Dingjing Shi

Assistant Professor

Education

Ph.D. in Quantitative Psychology, University of Virginia, 2020

Research Interests

Bayesian statistics, longitudinal models, network science, technology-based ecological momentary assessment and wearable sensors, digital and mobile health

About

I am a quantitative methodologist. My research program develops statistical and computational models and leverages technology-based tools to enhance the estimation, classification, prediction, and assessment of phenomenon in psychological, brain, biomedical, and health-related research.

My research particularly focuses on advancing 1) the methodological aspects of statistical modeling in Bayesian statistics, network science, and longitudinal models to address the challenges posed by high-dimensional, heterogeneous, and time-varying data structures, such as from brain imaging outputs (e.g., MRI, EEG) and multi-sensor wearable streams, 2) the assessment aspects of passive sensing and digital phenotyping, particularly for studying interindividual changes and interindividual differences in affect, behavior, cognition, and their interactions within the context of social, psychological, and environmental factors, and 3) digital tools for ecological momentary assessment and wearable sensors that capture real-time, high-density data input to design personalized, adaptive intervention systems and deliver context-aware and dynamically updated support for individuals.

My work has been funded by agencies including National Science Foundation, National Institutes of Health, Federal Aviation Administration, and Department of Homeland Security. I was the recipient of the APS Rising Star Award from the Association for Psychological Science, the SAS Institute Advanced Statistical Fellowship, and the Citation Abstract Award from the Society of Behavioral Medicine. I currently serve on the editorial boards of Multivariate Behavioral Research and Applied Developmental Sciences, Research Methods Section. My work can be found on the Google Scholar page.

Awards

APS Rising Star Award, Association for Psychological Science, 2026 Thank-a-Teacher Program Recognition, Georgia Institute of Technology, 2026 Vice President and Research Partnership Award for Excellence in Research Grants, University of Oklahoma, 2025 Provost’s Award for Excellence in Transdisciplinary, Convergence Research, University of Oklahoma, 2024 Vice President and Research Partnership Award for Excellence in Research Grants, University of Oklahoma, 2024 Citation Abstract Award, Society of Behavioral Medicine, 2023 Data Science Scholarship, University of Oklahoma, 2022 Junior Faculty Fellowship, University of Oklahoma, 2021 Ed Cline Faculty Development Award, University of Oklahoma, 2020

Selected Publications

Shi, D., Christensen, A., Day, E. A., Golino, H., & Garrido, L. (2024). Exploring estimation procedures for reducing dimensionality in psychological network modeling. Multivariate Behavioral Research, 16, 1-27. doi: 10.1080/00273171.2024.2395941.

Jiang, Z., Ouyang, J., Shi, D., Shi, D., Zhang, J., Xu, L., & Cai, F. (2024). Customizing Bayesian multivariate generalizability theory to mixed-format tests. Behavior Research Methods, 56(7), 8080-8090. doi: 10.3758/s13428-024-02472-7.

Businelle, M., Hébert, E., Shi, D., Benson, L., Kezbers, K., Tonkin, S., Piper, M.E., & Qian, T. (2024). Investigating best practices for ecological momentary assessment: Nationwide factorial experiment. Journal of Medical Internet Research, 26, e50275. doi: 10.2196/50275.

Shi, D., Shi, D., & Fairchild, A. J. (2023). Variable selection for mediators under a Bayesian mediation model. Structural Equation Modeling: A Multidisciplinary Journal, 30(6), 887-900. doi: 10.1080/10705511.2022.2164285

Shi, D., & Tong, X. (2022). Mitigating selection bias: a Bayesian approach to two-stage causal modeling with instrumental variables for nonnormal missing data. Sociological Methods & Research, 51(3), 1052-1099. doi: 10.1177/0049124120914920

Golino, H., Moulder, R., Shi, D., Christensen, A., Garrido, L., Nieto, M. D., Nesselroade, J., Sadana, R., Thiyagarajan, J., & Boker, S. M. (2021). Entropy fit index: a new fit measure for assessing the structure and dimensionality of multiple latent variables. Multivariate Behavioral Research, 56, 874-902. doi: 10.1080/00273171.2020.1779642

Golino, H., Shi, D., Christensen, A., Garrido, L., Nieto, M. D., Sadana, R., Thiyagarajan, J., & Martinez-Molina. (2020). Investigating the performance of Exploratory Graph Analysis and traditional techniques to identify the number of latent factors: a simulation and tutorial. Psychological Methods, 25(3), 292-320. doi: 10.1037/met0000255