Dr. Dingjing Shi is an assistant professor in the Department of Psychology at the University of Oklahoma. Dr. Shi is a quantitative methodologist. Her research program focuses on developing advanced statistical modeling techniques that can be useful to address issues common in behavioral, cognitive, and health-related data. Dr. Shi particularly focuses on the methodological aspects of Bayesian statistics, network psychometric models, longitudinal models, and ecological momentary assessment data. Dr. Shi directs the DISCOVER (Dynamical Interventions, Statistical Computing OVer Evaluation & Research) lab. More recently, she collaborates with health domain researchers and uses digital mobile technology, coupled with statistical modeling to address health-related issues and promote healthy living. Dr. Shi also collaborates with engineering researchers to assess and understand people’s decision-making and decision-choice processes.
Dr. Shi holds faculty affiliate appointments at the Data Scholarship Program at OU and the TSET Health Promotion Research Center at the OU Health Sciences Center. Dr. Shi 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.
[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/