Using Respondent-Driven Sampling to Study Self-Employed Nigerians in New York City: Applicability for Immigrant Populations

Leila Rodriguez, Pennsylvania State University

Respondent-driven sampling (RDS) is a novel sampling strategy useful for studying hidden populations. These populations are difficult to access because they conduct illicit activities, they are not easily distinguishable from the larger population, or because a sampling frame is unavailable and costly to create. RDS is conducted similarly to chain-referral sampling techniques, but under certain conditions, RDS allows generalization of proportions from the sample to the population. That is, RDS converts snowball sampling techniques into probability sampling ones. RDS has been successfully used to study drug users and jazz musicians. This paper examines the challenge of applying RDS to study entrepreneurship among Nigerian immigrants in New York City. It discusses the applicability and feasibility of using RDS to sample immigrant populations. It also presents some preliminary results for group proportions including ethnicity, residence, and self-employment status. Finally, it discusses information about their social networks discernible from the sampling process alone.