Haylee Min

ASSISTANT PROFESSOR

School of Labor and Employment Relations

Haylee Min headshot

247F LER Building, 504 E. Armory Avenue, Champaign, IL 61820

Education

Ph.D. Industrial-Organizational Psychology, Bowling Green State University, 2018
M.A. Industrial-Organizational Psychology, Bowling Green State University, 2016
M.S. Industrial-Organizational Psychology, Illinois Institute of Technology, 2013

Research interests

Application of advanced statistical tools (e.g., machine learning techniques, item response theory) in Industrial/Organizational Psychology

Measurement of individual differences in the workplace, with a focus on diversity, inclusion, and equity

Gender and cultural differences and organizational outcomes.

My research mainly focuses on the development and application of advanced statistical methods (e.g., machine learning techniques) and psychometrics tools (e.g., item response theory) to answer organizational questions. I am especially interested in extending the use of machine learning (ML) techniques to help address important organizational challenges. For instance, I am using ML techniques to examine performance trajectories and dynamics over time. I am also working to improve the feasibility and accessibility of using ML techniques by organizational researchers and practitioners. For example, how can we report ML results in a clear and transparent way that will help improve the replicability of our findings? I also have interests related to workplace diversity, especially detecting subtle forms of discrimination using advanced techniques (e.g., natural language processing algorithms to detect subtle discrimination in language).

Selected Publications

Min, M., Yang, B., Liu, M., Grandey, A., & Allen, D. (2022). Wisdom from the Crowd: Can recommender systems predict employee turnover and its destinations? Personnel Psychology.
Min, H., Peng, Y., Shoss, M., & Yang, B. (2021). Using machine learning to investigate the public’s emotional responses to work from home during the COVID-19 pandemic. Journal of Applied Psychology.
Min, H., & Zickar, M. (2022). The reconceptualization and measurement of Workplace Interpersonal Distrust. Journal of Business and Psychology.
Sun, T., Guo, F., Min, M., & Zhang, B., (2023). Practical machine learning algorithms for selection with multiple-outcome maximization and adverse impact examination: A use case study. Personnel Psychology.– as part of a larger thematic article for the Special Issue on Applying Machine Learning and Artificial Intelligence to Personnel Selection:
  • Koenig, N., Tonidandel, S., Thompson, I., Albritton, B., Koohifar, F., Yankov, G., Speer, A., Hardy, J., Gibson, C., Frost, C., Liu, M., McNeney, D., Capman, J. F., Lowery, S. B., Kitching, M., Nimbkar, A., Boyce, A. S., Sun, T., Guo, F., Min, H., Zhang, B., Lebanoff, L., & Newton, C. (2023). Improving Measurement and Prediction in Personnel Selection through the Application of Machine Learning. Personnel Psychology.
Min, H., Peng, Y., Rosenblatt, A., & Zhang, W., (2022). Psychometric evaluation of age discrimination measures using classic test and item response theories. Work, Aging, and Retirement.
Min, H., Guo, F.*, Jex, S., & Choi, Y.*, (2022). Detecting Measurement Invariance across Age Groups using an Advanced Technique: Item-focused Tree. Work, Aging, and Retirement.

Courses

Employee Motivation and Performance
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