and machine learning. As a result, I have begun to work in an emerging area called applied data science, which focuses on the management, analysis, and visualization of large data sets. I have published many scholarly articles in peer-reviewed behavioral science journals, such as the Journal of Applied Psychology, Personnel Psychology, and Human Performance. My articles also appear in Computers and Security, Communications of the ACM, Computers in Human Behavior, the International Journal of Human-Computer Interaction, Information Technology and People, the Journal of Information Systems Education, the Journal of Digital Information, Surveillance and Society, and Behaviour Information Technology. I have a number of book chapters on privacy, research methods, and program evaluation. My research has been supported through more than 20 grants, contracts, and supplements including the National Science Foundation CAREER award. I’ve recently published my first textbook: Reasoning with Data: An Introduction to Traditional and Bayesian Statistics Using R. The publisher site for the book contains a link to teaching resources that I have developed for IST 777/IST 772 students.


Over the past 20 years I have done organizational consulting in a variety of for-profit and non-profit firms. This work has included the development of performance management systems, organizational surveys, information security assessments, climate assessments, training, and coaching. From 2010-2016, I was a co-principal investigator on Syracuse University’s NSF-ADVANCE institutional transformation project, an effort to improve recruiting, retention, and career development for women faculty in science, technology, engineering, and mathematics disciplines.


While at the School of Information Studies, I have taught a variety of courses, at the undergraduate, master’s, and doctoral levels. I helped to design the school’s “Innovation Studio,” which is a classroom environment designed to encourage hands-on, active, problem-focused work and to discourage lecturing. One of the most fun classes I taught in the Innovation Studio was “Design and Virtual Worlds,” a class that taught students from a range of backgrounds the tools and strategies for creating immersive, three dimensional, virtual social environments. This is the only class I have taught where students were allowed to fly around the classroom.

I’m the professor of record for IST 772 and IST 777, quantitative methods courses for master’s and doctoral students respectively. IST 772 uses the Reasoning with Data textbook mentioned above and focuses on developing a deep understanding of statistical inference from both Bayesian and frequentist perspectives. Here’s a recent syllabus for IST 772 – Quantitative Reasoning for Data Science.


I am a songwriter and musician (mainly bass and guitar).