Odum Institute: Implementing Bayesian Estimation Under Complex Survey Sampling
This course will take place over two days (10/22/24 and 10/24/24) and will be offered via Zoom. Attendance is required as the course will not be recorded.
Complex survey sampling techniques (e.g., clustering, stratification, oversampling) allow for cost efficient estimation for large, dispersed populations. As such, they are frequently used in demographic, health, and public opinion survey research settings. In recent years, Bayesian statistical methods, due to their flexibility and intuitive interpretation relative to frequentist methods, have become increasingly popular for analyzing complex survey sample data; however, complex survey sampling introduces certain features (e.g., unequal selection probabilities, dependencies between observations) that violate traditional statistical assumptions and can bias survey estimates.
This course provides a practical introduction to the csSampling R package, which addresses these issues by implementing Bayesian estimation under complex survey sampling. The course will begin with an introduction to Bayesian statistical methods and complex survey sampling as well as the differences between Bayesian and frequentist methods to account for complex survey design. The bulk of the course will focus on a guided tutorial of the csSampling package with sample data and R code. Finally, the instructors will present use cases of how they have used the package in their own research.