Thursday, January 21, 2016 - 3:00pm
209 W. Eighteenth Ave. (EA), Room 170
Modeling Preferential Recruitment for Respondent-Driven Sampling
Katherine McLaughlin, University of California, Los Angeles
Respondent-driven sampling (RDS) is a network sampling methodology used worldwide to sample key populations at high risk for HIV/AIDS who are not typically reachable by conventional sampling techniques. In RDS, study participants recruit members of their social network to enroll, resulting in a sampling mechanism that is unknown to researchers. Current estimators for RDS data require many assumptions about the sampling process. A common assumption is that recruiters choose people from their network uniformly at random to participate in the study. However, in practice people likely recruit preferentially based on covariates such as age, race, or frequency of interaction.
In this talk, I present a sequential two-sided rational-choice framework to model preferential recruitment. At each wave of recruitment, each recruiter has a utility for selecting each peer, and simultaneously each peer has a utility for being recruited by each recruiter. People in the network make choices that maximize their utility. I model the unobserved utilities as functions of observable nodal or dyadic covariates plus unobserved heterogeneities. I develop inference for this model within a Bayesian framework by approximating the posterior distribution of the preference coefficients via Markov chain Monte Carlo (MCMC). The algorithm is a form of constrained Metropolis-Hastings. My framework results in a tractable generative model for the RDS sampling mechanism. This greatly enhances both design-based and model-based inference.