Wei Wang
Position: Associate Professor
Programs: Psychology | Educational Psychology
Campus Affiliation: Graduate Center
Phone: 212-817-8714
Degrees/Diplomas: Ph.D. in Industrial/Organizational Psychology, M.S. in Statistics, M.A. in Social/Personality Psychology, all from University of Illinois, Urbana-Champaign
Research Interests: Big Data Analytics and Computational Modeling (e.g., text mining and analysis, machine learning, etc.); Applied Psychometrics and Technology (e.g., measuring personality, intelligence, emotional intelligence and aggression with eye-tracking techniques); Social Network Analysis (e.g., network contagion for job attitudes, turnover, (un)ethical behaviors, performance etc.).
Wei Wang is an Associate Professor 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. His research interests primarily lie in quantitative methods and computational modeling, and their broad applications in various psychological, managerial, and educational areas. Currently. He 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 experiences in both the 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.

In addition to joining The Graduate Center as an associate professor of psychology and of educational psychology, Wang is a faculty member in the Industrial/Organizational Psychology program at Baruch College. His research interests primarily lie in quantitative methods and computational modeling, and in their broad applications in various psychological, managerial, and educational areas. Currently he is conducting research around three themes: social networks, applied psychometrics, and big data analytics and technology. Wang has received funding from the National Science Foundation and he won the Best Convention Paper Award from the Academy of Management.

Awards and Grants
  • Image Visual Perception and Aggression: A Machine Learning Eye-Tracking Approach, Program of Robust Intelligence (RI), Division of Information & Intelligent Systems, National Science Foundation (NSF)
  • Top Downloaded Paper (2018 – 2019) Award, Wiley
  • Medium: Collaborative Research: Understanding and Editing Visual Sentiment, National Science Foundation (NSF)
  • John C. Flanagan Award (For the Best Posters at the 2017 SIOP Conference)
  • Best Convention Paper Award, the Academy of Management, HR Division (#1 overall paper submitted to HR Division in AOM 2016)
Professional Affiliations and Memberships
  • Society for Industrial-Organizational Psychology (APA Division 14)
  • Academy of Management
  • The Psychometric Society
Courses Taught
  • Hierarchical Linear Models (PSYC86002/EPSY84200)
  • Structural Equation Modeling (PSYC86001)
  • Statistical Methods in Psychology II (PSYC70600)
  • Statistical Methods in Psychology I (PSYC70500)
  • Yang, J., Liu, Y., Stackhouse, M., & Wang, W. (in press). Forgiveness and Attribution: When Abusive Supervision Enhances Performance. Journal of Managerial Psychology.
  • Yuksel, M., Wang, W., Chaudhry, S., Turgut, D., & Kapucu, D. (2019). Challenges and Opportunities in Utilizing IoT-Based Stress Maps as a Community Mood Detector. The 2019 IEEE International Symposium on Technologies for Homeland Security Proceedings. https://doi.org/10.1109/HST47167.2019.9032995
  • 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. https://doi.org/10.1002/job.2303   
  • Peng, P., Barnes, M., Wang, C., Wang, W., Li, S., Swanson, L., Dardick, W., & Tao, S. (2018). A Meta-Analysis on the Relation between Reading and Working Memory. Psychological Bulletin, 144(1), 48–76. http://dx.doi.org/10.1037/bul0000124
  • LaPalme, M., Tay, L., Wang, W. (2018). A Within-person Examination of the Ideal-Point Response Process. Psychological Assessment, 30(5), 567–581. https://doi.org/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. https://doi.org/10.5465/ambpp.2016.82
  • 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. https://doi.org/10.1177/0146621616663682
  • 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. https://doi.org/10.1111/apps.12065
  • LaPalme, M. L., Wang, W., Joseph, D. L, Saklofske, D., & Yan, G-G. (2016). Measurement equivalence of the Wong and Law Emotional Intelligence Scale across cultures: An item response theory approach. Personality and Individual Differences, 90, 190–198. https://doi.org/10.1016/j.paid.2015.10.045
  • Wang, W., de la Torre, J., & Drasgow, F. (2014). MCMC GGUM: A New Computer Program for Estimating Unfolding IRT Models. Applied Psychological Measurement. https://doi.org/10.1177/0146621614540514
  • Wang, W., Neuman. E. J., & Newman, D. A. (2014). Statistical power of the social network autocorrelation model. Social Networks, 38, 88–99. https://doi.org/10.1016/j.socnet.2014.03.00