Douglas D. Heckathorn

Respondent-Driven Sampling: A New Approach to the Study of Hidden Populations

A population is “hidden” when no sampling frame exists and public acknowledgment of membership in the population is potentially threatening. Accessing such populations is difficult because standard probability sampling methods produce low response rates and responses that lack candor. Existing procedures for sampling these populations, including snowball and other chain-referral samples, the key-informant approach, and targeted sampling, introduce well-documented biases into their samples. This paper introduces a new variant of chain-referral sampling, respondent-driven sampling, that employs a dual system of structured incentives to overcome some of the deficiencies of such samples. A theoretic analysis, drawing on both Markov-chain theory and the theory of biased networks, shows that this procedure can reduce the biases generally associated with chain-referral methods. The analysis includes a proof showing that even though sampling begins with an arbitrarily chosen set of initial subjects, as do most chain-referral samples, the composition of the ultimate sample is wholly independent of those initial subjects. The analysis also includes a theoretic specification of the conditions under which the procedure yields unbiased samples. Empirical results, based on surveys of 277 active drug injectors in Connecticut, support these conclusions. Finally, the conclusion discusses how respondent- driven sampling can improve both network sampling and ethnographic 44 investigation.

Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling

Standard statistical methods often provide no way to make accurate estimates about the characteristics of hidden populations such as injection drug users, the homeless, and artists. In this paper, we further develop a sampling and estimation technique called respondent-driven sampling, which allows researchers to make asymptotically unbiased estimates about these hidden populations. The sample is selected with a snowball-type design that can be done more cheaply, quickly, and easily than other methods currently in use. Further, we can show that under certain specified (and quite general) conditions, our estimates for the percentage of the population with a specific trait are asymptotically unbiased. We further show that these estimates are asymptotically unbiased no matter how the seeds are selected. We conclude with a comparison of respondent-driven samples of jazz musicians in New York and San Francisco, with corresponding institutional samples of jazz musicians from these cities. The results show that some standard methods for studying hidden populations can produce misleading results.

6. Extensions of Respondent-Driven Sampling: Analyzing Continuous Variables and Controlling for Differential Recruitment:

Respondent-driven sampling (RDS) is a network-based method for sampling hidden and hard-to-reach populations that has been shown to produce asymptotically unbiased population estimates when its assumptions are satisfied. This includes resolving a major concern regarding bias in chain-referral samples—that is, producing a population estimate that is independent of the seeds (initial subjects) with which sampling began. However, RDS estimates are limited to nominal variables, and one of the assumptions required for the proof of lack of bias is the absence of differential recruitment. One aim of this paper is to analyze the role of differential recruitment, quantify the bias it produces, and propose a new estimator that controls for it. The second aim is to extend RDS so that it can be employed to analyze continuous variables in a manner that controls for differential recruitment. The third aim is to describe means for carrying out multivariate analyses using RDS data. The analyses employ data from an RDS sample of 264 jazz musicians in the greater New York metropolitan area, taken in 2002.

Extensions of Respondent-Driven Sampling: A New Approach to the Study of Injection Drug Users Aged 18-25

Researchers generally use nonprobability methods such as chain-referral sampling to study populations for which no sampling frame exists. Respondent-driven sampling is a new form of chain-referral sampling that was designed to reduce several sources of bias associated with this method, including those from the choice of initial participants, volunteerism, and masking. This study expands this method by introducing “steering incentives,” supplemental rewards for referral of members of a specific group, injection drug users (IDUs) aged 18–25. The results are based on an interrupted time series analysis in which 196 IDUs from Meriden, CT, were interviewed before introduction of the steering incentives, and another 190 were interviewed afterwards. The steering incentives increased the percentage of younger IDUs sampled by 70%. We compared recruitment patterns with institutional data and self-reported personal networks to determine representativeness and whether volunteerism or masking were present. The results indicated that steering incentives helped to increase recruitment of younger IDUs, that the sample was representative, and that both volunteerism and masking were modest.

