The Workplace Assessment and Social Perceptions (WASP) Lab uses modern methods, including natural language processing and machine learning, to investigate workplace phenomena including: human and machine bias, impression formation, and personality. Our work addresses both social and technical issues, and its multidisciplinary nature provides opportunities for collaboration with computer scientists and other engineers.
Our research on first impressions was recently featured in the No Stupid Questions podcast.
We are accepting research assistant applications for next semester! If you are interested, please complete this application and email it to email@example.com together with your resume/CV.
Students who participate as research assistants (in PSYC 2994/4994 or volunteer) for two or more semesters will receive a letter of recommendation for whatever is next in their career (e.g., graduate school, work).
Director - Louis Hickman
Select Publications and Presentations
- Zhang, N., Wang, M., Xu, H., Koenig, N., Hickman, L., Kuruzovich, J., Ng, V., Arhin, K., Wilson, D., Song, Q. C., Tang, C., Alexander III, Leo., & Kim, Y. Reducing subgroup differences in personnel selection through the application of machine learning. Personnel Psychology, advance online publication. *First author of Paper 2, fourth author of Paper 3 (manuscript has three papers). https://doi.org/10.1111/peps.12593
- Hickman, L., Bosch, N., Ng, V., Saef, R., Tay, L., & Woo, S. E. (2022). Automated video interview personality assessments: Reliability, validity, and generalizability investigations. Journal of Applied Psychology, 107(8), 1323–1351. https://doi.org/10.1037/apl0000695
- Hickman, L., Thapa, S., Tay, L., Cao, M., & Srinivasan, P. (2022). Text preprocessing for text mining in organizational research: Review and recommendations. Organizational Research Methods, 25(1), 114-146. https://doi.org/10.1177/1094428120971683
- Hickman, L., Song, Q. C., & Woo, S. E. (2022). Evaluating data. In K. Murphy (Ed.), Data, Methods, and Theory in Organizational Sciences. SIOP Organizational Frontiers Series.