NEW SURVEY QUESTIONS AND ESTIMATORS FOR NETWORK CLUSTERING WITH RESPONDENT-DRIVEN SAMPLING DATA

Abstract

Respondent-driven sampling (RDS) is a popular method for sampling hard-to-survey populations that leverages social network connections through peer recruitment. While RDS is most frequently applied to estimate the prevalence of infections and risk behaviors of interest to public health, such as HIV/AIDS or condom use, it is rarely used to draw inferences about the structural properties of social networks among such populations because it does not typically collect the necessary data. Drawing on recent advances in computer science, we introduce a set of data collection instruments and RDS estimators for network clustering, an important topological property that has been linked to a network's potential for diffusion of information, disease, and health behaviors. We use simulations to explore how these estimators, originally developed for random walk samples of computer networks, perform when applied to RDS samples with characteristics encountered in realistic field settings that depart from random walks. In particular, we explore the effects of multiple seeds, without replacement versus with replacement, branching chains, imperfect response rates, preferential recruitment, and misreporting of ties. We find that clustering coefficient estimators retain desirable properties in RDS samples. This paper takes an important step toward calculating network characteristics using nontraditional sampling methods, and it expands the potential of RDS to tell researchers more about hidden populations and the social factors driving disease prevalence.

Keywords: HIV/AIDS; clustering coefficient; estimation; hidden populations; respondent-driven sampling (RDS); sampling; small world model; social networks; transitivity; triad.

Figure 1.

Example network with hypothetical random walk sampling (RWS) and components needed to calculate local and global clustering coefficients for the whole network.

Figure 2.

Largest weakly connected component of Project 90 data set; nodes shaded by race (grey = white; black = nonwhite) and sized by degree. The network is displayed using the ForceAtlas2 algorithm, with no node overlap, in Gephi 0.9.

Figure 3.

Performance of Hardiman Katzir estimators by estimator and question format in RWS on the Project 90 data set. Note: These are nonstandard box plots that show the mean rather than the median as the central line; the thick dashed line indicates the population parameter.

Figure 4.

Performance of Hardiman Katzir estimators by estimator and question format in RWS and RDS scenarios on the Project 90 data set. Note: These are nonstandard box plots that show the mean rather than the median as the central line; the thick dashed line indicates the population parameter.