new ways to measure movements via hyper network sampling
Rory, from the home office in South Bend, sends me links to new social movement research. A major question in social movement research is how you measure contentious politics. A lot of our sources are notoriously incomplete, such as media accounts. Kriage Bayerln, Peter Barwis, Bryant Crubaugh, and Cole Carnesecca use hypernetwork sampling (asking a random sample of people to list their social ties) to attack this issue. From Sociological Methods and Research:
The National Study of Protest Events (NSPE) employed hypernetwork sampling to generate the first-ever nationally representative sample of protest events. Nearly complete information about various event characteristics was collected from participants in 1,037 unique protests across the United States in 2010 to 2011. The first part of this article reviews extant methodologies in protest-event research and discusses how the NSPE overcomes their recognized limitations. Next, we detail how the NSPE was conducted and present descriptive statistics for a number of important event characteristics. The hypernetwork sample is then compared to newspaper reports of protests. As expected, we find many differences in the types of events these sources capture. At the same time, the overall number and magnitude of the differences are likely to be surprising. By contrast, little variation is observed in how protesters and journalists described features of the same events. NSPE data have many potential applications in the field of contentious politics and social movements, and several possibilities for future research are outlined.
Readers in social network analysis and organization studies will recognize the importance of this technique. As long as you can sample people, you can sample social ties and the adjust the sample for repetition. Peter Marsden used this technique to sample organizational networks. In movements research, my hybrids paper used the technique to sample orgs that were involved in movement mobilization. It’s great to see this technique expand to sample large samples of events.See Bayerlin’s research project website for more. Recommended.