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Dingjing Shi

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Research

Dr. Dingjing Shi is an assistant professor in the Department of Psychology at the University of Oklahoma. She received her Ph.D. and M.A. in quantitative psychology at the University of Virginia, and her M.S. in learning and developmental sciences at Indiana University Bloomington.

Dingjing’s research interests focus on developing and applying latent variable models and data mining techniques to analyze complex-structured, psychometric, and contaminated data. Dingjing is particularly interested in the methodological aspects of Bayesian statistics, longitudinal models, and computational psychometrics. She is also interested in applying statistical methods to developmental, cognitive and health-related studies.

Selected Publications:

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.org/10.1037/met0000255

Shi, D., & Tong, X. (2020). Mitigating selection bias: a Bayesian approach to two-stage causal modeling with instrumental variables for nonnormal missing data. Sociological Methods & Research. https://doi.org/10.1177/0049124120914920

Shi, D., Tong, X., & Meyer, J. M. (2020). A Bayesian approach to mitigating selection bias in causal modeling: a tutorial with the ALMOND package in R. Frontiers in Psychology, section Quantitative Psychology and Measurement, 11:169. doi: 10.3389/fpsyg.2020.00169

Golino, H., Moulder, R., Shi, D., Christensen, A., Garrido, L., Nieto, M. D., Nesselroade, J., Sadana, R., Thiyagarajan, J., & Boker, S. M. (2020). Entropy fit index: a new fit measure for assessing the structure and dimensionality of multiple latent variables. Multivariate Behavioral Research. DOI: 10.1080/00273171.2020.1779642

[R Package]. ALMOND: Bayesian Analysis of LATE (Local Average Treatment Effect) for Missing Or/and Nonnormal Data. Role: Author, Creator.

Retrievable from https://github.com/dingjshi/ALMOND, and

available at Rdevtools::install_github(‘dingjshi/ALMOND’) in R.

[Shiny app]. Dimensionality assessment for psychometric properties: a tool to simulate data, analyze empirical data, and conduct Monte Carlo simulations using the Shiny web app. Role: Contributor

Retrievable from https://appdim.shinyapps.io/app_dimensionality/