Dr. Wei Wang is an Associate Professor of Psychology at the Graduate Center, City University of New York. He is also a faculty member of the Industrial/Organizational Psychology at Baruch College and the Educational Psychology at the Graduate Center. Dr. Wang earned his Ph.D. in Industrial/Organizational Psychology, M.S. in Statistics, and M.A. in Social/Personality Psychology, all from the University of Illinois at Urbana-Champaign. Dr. Wang’s research interests primarily lie in quantitative methods and computational modeling, and their broad applications in various psychological, managerial, and educational areas. Currently, he is conducting research around three themes: 1) social networks, 2) applied psychometrics, and 3) big data analytics and technology. Dr. Wang has received funding from the National Science Foundation and won the Best Convention Paper Award from the Management of Academy (AoM). Before joining CUNY, Dr. Wang has worked in both academia (UCF, Northwestern University) and the consulting industry, where he worked as an R&D manager developing assessments (using IRT models and computer gamification simulations) for personnel selection and training for various companies, including tech giants.
Young, H., Glerum, D., Wang, W., & Joseph, D. (2018). Who are the Most Engaged at Work? A Meta-Analysis of Personality and Employee Engagement.Journal of Organizational Behavior, 39, 1330–1346. doi: 10.1177/0146167218756031
LaPalme, M., Tay, L., Wang, W. (2018). A Within-person Examination of the Ideal-Point Response Process. Psychological Assessment., 30(5), 567–581.doi: 10.1037/pas0000499
Wang, W., Newman, D. A., & Dipboye, R. L. (2016). Social network contagion in the job satisfaction-intention-turnover model. Academy of Management Proceedings.doi: 10.5465/AMBPP.2016.82.
Wang, W., Hernandez, I., Newman, D. A., He, J., & Bian, J. (2016). Twitter Analysis: Studying U.S. Weekly Trends in Work Stress and Emotion. Applied Psychology: An International Review., 65(2), 355–378. doi: 10.1111/apps.12065.
Wang, W., Lee, P., Joo, S-H., Stark, S., & Louden, R. (2016). MCMC Z-G: An IRT Computer Program for Forced-Choice Noncognitive Measurement. Applied Psychological Measurement., 40, 551–553. doi: 10.1177/0146621616663682
Wang, W., Neuman. E. J., & Newman, D. A. (2014). Statistical power of the social network autocorrelation model. Social Networks, 38, 88-99. doi:10.1016/j.socnet.2014.03.004