The Ken Kennedy Institute's April Member of the Month, Fred Oswald is a Professor of Psychology, Herbert S Autrey Chair in Social Sciences, and Director of Graduate Studies. Dr. Oswald's research areas focus on Industrial/Organizational Psychology including workforce readiness, quantitative methods (meta-analysis, psychometrics, and big data). Dr. Oswald's lab is the Organization & Workforce Laboratory (OWL). The lab focuses on researching the future of the workforce including employee performance and academic success, workforce readiness and personnel selection, developing psychological tests, big data and modern analytics for organizations and colleges.
Dr. Oswald earned his undergraduate degree in Psychology from the University of Texas at Austin in 1992. From there, he earned his M.A. and Ph.D. in Industrial/Organizational Psychology from the University of Minnesota in 1999.
What is your favorite book?
I'll recommend a few: Confederacy of Dunces, One Hundred Years of Solitude, and a 619-page turner, the North American Scrabble Player's Association Word List, 2020 Edition.
How do you explain your research in one sentence?
I'll test the limits of the one-sentence rule with a semicolon!:
My graduate students and I help create fairer workplaces by reliably assessing the psychological characteristics of job applicants (e.g., knowledge, motivation, interests); this helps ensure that "good data" will contribute to "big data" within company data sets.
What is your favorite aspect of your research?
The most enjoyable aspect of my research is simply sitting with graduate and undergraduate students and simply working things out on a project - ideas, analyses, and writing. These interactions are exciting, challenging, frustrating, sometimes funny, rewarding, and fun.
What challenges do you see in your research that you didn't expect?
Unexpected challenges happen all the time when it comes to implementing projects in organizations. So, we have to try to expect the unexpected. For instance, a close organizational contact may leave for another position, and then your new contact may not understand the work as well (...and this is one reason why it is helpful to have multiple collaborators in an organization). Or your data set might be corrupted (...and this is why you always save a copy of your original data in multiple locations).
How do you see computation and data advancing in the future?
Already, we see legal, ethical, and reputational issues arising surrounding the use of AI and machine learning in the personnel selection context, and that trend will surely continue. Also new technologies (and their data) are becoming more embedded and integrated in the workplace (across employees, teams, and tasks), which will redefine jobs and what performance on the job means. Tensions between employee privacy and employee monitoring will likely only increase in this context.