Respondentdriven

Respondentdriven is a term used to describe sampling approaches that build on respondent-driven sampling (RDS), a chain-referral method developed for studying hidden or hard-to-reach populations. In a typical respondent-driven design, researchers select a small number of seed participants who meet study criteria. Each seed receives a fixed number of recruitment coupons to invite peers, who in turn recruit others, generating successive waves. The process relies on social networks to access participants who may not be reachable through conventional sampling methods. Financial or non-monetary incentives are commonly used to encourage recruitment, and researchers collect information about each respondent’s network size to support subsequent weighting.

To produce population-level inferences, analysts apply specialized estimators that adjust for differential recruitment and network size, such as RDS-weighted means and proportions. Assumptions include a sufficiently connected population network, random recruitment within social ties, and eventual convergence to sampling independence from the initial seeds (equilibrium). The method often uses software tools to model chain-referral processes and compute weights.

Applications include epidemiology, sociology, and public health research on stigmatized or hard-to-reach groups (e.g., injection drug users, sex workers, men who have sex with men in conservative contexts). Limitations include sensitivity to seed selection, recruitment patterns, network structure, and accurate reporting of network size; potential biases if assumptions fail; ethical considerations around confidentiality and coercion. Variants and methodological refinements continue to address biases and improve weighting methods.