TY - JOUR
T1 - Hard-to-Survey Populations and Respondent-Driven Sampling
T2 - Expanding the Political Science Toolbox
AU - Khoury, Rana B.
N1 - Funding Information:
My Northwestern University community is my fortune. I owe special thanks to Jason Seawright, as well as Ana Arjona, Ethan C. Busby, Jordan Gans-Morse, Laura García Montoya, Sasha Klyachkina, Matthew Lacombe, Sean Lee, Sarah Moore, Wendy Pearlman, David Peyton, William Reno, and Bruce Spencer. I also received guidance from Jesse Driscoll, Lisa G. Johnston, Mara Revkin, and Alex Shakar. Generous anonymous reviewers provided feedback that greatly improved this article. I am indebted to my Syrian interviewees and survey participants, and to my research assistants and interlocutors who deserve recognition but whose confidenti-ality I shall maintain. This project was supported by the Council of American Overseas Research Centers and the Kellogg School of Management at Northwestern University.
Publisher Copyright:
© American Political Science Association 2019.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - 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.
AB - 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.
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U2 - 10.1017/S1537592719003864
DO - 10.1017/S1537592719003864
M3 - Review article
AN - SCOPUS:85076740078
SN - 1537-5927
VL - 18
SP - 509
EP - 526
JO - Perspectives on Politics
JF - Perspectives on Politics
IS - 2
ER -