Modeling longitudinal data is one of the most active areas of research in social and behavioral sciences because longitudinal research provides valuable insights into change and causal relationships. The application of Bayesian methods in longitudinal research has gained increasing popularity. Taught by four quantitative researchers who are active in developing Bayesian methods and longitudinal data analytical methods, this workshop will teach participants how to analyze longitudinal data using Bayesian statistics. We will introduce the basic idea of Bayes' theorem first and move on to models including multilevel models, growth curve models, growth mixture models, and longitudinal structural equation models. Concrete examples will be provided to illustrate how to compute, report, and interpret Bayesian modeling results with empirical psychological data. Additionally, we will teach the application of Bayesian robust methods for nonnormal data ignorable and non-ignorable missing data.