Bayesian Longitudinal Data Analysis

About the Training

Supported by the Institute of Education Sciences, we are conducting a five-day training on Bayesian Longitudinal Data Modeling in Education Sciences.

The purpose of this training program is to fill the existing gap in advanced Bayesian skills among education researchers, thereby advancing the field and enhancing educational outcomes for the next generation. Through a well-designed training plan, the program will introduce a range of Bayesian methods, including growth mixture models, high-dimensional variable selection, and techniques for handling non-normal and missing data. The training is committed to creating an inclusive and active learning environment that integrates statistics and programming, offering interactive sessions where participants can present their work and receive constructive feedback. The program plans to recruit a cohort of 30 participants each year. It features a 5-day in-person workshop, monthly follow-up meetings, and a platform for trainees to present their research. To ensure the training is closely aligned with the needs of the participants, the contents are fine-tuned based on an initial review of the trainees’ application materials. Overall, this program is designed to significantly boost the capacity of education researchers to effectively analyze longitudinal data using cutting-edge Bayesian methods, fostering a deeper understanding of educational processes and contributing to informed policymaking and practice.

Instructors

Cynthia Tong -- University of Virginia

Dr. Tong is an associate professor at the University of Virginia. Her research focuses on developing and applying statistical methods in the areas of developmental and health studies. Methodologically, she is interested in Bayesian methodology, growth curve modeling, and robust structural equation modeling with nonnormal and missing data. Substantively, she is interested in analyzing the longitudinal development of cognitive ability and achievement skills.
Han Du -- University of California, Los Angeles

Dr. Han Du is an assistant professor at the University of California, Los Angeles. Her methodological interests have evolved along three inter-related lines: (1) Bayesian methods and statistical computing; (2) longitudinal data analysis and time series analysis; and (3) study design and sample size determination. She also works on developing new meta-analysis and machine learning techniques. From a substantive perspective, she is interested in applying quantitative methods in developmental, clinical, cognitive, educational, and health research.
Laura Lu -- University of Georgia

Dr. Laura Lu is an associate professor in the Quantitative Methodologies (QM) program at the University of Georgia (UGA). In general, she has expertise in structural equation modeling (SEM), longitudinal data analysis, hierarchical linear modeling (HLM), and computational statistics such as Bayesian. During her professional career at UGA, her research has focused on developing innovative statistical approaches to address perennial challenges in statistical modeling such as mixture structure, reliabilities, model selection, missing data, outliers, topic modeling, and also promoting statistical models to applied research areas through collaborating, mentoring, and classroom teaching.
Qian Zhang -- Florida State University

Dr. Qian (Jackie) Zhang is an associate professor in the Department of Educational Psychology and Learning Systems at Florida State University. Her research interests focus on causal inferences, longitudinal data analysis, and effect size synthesis using multilevel models and multilevel structural equation models. She is also interested in handling sub-optimal data conditions with missing data, measurement error, and/or confounding variables in statistical analyses. She collaborates with substantive researchers in the areas of early childhood development, sports psychology, and educational research. Her work has been published in prestigious journals such as Psychological Methods, Multivariate Behavioral Research, Structural Equation Modeling, British Journal of Mathematical and Statistical Psychology, and Behavioral Research Methods.
Johnny Zhang -- University of Notre Dame

Dr. Johnny Zhang is a professor in Quantitative Psychology at the University of Notre Dame. His research aims to develop better statistical methods and software in the areas of education, health, management and psychology. He has conducted research in the areas of Bayesian methods, Big data analysis, Structural equation modeling, Longitudinal data analysis, Mediation analysis, and Statistical computing and programming. His most recent research involves the development of new methods for social network and text analysis.