Survey research can generate knowledge that is central to the study of collective action, public opinion, and political participation. Unfortunately, many populations—from undocumented migrants to right-wing activists and oligarchs—are hidden, lack sampling frames, or are otherwise hard to survey. An approach to hard-to-survey populations commonly taken by researchers in other disciplines is largely missing from the toolbox of political science methods: respondent-driven sampling (RDS). By leveraging relations of trust, RDS accesses hard-to-survey populations; it also promotes representativeness, systematizes data collection, and, notably, supports population inference. In approximating probability sampling, RDS makes strong assumptions. Yet if strengthened by an integrative multimethod research design, it can shed light on otherwise concealed—and critical—political preferences and behaviors among many populations of interest. Through describing one of the first applications of RDS in political science, this article provides empirically grounded guidance via a study of activist refugees from Syria. Refugees are prototypical hard-to-survey populations, and mobilized ones are even more so; yet the study demonstrates that RDS can provide a systematic and representative account of a vulnerable population engaged in major political phenomena.
Respondent-Driven Sampling and Sparse Graph Convergence
