Review article
Respondent-driven Sampling: A New Approach to the Sampling of Hidden Populations
Valerio Baćak
Abstract
Respondent-Driven Sampling (RDS) is part of chain-referral sampling methods based on social networks, and focuses on hidden and hard to reach populations such as injecting drug users, and men who have sex with men. These populations are exposed to an increased risk of HIV infection and therefore are the main target groups of prevention activities. Such activities, however, cannot be performed without the necessary information about the attitudes, behavior, and sociodemographic characteristics of these populations. In this paper, RDS is presented through a comparison with other predominant sampling methods for hard to reach and hidden populations, among which time-location sampling, targeted sampling, and snowball sampling are briefly discussed in order to clearly present which shortcomings of existing methods does RDS resolve. RDS is a probabilistic sampling method that provides a representative sample, which is a valuable achievement when it comes to hidden populations. Theoretical, methodological, and practical aspects of RDS are portrayed in this paper via basic concepts such as primary and secondary incentives, formative research, equilibrium, and homophily. In a review of some most recent studies, the empirical evaluation of RDS is examined both in regards to different sociocultural contexts where it was implemented, and in regard to different study populations and research topics that RDS dealt with.
Keywords
SAMPLING METHODS; HIDDEN POPULATIONS; RESPONDENT-DRIVEN SAMPLING; SOCIAL NETWORKS; PRIMARY AND SECONDARY INCENTIVES; HOMOPHILY
Hrčak ID:
13218
URI
Publication date:
31.12.2006.
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