Respondent-Driven Sampling: a Sampling Method for Hard-to-Reach Populations and Beyond

Purpose of Review We provided an overview of sampling methods for hard-to-reach populations and guidance on implementing one of the most popular approaches: respondent-driven sampling (RDS). Recent Findings Limitations related to generating a sampling frame for marginalized populations can make them hard-to-reach when conducting population health research. Data analyzed from non-probability-based or convenience samples may produce estimates that are biased or not generalizable to the target population. In RDS and time-location sampling (TLS), factors that influence inclusion can be estimated and accounted for in an effort to generate representative samples. RDS is particularly equipped to reach the most hidden members of hard-to-reach populations. TLS, RDS, or a combination can provide a rigorous method to identify and recruit samples from hard-to-reach populations and more generalizable estimates of population characteristics. Researchers interested in sampling hard-to-reach populations should expand their toolkits to include these methods.

Automated summary

The review provides an overview of sampling methods for hard-to-reach populations, emphasizing the advantages of RDS in reaching hidden members of these populations and addressing issues related to generating sampling frames and biased data.