Mijke Rhemtulla

Education

• Ph.D., Developmental Psychology, University of British Columbia, 2010
• M.A., Developmental Psychology, University of British Columbia, 2005
• B.A., Psychology, University of Alberta, 2002

About

In addition to her academic appointment in Psychology, Mijke Rhemtulla is the director of the Psychological Models and Measurement Lab. She is an Associate Editor of Advances in Methods and Practices in Psychological Science, Guest Editor for the Special Issue in Psychometrika on Network Psychometrics in Action, Consulting Editor for Psychological Methods, and Statistical Advisor for Psychological Science. She is a member of the Society for Multivariate Behavioral Methods (SMEP) and the Society for the Improvement of Psychological Science (SIPS).

Research Focus

Structural equation modeling (SEM) is gaining traction across disciplines of psychology as the most versatile and powerful analytic tool available to examine psychological theories. Dr. Rhemtulla develops and studies methods and models within the SEM framework, focusing on practical issues such as how to fit models to ordinal and incomplete data, how to optimize planned missing data designs for SEM models, and how to use item parcels to minimize bias. She also studies theoretical problems, such as how to interpret latent variable representations of psychological constructs, and what theoretical implications arise from competing (e.g., network) models of psychological constructs.

Publications

Wang, Y. A., & Rhemtulla, M. (in press). Power analysis for parameter estimation in structural equation modeling: A discussion and tutorial. Advances in Methods and Practices for Psychological Science.

Rhemtulla, M., van Bork, R., & Borsboom, D. (2020). Worse than measurement error: Consequences of inappropriate latent variable measurement models. Psychological Methods, 25, 30-45. doi:10.1037/met0000220

Wysocki, A. C.*, & Rhemtulla, M. (2019). On penalty parameter selection for estimating network models. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2019.1672516

Grotzinger, A. D.*, Rhemtulla, M., de Vlaming, R., Ritchie, S. J., Mallard, T. T., Hill, W. D., Ip, H. F., Marioni, R., McIntosh, A. M., Deary, I. J., Koellinger, P. D., Harden, K. P., Nivard, M G., & Tucker-Drob, E. M. (2019). Genomic SEM provides insights into the multivariate genetic architecture of complex traits. Nature Human Behavior, 3, 513-525. https://doi.org/10.1038/s41562-019-0566-x

Schott, E., Rhemtulla, M., & Byers-Heinlein, K. (2019). Should I test more babies? Solutions for transparent data peeking. Infant Behavior and Development, 54, 166-176

Savalei, V., & Rhemtulla, M. (2017). Normal theory two-stage estimator for models with composites when data are missing at the item level. Journal of Educational and Behavioral Statistics, 42, 405-431.

Rhemtulla, M., & Hancock, D. (2016). Planned missing data designs for educational research. Educational Psychologist, 51, 305-316.

Rhemtulla, M. (2016). Population performance of SEM parceling strategies under measurement and structural model misspecification. Psychological Methods, 21, 348-368.

Rhemtulla, M., Savalei, V., & Little, T. D. (2016). On the asymptotic relative efficiency of planned missingness designs. Psychometrika, 81, 60-89.

Rhemtulla, M., Brosseau-Liard, P., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under sub-optimal conditions. Psychological Methods, 17, 354-373.

Teaching

Dr. Rhemtulla teaches Structural Equation Modeling (PSC 205C), Measurement (PSC 104 and 205E), and Intro/Advanced Statistical Methods for Psychology (103B and 204A).

She has previously taught courses in Multivariate Analysis and Missing Data.

Awards

Dr. Rhemtulla has received awards and fellowships from the European Research Council, the Social Sciences and Humanities Granting Council of Canada, and the National Sciences and Engineering Granting Council of Canada.