SOC 6350 - [Network Sampling and Network Structure]

(CU-SBY)

Spring. Next offered: 2020-2021. 4 credits. Student option grading.

D. Heckathorn.

Network sampling methods provide means for drawing probability samples of hidden and hard-to-reach populations. These populations are difficult to sample using standard survey research methods because they lack a sampling frame, that is, an exhaustive list of population members from which the sample can be drawn and constructing a sampling frame is not feasible due to the closed nature of the populations networks or associated factors. Populations with these characteristics are important to studies of public health (e.g., drug users and commercial sex workers), public policy (e.g., immigrants and the homeless), and arts and culture (e.g., jazz musicians and aging artists). This course will survey the use of network-based approaches to sample populations and study the structure of social networks. The focus will range from initial work on biased network theory, through various approaches based on snowball sampling, the “random-walk” approach, adaptive sampling, and link-tracing designs, to a principal focus on respondent-driven sampling (RDS), including the analytics underlying that method, operational procedures, recent work extending the method, the potential for use of RDS to study the structure of very large social networks, and open areas in which further work is continuing and alternative formulations are emerging.