Title Web-respondent-driven sampling
Workshop Workshop 2014
Year 2014

Purpose of the study: This study investigates the effectiveness and efficiency of respondent driven sampling (RDS) as a webbased sampling method. Attention is devoted to bias related to survey issues. A RDS recruitment experiment is carried out, using non-material incentives.
Design/methodology/approach: RDS is a network-based technique for estimating traits in hard-to-reach populations (Heckathorn, 1997, 2002, 2007). It is similar to snowball sampling: initial seed respondents recruit additional respondents from their social network and this recruiting process repeats iteratively. Unlike snowball sampling, RDS allows to derive estimates that are not biased and have known level of precision. This is possible making reference to Markov chain theory. Findings: First, a critical evaluation of different RDS versions is presented, with specific reference to the web context and special attention to conditions ensuring the possibility of probabilistic reasoning. The overview shows that very few attempts have been made in the literature to conduct RDS on the Internet. Next, we carry out an experiment, which provides further insights on the performance of web-RDS. Methodological challenges, like clustering among respondents and their contacts are set under observation. Furthermore, an issue that is considered is whether/how RDS recruitment is affected by the topic of the survey being administered (sensitive versus non-sensitive issues). Originality/value: RDS is typically implemented face to face and targeting hard-to-reach populations. Few attempts have been made to conduct RDS on the Internet (Schonlau, Weidmer, and Kapteyn, 2014; Wejnert and Heckathorn, 2008).
The value of this work is twofold. First, an RDS experiment is conducted on the web, providing some insights on the efficacy of this technique for recruiting on the web. Second, the experiment targets the general population.
Research limitations/implications: The research is based on an experiment with a limited sample size and on a specific topic. In order to generalize results, it is necessary to carry out more extensive experiments also on different topics. Practical implications: The research allows to identify some critical elements that need to be taken into account when applying RDS for recruiting the general population over the Internet.