Holly P. O'Rourke, PhD

Biosketch

I am an Associate Professor with the Quantitative Psychology specialization in the Department of Psychology at UC Riverside (and a cooperating faculty in the Department of Statistics). My research and teaching interests include methodological investigations and applications in statistical modeling and data science, with my work focusing on zero-inflated generalized linear models, mediation analysis, data-driven approaches to constructing models, and longitudinal structural equation models. My methodological work is informed by applied issues in the behavioral sciences, particularly the areas of health, substance use, and cognitive neuroscience. In my free time I enjoy volunteering with Statistics Without Borders. On this site you can find information on my papers, courses, and software files.

Topics I Teach

Mediation Analysis 

Structural Equation Modeling

General Linear Modeling (includes topics in multiple regression, ANOVA, and correlation)

Introductory Statistics

Selected methodological publications 

(Full CV here)

O'Rourke, H., & Hilley, C. D. (2025). A guide to specifying effects in latent change score models with moderated mediation. Journal of Behavioral Data Science, 5(2), 1-27. https://doi.org/10.35566/jbds/orourke 

O’Rourke, H. P., & Han, D. E. (2023). Considering the distributional form of zeroes when calculating mediation effects with zero-inflated count outcomes. Journal of Behavioral Data Science, 3(2), 1-14. https://doi.org/10.35566/jbds/v3n2/orourke 

O’Rourke, H. P., Fine, K. L., Grimm, K. J., & MacKinnon, D. P. (2022). The importance of time metric precision when implementing bivariate latent change score models. Multivariate Behavioral Research, 57, 561-580. https://doi.org/10.1080/00273171.2021.1874261

Hilley, C. D., & O’Rourke, H. P. (2022). Dynamic change meets mechanisms of change: Mediation in the latent change score framework. International Journal of Behavioral Development, 46, 125-141. https://doi.org/10.1177/01650254211064352

Grimm, K. J., Helm, J., Rodgers, D., & O’Rourke, H. P. (2021). Analyzing cross-lag effects: A comparison of different cross-lag modeling approaches. New Directions for Child and Adolescent Development, 175, 11-33. https://doi.org/10.1002/cad.20401

O’Rourke, H. P., & Vazquez, E. (2019). Mediation analysis with zero-inflated substance use outcomes: Challenges and recommendations. Addictive Behaviors, 94, 16-25. https://doi.org/10.1016/j.addbeh.2019.01.034

O’Rourke, H. P., & MacKinnon, D. P. (2019). The importance of mediation analysis in substance-use prevention. In Z. Sloboda, H. Petras, E. Robertson, & R. Hingson (Eds.), Prevention of Substance Use (pp. 233-246). Cham, Switzerland: Springer Nature.

O’Rourke, H. P., & MacKinnon, D. P. (2018). Reasons for testing mediation in the absence of an intervention effect: A research imperative in prevention and intervention research. Journal of Studies on Alcohol and Drugs, 79, 171-181. https://doi.org/10.15288/jsad.2018.79.171

Gonzalez, O., O’Rourke, H. P., Wurpts, I. C., & Grimm, K. J. (2018). Analyzing Monte Carlo simulation studies with classification and regression trees. Structural Equation Modeling, 25, 403-413. https://doi.org/10.1080/10705511.2017.1369353

Miočević, M., O’Rourke, H. P., MacKinnon, D. P., & Brown, C. H. (2018). Statistical properties of four effect-size measures for mediation models. Behavior Research Methods, 50, 285-301. https://doi.org/10.3758/s13428-017-0870-1

O’Rourke, H. P., & MacKinnon, D. P. (2015). When the test of mediation is more powerful than the test of the total effect. Behavior Research Methods, 47, 424-442. https://doi.org/10.3758/s13428-014-0481-z