Respondent-driven Sampling for Highly Structured Populations

A network-based type of sampling technique and the corresponding set of estimates, known as Respondent- Driven Sampling (RDS), is the current method of choice for many researchers studying hard-to-reach or hidden populations. RDS exploits social networks by starting with a small set of individuals and allowing the respondents at each wave to recruit the next wave of the sample from their contacts. However, it is often unclear whether important assumptions of RDS estimators about the population-specific network structure and the chain-referral recruitment process are satisfied. In this project, focusing on population clustering structures, we will (1) Infer relational structures from egocentric data that are important for RDS feasibility; (2) develop a comprehensive simulation study framework for assessing RDS feasibility; and (3) extend the model-assisted approach to inference from RDS data to account for population clustering. We will apply these new methods to unique observational data on the size and structure of social networks of older GLBT adults from the study Caring and Aging with Pride to inform computer simulations of both social networks and RDS chain-referral processes in order to systematically study the quality of potential RDS estimators in this hard-to-reach population. We will make these methods available in the R-package RDSAnalyst so they can be used by applied RDS researchers to decide whether RDS is warranted in a fashion similar to the sample size computation prior to a funding request for traditional survey research.