Dingjing Shi

Assistant Professor

Education

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

Research Interests

Bayesian statistics, longitudinal data, network models, smartphone and wearable-based ecological momentary assessment

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