THE DYNAMICS AND DILEMMAS OF COLLECTIVE ACTION

Theoretical accounts of participation in collective action have become more divergent. Some analysts employ the Prisoners Dilemma paradigm, other analysts suggest that different social dilemmas underlie collective action, and still others deny that social dilemmas play any significant role in collective action. I propose a theoretically exhaustive inventory of the dilemmas arising in collective action systems and show thatfive games, including the Prisoners Dilemma, can underlie collective action. To analyze action within each game I use a dynamic selectionist model based on three modes of organization-voluntary cooperation, strategic interaction, and selective incentives. Social dilemmas exist in four of the five games, and conflicting accounts of collective action have focused on different games and modes of organization. As collective action proceeds from initiation to rapid expansion to stability, its game type varies in a way that can be precisely characterized as movement through a two-dimensional game-space. Finally, I distinguish between two ways of promoting collective action: One way focuses on resolving the dilemma within a particular game; the other focuses on changing the game so the dilemma is more easily resolved or eliminated.

Collective Sanctions and Compliance Norms: A Formal Theory of Group-Mediated Social Control

The link between external sanctions and intragroup normative control is examined to distinguish the conditions under which the two control systems augment or weaken one another. I construct a dynamic rational choice model that incorporates essential features of the sanction/norm link. The analysis suggests that much social control that appears to result from the sanctions of individuals derives instead from a form of group-mediated control termed compliance norms. In other contexts, intragroup control acts in opposition to external sanctions, resulting in aform of control termed opposition norms. According to the formal analysis, group responses to sanctions depend on the strength of sanctions, monitoring capacities, and the efficacy and cost of intragroup control.

From Networks to Populations: The Development and Application of Respondent-Driven Sampling Among IDUs and Latino Gay Men

One of the challenges in studying HIV-risk behaviors among gay men is gathering information from a non-biased sample, as traditional probability sampling methods cannot be applied in gay populations. Respondent-Driven Sampling (RDS) has been proposed as a reliable and bias-free method to recruit “hidden” populations, such as gay men. The aim of this study is to assess the feasibility and effectiveness of RDS to sample Latino gay men and transgender persons. This was carried out when we used RDS to recruit participants into a study that investigated community involvement on HIV/AIDS sexual risk behaviors among Latino gay and bisexual men, and transgender (male-to-female) persons in Chicago and San Francisco. The population coverage of RDS was then compared to simulated time-location sampling (TLS). Recruitment differences were observed across cities, but the samples were comparable. RDS showed broader population coverage than TLS, especially among individuals at high risk for HIV.

Collective Action and the Second-Order Free-Rider Problem

This article examines the relationship between the first- and second-order free-rider problems in collective sanction systems, with special emphasis on the relative robustness of cooperation in the first and second levels. The results indicate that second-order cooperation exhibits surprising robustness relative to first-order cooperation. The implication is that hypocrisy, though universally maligned, may play a crucial transitional role both in the emergence of collective action and in the continuity of collective action under adverse circumstances.

COMMENT: SNOWBALL VERSUS RESPONDENT‐DRIVEN SAMPLING

Leo Goodman (2011) provided a useful service with his clarification of the differences among snowball sampling as originally introduced by Coleman (1958–1959) and Goodman (1961) as a means for studying the structure of social networks; snowball sampling as a convenience method for studying hard-to-reach populations (Biernacki and Waldorf 1981); and respondent-driven sampling (RDS), a sampling method with good estimability for studying hard-to-reach populations (Heckathorn 1997, 2002, 2007; Salganik and Heckathorn 2004; Volz and Heckathorn 2008). This comment offers a clarification of a related set of issues. One is confusion between the latter form of snowball sampling, and RDS. A second is confusion resulting from multiple forms of the RDS estimator that derives from the incremental manner in which the method was developed. This comment summarizes the development of the method, distinguishing among seven forms of the estimator.

Implementation and Analysis of Respondent Driven Sampling: Lessons Learned from the Field

Those who engage in illegal or stigmatized behaviors, which put them at risk of HIV infection, are largely concentrated in urban centers. Owing to their illegal and/or stigmatized behaviors, they are difficult to reach with public health surveillance and prevention programs.1 These populations include illicit drug users, sex workers and men who have sex with men. Development and implementation of adequate prevention services targeting hidden populations requires data on risk behaviors and disease prevalence from non-biased samples. In the last two decades, a number of sampling methods have been used to collect risk behavior and disease prevalence data from highly at-risk populations and to direct survey participants to prevention services. These include venue-based time–space sampling, targeted sampling, and snowball sampling.