Pascal R. Deboeck, Ph.D.

Research Interests

I am an associate professor at the University of Utah. Trained as a quantitative psychologist, I work to develop, improve, study and apply statistics to social, behavioral, and medical data.

My interests are focused on the development and application of methods for the analysis of intensive, intraindividual time series. I focus on the development and application of derivatives, differential equation modeling, and dynamical systems concepts to time series that have characteristics common to behavioral and some physiological measures such as relatively low sampling rates, large amounts of measurement and/or dynamic error, and unequally spaced or missing observations. In analyzing such data, I often focus on questions related to the role of variability and less-stable change (the “error” in many statistical models). These methods have the potential to inform theories that address how, when, and why people change over time.

I have worked with a range of applied topics including: resiliency and affect in older adults, health and depression as long-term outcomes of daily stress processing, sustained attention while driving, adult attachment, the coupling of maternal depression with child behavior, modeling of proteins associated with Alzheimer's, mood change in patients with rapid cycling bipolar disorder, and the motion of dancing individuals and dyads.

Opportunities for Students

I am currently looking for graduate students interested in combining expertise in quantitative methods for modeling repeated observations with Developmental Psychology, Social Psychology, Cognition & Neural Science, or Clinical Psychology.

Education

Ph.D. University of Notre Dame (Quantitative Psychology, 2007)

Selected Publications

Deboeck, P. R., Cole, D. A., Preacher, K. J., Forehand, R. & Compas, B. E. (2021). Modeling Dynamic Processes with Panel Data: An Application of Continuous Time Models to Prevention Research. International Journal of Behavioral Development, 45(1), 28–39. doi: 10.1177/0165025420905354

Deboeck, P. R. (2020). Empirical Bayes Derivative Estimates. Multivariate Behavioral Research, 55:3, 382– 404. doi: 10.1080/00273171.2019.1642729.

Bulik, C. M., Butner, J. E., Tregarthen, J., Thornton, L. M., Flatt, R. E., Smith, T., Carroll, I. M., Baucom, B. R.W. & Deboeck, P. R. (2020). The Binge Eating Genetics Initiative (BEGIN): study protocol. BMC Psychiatry, 20(307), 1–9, doi: 10.1186/s12888-020-02698-7.

Fraley, R. C., Gillath, O., & Deboeck, P. R. (2020). Do life events lead to enduring changes in adult attachment styles? A naturalistic longitudinal investigation. Journal of Personality and Social Psychology, 120(6), 1567–1606. doi: 10.1037/pspi0000326.

Deboeck, P. R., Preacher, K. J. & Cole, D. A. (2018). Mediation Modeling: Differing Perspectives on Time Alter Mediation Inferences. In K. van Montfort, J. Oud & M. Voelkle (Eds.) Continuous Time Modeling in the Behavioral and Related Sciences, pp. 179–203. Springer.

Deboeck, P. R. & Boulton, A. J. (2016). Integration of Stochastic Differential Equations using Structural Equation Modeling: A Method to Facilitate Model Fitting and Pedagogy. Structural Equation Modeling, 23, 888–903.

Deboeck, P. R., Nicholson, J. S., Kouros, C. D., Little, T. D., & Garber, J. (2015). Integrating developmental theory and methodology: Using derivatives to articulate change theories, models, and inferences. Applied Developmental Science.

Deboeck, P. R., & Preacher, K. J. (2015). No Need to be Discrete: A Method for Continuous Time Mediation Analysis. Structural Equation Modeling.

Deboeck, P. R., Nicholson, J. S., Bergeman, C. S., & Preacher, K. J. (2013). From Modeling Long–Term Growth to Short–Term Fluctuations: Differential Equation Modeling is the Language of Change. (pp. 427-447).

Bergeman, C. S. & Deboeck, P. R. (2014). Trait stress resistance and dynamic stress dissipation on health and well-being: The reservoir model. Research in Human Development, 11(2), 108–125.

Deboeck, P. R., & Bergeman, C. S. (2013). The Reservoir Model: A Differential Equation Model of Psychological Capacity. Psychological Methods, 18(2), 237-256.

Montpetit, M. A., Bergeman, C. S., Deboeck, P. R., Tiberio, S. S. & Boker, S. M. (2010) Resilience–As–Process: Negative Affect, Stress, and Coupled Dynamical Systems. Psychology and Aging, 25 (3), 631–640.

Recent Grants

• Advancing understanding of racism-related health disparities beginning before birth: A multisite study with Black and Latina pregnant women. (PI: Sierra Carter, Elisabeth Conradt, Sheila Crowell) Role: Co–Investigator. National Institutes of Mental Health; Approximate July 2022 — June 2027; Total Costs, University of Utah Subaward: $351,422.
• Clinical markers of neonatal opioid withdrawal syndrome: onset, severity, and longitudinal neurodevelopmental outcome. (PI: Barry Lester, Elizabeth Conradt) Role: Co–Investigator. National Institutes of Health, National Institute on Drug Abuse; September 2019 — August 2024; Total Costs: $1,706,873.
• Predicting binge and purge episodes from passive and active Apple Watch data using a dynamical systems approach. (PIs: Cynthia Bulik, Jonathan Butner) Role: Co–Investigator. National Institutes of Health, National Institute on Aging; September 2019 — August 2023; Total Costs: $1,134,679.
• Family-focused melanoma preventive intervention for children of survivors. (PI: Yelena Wu) Role: Collaborator. American Cancer Society; January 2020 — December 2023; Total Costs: $164,996.
• Collaborative Research: Separating transient and enduring forms of change in adult attachment styles. (PIs: R. Chris Fraley, Omri Gillath, Pascal R. Deboeck) National Science Foundation; September 2014 — August 2019; Total Costs: $550,000.

Other Research Interests

• Intensive Longitudinal Data, Intraindividual Time Series, Diary Data, Ecological Momentary Assessments
• Dynamical Systems
• Derivatives & Differential Equation Models
• Longitudinal Data Analysis
• Intraindividual Modeling