Unlocking the art and science of sampling with an applied, hands-on approach, the course Sampling in Practice is designed for applied practitioners who want to master real-world sampling techniques through active learning and practical programming. Students will learn about probability sampling methods, including simple random sampling, stratification, systematic selection, cluster sampling, probability proportional to size sampling, and multistage sampling. We will also cover sampling cost models, sampling error estimation techniques, non-sampling errors, missing data, and nonprobability samples. The course emphasizes practical implementation, featuring interactive coding exercises and in-class examples to reinforce each concept. A culminating project will give students the opportunity to integrate multiple techniques into a comprehensive sample design and demonstrate the profession in designing surveys, selecting subjects, analyzing sample data, and solving real sampling problems using modern statistical tools.
Why take this course?
The course is crafted for students and practitioners eager:
- To build proficiency in modern sampling techniques through active engagement and practical coding experience
- To understand the basic ideas, concepts and principles of probability sampling from an applied perspective
- To be able to identify and appropriately apply sampling techniques to survey design problems
- To understand and be able to assess the impact of the sample design on survey estimates
- To be able to compute the sample size for a variety of sample designs
- To learn how to design and select a probability sample involving complex sampling techniques in a survey project, and receive expert feedback on a sampling report