Hard-to-Survey Populations and Respondent-Driven Sampling: Expanding the Political Science Toolbox

Abstract

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.

Explaining the distinction between religious and political activism in Islamism: evidence from the Tunisian case

“…Trusted connections were crucial in contacting current and former members of the Ennahda organization, who remain guarded in a polarized and repressive context. Our approach therefore relied on participants' networks and semi-structured interviews, but it was different from research designed to administer survey questions to hard-to-reach populations (see, e.g., Khoury, 2020). Interviews were contextualized against party documents, Islamist writings, news reports, and memoirs.…”

How High-Frequency Users Embraced Cannabis Regulation in Uruguay

“…A hard-to-reach population refers both to those without a sampling frame and those who —due to some characteristic or behavior— resist public identification (Boidi, Queirolo, & Cruz, 2016; Heckathorn, 1997). Examples of applications of the RDS technique can be found in a diversity of populations, such as HIV patients (Carballo-Diéguez et al, 2011; Johnston et al, 2016), men who have sex with men (Inghels et al, 2021), or refugee activists (Khoury, 2020), among others. In our case, we consider cannabis high-frequency users as a hard-to-reach population.…”

(Dis)courtesy Bias: “Methodological Cognates,” Data Validity, and Ethics in Violence-Adjacent Research

“…Researchers may encounter bureaucratic hurdles, government intimidation, or social unrest that disrupts even well-planned research visits (Clark, 2006; Thomson, 2009, 2010). Populations may be hidden or otherwise hard to identify and reach (Khoury, 2020). Respondents’ political biases, whether with reference to US or European government sponsorship of research or to local enumerator ethnicity, may influence participants’ willingness to join a survey or remain part of it (Adida et al, 2016; Corstange, 2014, 2015).…”

“…Scholars may approach these challenges from any number of angles, ranging from emphasizing the importance of trust-based, credible, and/or working local relationships (Al-Faham, 2021, p. 5; Fujii, 2017; Thomson, 2010) to relying on sub-national comparisons (Giraudy et al, 2019; Snyder, 2001), employing sophisticated quantitative analysis (Hoover Green & Ball, 2019; Price et al, 2014), or practicing data triangulation, especially via mixed-methods or multi-epistemological research (Davenport & Ball, 2002; Driscoll, 2021; Khoury, 2020; Thachil, 2014, 2017; Thaler, 2017, 2019). Yet, the difficulties associated with conducting research in these locales are often understood as uniform, intrinsic characteristics that influence researcher-participant relations in predictable, blanket, and consistent ways across populations of interest.…”