Jolynn Pek received her Ph.D. in Quantitative Psychology from the University of North Carolina at Chapel Hill at the L. L. Thurstone Psychometric Laboratory. She is a member of the Society for Multivariate Experimental Psychology (SMEP) and Fellow of the American Psychological Association (APA) Division 5 (Quantitative and Qualitative Methods). She is a recipient of the Early Researcher Award by the Ontario Ministry of Research and Innovation, the Association for Psychological Science (APS) Rising Star Award, the SMEP Early Career Research Award, and the APA Division 5 Anne Anastasi Distinguished Early Career Contributions Award.

Her research interests are motivated by quantifying uncertainty inherent in designing studies and in results obtained from fitting models to data. Quantifying uncertainty is fundamental to understanding the limits of statistical results and ascertaining statistical conclusion validity. Part of this work is also motivated by moving methods into practice.

There are many sources of uncertainty in statistical results. In the phase of study planning, uncertainty is inherent in not knowing enough about the phenomenon under study. Such uncertainty has implications for designing a study, including the use of power analysis. When data have been collected, uncertainty in results may be due to the presence of influential cases which can unduly affect parameter estimates, motivating the development of identification and diagnostic methods. Parameter estimates also carry uncertainty in the form of sampling variability which are communicated by confidence sets; a related but distinct aspect of parameter uncertainty is carried by fungible parameter estimates, which are alternative descriptions of the data afforded by parameter values that are associated with a similar level of fit to the data compared to optimal estimates. Finally, the functional form linking constructs during the exploratory phase of research is uncertain, prompting the development of methods to flexibly recover unknown relationships. Dr. Pek has formalized and developed different approaches to quantify these various sources of uncertainty in terms of estimation, algorithms, measures, and graphical approaches.

Awards and Honors 

• 2023 – 2024: Fred Brown Research Award, Department of Psychology, The Ohio State University, awarded for De Boeck, P., Pek, J., Walton, K. M., Wegener, D. T., Turner, B. M., Andersen, B. A., Beauchaine, T. P., Lecavalier, L., Myung, J., & Petty, R. E. (2023)
• 2022 – 2023: Fred Brown Research Award, Department of Psychology, The Ohio State University, awarded for Flake, Davidson, Wong, & Pek (2022)
• 2022: Anne Anastasi Distinguished Early Career Contributions Award, American Psychological Association, Division 5 (Quantitative & Qualitative Methods)
• 2021 – 2022: Distinguished Teaching Award, Department of Psychology, The Ohio State University
• 2021: Early Career Research Award, Society of Multivariate Experimental Psychology
• 2019 – 2020: Fred Brown Research Award, Department of Psychology, The Ohio State University, awarded for Pek & Park (2019)
• 2020: Fellow, American Psychological Association, Division 5 (Quantitative and Qualitative Methods)
• 2018: Quantitative Methods Research Award, Canadian Psychological Association (Quantitative Methods Section), awarded for Pek & Flora (2018)
• 2017: Rising Star Award, Association for Psychological Science
• 2017: Elected Member, Society of Multivariate Experimental Psychology (SMEP)
• 2017: York Research Leader, York University
• 2013: Dean’s Health Research Catalyst Award, York University
• 2006 – 2007: University Merit Fellowship, University of North Carolina at Chapel Hill
• 2002 – 2004: Research Scholarship, National University of Singapore

Select Publications

*Student co-author during time of research conducted
†Postdoctoral fellow co-author during time of research conducted

• Pek, J., Pitt, M. A., & Wegener, D. T. (in press). Uncertainty limits the use of power analysis. Journal of Experimental Psychology: General.
   
• †Flake, J. K., *Davidson, I., J., *Wong, O., & Pek, J. (2022). Construct validity and the validity of replication studies: A systematic review. American Psychologist. doi: 10.1037/amp0001006
   
• *Park, J., & Pek, J. (2022). Conducting Bayesian-classical hybrid power analysis with R package hybridpower. Multivariate Behavioral Research. doi: 10.1080/00273171.2018.1557032
   
• Wegener, D. T., Fabrigar, L. R., Pek, J., & *Hoisington-Shaw, K. J. (2021). Evaluating research in personality and social psychology: Considerations of statistical power and concerns about false findings. Personality and Social Psychology Bulletin. doi: 10.1177/01461672211030811
   
• Pek, J. & *Park, J. (2019). Complexities in power analysis: Quantifying uncertainties with a Bayesian-classical hybrid approach. Psychological Methods, 24(5), 590–605. doi: 10.1037/met0000208
   
• Pek, J., & Wu, H. (2018). Parameter uncertainty in structural equation models: Confidence sets and fungible estimates. Psychological Methods, 23(4), 635–653. doi: 10.1037/met0000163
   
• Pek, J., *Wong, O. & Wong, A. C. M. (2018). How to address non-normality: A taxonomy of approaches, reviewed and illustrated. Frontiers in Psychology, 9, 2104. doi: 10.3389/fpsyg.2018.02104
   
• Pek, J., & Flora, D. (2018). Reporting effect sizes in original psychological research: A discussion and tutorial. Psychological Methods, 25(5), 590-605. doi: 10.1037/met0000126
   
• †Flake, J. K., Pek, J., & Hehman, E. (2017). Construct validation in social and personality research: Current practice and recommendations. Social Psychological and Personality Science, 8, 370-378. doi: 10.1177/1948550617693063

View additional publications in our faculty research database →