Modeling longitudinal data is one of the most active areas of research in social, behavioral, and education sciences because longitudinal data can provide valuable insights into change and causal relationships. The application of Bayesian methods in longitudinal research has gained increasing popularity. This interactive workshop focuses on Bayesian methods in analyzing longitudinal data. Particularly, it will cover topics on growth mixture modeling, missing data analysis, and Bayesian model assessment. Concrete examples will be provided to illustrate how to compute, report, and interpret Bayesian modeling results with empirical psychological data.
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.
This workshop is supported by the William K. and Katherine W. Estes Fund that is jointly overseen by the Association for Psychological Science and the Psychonomics Society.
- Instructor: Cynthia Tong