Ronald S. Landis, Ph.D.
Ronald S. Landis, Ph.D., is the Nambury S. Raju Professor of Psychology. Ron has primary research interests in the areas of structural equation modeling, multiple regression, and other issues associated with measurement and the prediction of performance. His work has been published in top-tier journals including Organizational Research Methods, Organizational Behavior and Human Decision Processes, Personnel Psychology, and Journal of Applied Psychology. He currently serves as Associate Editor for the Journal of Business and Psychology and is on the editorial boards of Personnel Psychology, Organizational Research Methods, Journal of Management, Human Performance and Journal of Applied Psychology. He was the Principal Investigator (PI) for a three-year NSF-funded study ($1.3 M) examining the roles of emotion, cognition, and meta-cognition in learning science. Landis has also served as a consultant for a number of public and private sector organizations.
Banks, G.C., Field, J.G., Oswald, F.L., O’Boyle, E.H., Landis, R.S. Rupp, D.E., Rogelberg, S.G. (2018). Answers to 18 questions about open science practices. Journal of Business and Psychology, https://doi.org/10.1007/s10869-018-9547-8.
Grand, J.A., Rogelberg, S.G., Banks, G.C., Landis, R.S., & Tonidandel, S. (2018). From outcome to process focus: Fostering a more robust psychological science through registered reports and results-blind reviewing. Perspectives on Psychological Science, 13, 448–456.
Morris, S.B., McAbee, S.T., Landis, R.S., & Bauer, K.N. (2017). Don’t get too confident: Uncertainty in SDρ. Industrial and Organizational Psychology: Perspectives on Science and Practice, 10, 467-472.
Köhler, T., Landis, R.S., & Cortina, J.M. (2017). Establishing methodological rigor in quantitative management learning and education research: The role of design, statistical methods, and reporting standards. Academy of Management Learning & Education, 16, 173-192.
McAbee, S.T., Landis, R.S., & Burke, M.I. (2017). Inductive reasoning: The promise of big data. Human Resource Management Review, 27, 277-290. http://dx.doi.org/10.1016/j.hrmr.2016.08.005
Mackay, M.M., Allen, J., & Landis, R.S. (2017). Is employee engagement a redundant construct? A meta-analytic path analysis. Human Resource Management Review, 27, 108-120. http://dx.doi.org/10.1016/j.hrmr.2016.03.002