Respondent-driven sampling is a network-based technique to collect information and make estimation about behavior and composition of social groups in hidden population. The non-randomly selected samples prohibit the use of the sample mean as a statistically valid estimator. Researchers have proposed several asymptotically unbiased estimators, but many fail to realize that the high variance of t...

BACKGROUND Internationally wheelchair users are an emerging demographic phenomenon due to their rapidly increasing life-span coupled with accelerated general population ageing. While having significant healthcare and social implications, basic robust epidemiological information of wheelchair users is often lacking due in part to this population's "hidden" nature. Increasingly popular in epidemi...

Recruiting samples that are more representative of illicit drug users is an on-going challenge in substance abuse research. Respondent-driven sampling (RDS), a new form of chain-referral sampling, is designed to eliminate the bias caused by the non-random selection of the initial recruits and reduce other sources of bias (e.g. bias due to volunteerism and masking) that are usually associated wi...

Respondent-Driven Sampling (RDS) employs a variant of a link-tracing network sampling strategy to collect data from hard-to-reach populations. By tracing the links in the underlying social network, the process exploits the social structure to expand the sample and reduce its dependence on the initial (convenience) sample.The current estimators of population averages make strong assumptions in o...

Respondent-driven sampling (RDS) is one of the most commonly used methods when sampling from hidden or hard-to-reach populations. The RDS methodology combines an improved snowball sampling scheme with a mathematical model that is able to produce unbiased population estimates given that some assumptions about the actual recruitment process are fulfilled. One critical assumption, which is not lik...