Caught in the act of political participation: surveying political activists

In: SAGE research methods cases

Abstract

Traditional general population surveys are unable to provide in-depth data about political activists because those citizens who participate in forms of action such as youth parties and protests constitute rare populations. Even when political participation is measured, it is decontextualized, which means that the survey answers respondents give in relation to their political participation are likely to be ecologically invalid. In other words, they are not generalizable to the particular context of the real-life setting of political participation because the questions are asked outside of that context. Moreover, traditional surveys ask respondents whether they have participated in a range of political actions, but very few are able to capture the issue of a demonstration (e.g., a climate march or a right-wing demonstration) and youth-specific forms of political participation such as youth councils and political parties youth wings are not included in general population surveys. The latter are sometimes covered in youth-specific surveys, but these are rare. In this case study, we outline a method for systematically surveying hard-to-reach political activists in a contextualized way. We focus on how to use surveys to catch protesters and young activists in the act of political participation. We describe an original method developed to robustly survey street demonstrations and explain how we adapted it to survey young participants at political events. We will reflect on the adaptations that we made to the methodology to capture young participants at political events as well as their effectiveness. The central theme is to illustrate the possibility and value of "catching" people as they are doing political participation. This enables a more sophisticated analysis of political activists than analysis of existing secondary datasets because the sample sizes of people engaging in these relatively rare behaviors are significantly larger, and the data are much more nuanced.