Joshua Prasad

Assistant Professor
Industrial Organizational PsychologyJoshua faculty image
Office: 223 BHSCI
Phone: (970) 491-4942
Email: joshua.prasad@colostate.edu
Web Page: https://scholar.google.com/citations?user=JVRH_s0AAAAJ&hl=en

Education: Ph.D., Michigan State University, 2019
Area of Specialization: Assessment use across diverse groups, vocational interests, workplace diversity, authenticity at work, applications of machine learning to IO psychology, longitudinal performance

Teaching Courses: Psy 210 - Psychology of the Individual in Context, PSY 370/371 Psychological Measurement and Testing, PSY 643 - Advanced Industrial Psychology, PSY 652 - Methods of Research in Psychology I
Office Hours:
Monday- | Tuesday- | Wednesday- | Thursday- | Friday- | By Appointment- X

Current Research: Much of my research is inspired by the intersection of workplace phenomena and the tools used to study those phenomena. My earlier work looked at the use of assessments in the workplace (for hiring, employee development, etc.) and why those assessments may approached differently across diverse individuals. For example, responses to survey measures of past achievements may be influenced by the values and experiences one has due to their cultural background. I've also conducted research on vocational interests whereby interests are thought to bring about positive workplace outcomes when one's interests match the demands of their work. The operationalization of this match, via polynomial regression, has inspired multiple studies of mine. My interest in workplace diversity began with assessment, but has grown to include broader workplace experiences including authenticity at work. Along with this growth beyond single assessment instances, I have conducted more work evaluating performance and related constructs over time primarily using archival data. Most recently, I am both concerned with and inspired by the use of machine learning in IO psychology. As a result, have several papers that I'm working on that explore how these methods impact equity in hiring decisions and whether new techniques can be applied to explain how machine learning yields the predictions that inform such decisions.