The current serving members of the Quantitative Methods executive committee are:
Dr. Robert Cribbie (Chair)
Dr. Cribbie is a Professor in the Quantitative Methods Area of the Department of Psychology at York University. He received his PhD from the Department of Psychology at the University of Manitoba specializing in robust ANOVA strategies, structural equation modeling, and multiplicity control. At York he teaches both graduate and undergraduate psychology courses in quantitative methods and has been a long time member of the Statistical Consulting Service. His current research centers around equivalence testing, multiplicity control, effect sizes, robust statistics, and related topics.
Dr. Andrea Howard (Past- Chair)
Dr. Howard is an Associate Professor in the Department of Psychology at Carleton University, with a research program focused on promoting well-being and mental health in adolescence and the transition to adulthood. The unique challenges of the transition to university figure prominently in her work. Dr. Howard trained as a developmental psychologist during her PhD program at the University of Alberta, and worked for three years as a postdoctoral researcher in the quantitative psychology program at the University of North Carolina at Chapel Hill studying innovative methods for developmental data analysis. She teaches quantitative methods courses in her graduate program and publishes research answering a diverse range of questions about youth development, health behaviours, mental health, and quantitative methods . Her research is currently funded by grants from the Social Sciences and Humanities Research Council of Canada, the Canadian Institutes of Health Research, and the U.S. National Institutes of Health.
Dr. Johnson Li (Chair- Elect)
Dr. Johnson Li is an Associate Professor in the Department of Psychology at the University of Manitoba. His research focuses on developing new measurements and statistical models for use by researchers, teachers, and psychologists, many of which have implications for theory and practice in psychology and education. Dr. Li’s research interests include effect size estimates, corrections for study artifacts, creation and evaluation of educational and psychological scales, meta-analysis, robust statistics, probability-based statistical measures, structural equation modeling, cognitive diagnostic modeling, and reliability assessment.
Dr. Oscar L. Olvera Astivia (Communications)
Dr. Olvera Astivia is an assistant professor in the College of Eduction at the University of Washington. Dr. Olvera Astivia specializes in psychometric and statistical research, with special emphasis on the mathematical properties of data-generating algorithms for Monte Carlo simulations. He is interested in studying multivariate, non-normal spaces through the use of copula distribution theory and how this framework can be applied to latent variable modelling. He also has a special focus on revisiting what can be considered “best practices” or “common knowledge” in data analysis and look at their implicit assumptions.
Dr. Olvera Astivia’s work has been published in the “British Journal of Mathematical and Statistical Psychology,” the “Journal of Educational and Behavioral Statistics,” and “Educational and Psychological Measurement,” among others.
Dr. Milica Miočević (Secretary/Treasurer)
Dr. Milica Miočević is an Assistant Professor in the Psychology department at McGill University. She received her PhD in Quantitative Psychology from Arizona State University in 2017. Dr. Miočević’s has three research lines that focus on: 1) optimal methods for using historical data and pilot studies to create informative prior distributions for Bayesian mediation analysis, 2) methods for synthesizing findings about the mediated effect in the presence of important between-study differences (e.g., samples from different populations), and 3) mediation analysis in Single Case Experimental Designs (SCEDs).
Udi Alter (Student Representative)
Udi Alter is a second-year master’s student at Ryerson University’s Research Methods and Statistics lab in the Psychology Department. He is interested in equivalence testing, accuracy in parameter estimation, effect sizes, and Monte Carlo simulations. In addition, Udi is passionate about teaching statistics, data science, programming, and philosophy of science. He is an advocate for open-science practices, replicable and reproducible research, and an avid R user.