Respondent-Driven Sampling Online (Web Rds) as a Strategy to Access Hard-To-Reach But Non-Hidden Populations: The Case of Health Professionals Working in Chilean Schools

Abstract Respondent-driven sampling (RDS) is a method commonly used to access hidden or hard-to-reach populations through peer referral chains. This study examines the application of Web RDS to a population of education support professionals (PEAs) working in inclusive school settings in Chile. Although this population is socially visible, they lack a defined sampling frame and are difficult to access due to regulations that restrict the dissemination of contact information for professionals in the Chilean school system. We used a sequential exploratory mixed-methods design. The formative qualitative phase included interviews with 52 PEAs and guided seed selection and recruitment strategy design. The quantitative Web RDS survey reached 474 participants across 11 recruitment waves. Diagnostic indicators—equilibrium, homophily, recruitment depth, and network-size stability—were calculated to assess whether key RDS assumptions were reasonably met. Additional control mechanisms included institutional email validation, unique access codes, and a set of internal eligibility questions that prevented respondents from continuing the survey when their answers did not meet the study criteria (e.g. confirming current employment in a school and active work as an education support professional). Findings show that Web RDS can be successfully adapted to semi-visible institutional populations beyond its traditional applications in sociological and public health research. The study also provides a transparent discussion of potential sources of error and practical considerations for applying RDS in professional educational contexts.