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.

Learning Objectives for the course are:

●      Define key concepts in complex sample survey data collection and analysis.

●      Define key concepts in Bayesian statistical methods.

●      Compare Bayesian and frequentist approaches to account for complex survey sampling designs.

●      Understand the functionality of the csSampling R package.

●      Describe prior applications of the csSampling R package to real-world data analysis problems.

●      Implement sample R code to build Bayesian models analyzing complex sample survey data.

Prior experience using R to manipulate and analyze data will be assumed. Some familiarity with either Bayesian statistics and/or complex sample survey designs will also be helpful but is not required.