Sampling Methods for People, Networks, and Records

Every meaningful data insight begins with a well-constructed sample, yet choosing the right subjects is often the most overlooked step in the research process. This course teaches you how to move beyond haphazard data collection to implement structured sampling strategies that ensure your findings are accurate and representative. By the end of this course, you will be able to confidently select the most appropriate sampling method for any project, whether you are dealing with individuals, complex social structures, or historical records. What you'll learn: - Understand the core terminology and mathematical foundations of sampling theory - Differentiate between probability-based and non-probability selection methods - Apply specific techniques for sampling social networks and interconnected populations - Learn the unique requirements for extracting representative data from administrative records - Identify and mitigate common sources of selection bias and sampling error - Explore modern approaches for sampling in digital environments and hard-to-reach groups The course starts with essential definitions and the logic of inference before guiding you through written explanations of practical applications for various data types and research environments. This program is designed for beginners and requires no prior background in statistics or research design. Start building a stronger foundation for your data collection today.