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Asking sensitive questions in conservation using Randomised Response Techniques

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posted on 09.06.2021, 10:31 by Harriet IbbettHarriet Ibbett, Julia P.G. Jones, Freya St JohnFreya St John
  1. Conservation increasingly seeks knowledge of human behaviour. However, securing reliable data can be challenging, particularly if the behaviour is illegal or otherwise sensitive. Specialised questioning methods such as Randomised Response Techniques (RRTs) are increasingly used in conservation to provide greater anonymity, increase response rates, and reduce bias. A rich RRT literature exists, but successfully navigating it can be challenging. To help conservationists access this literature, we summarise the various RRT designs available and conduct a systematic review of empirical applications of RRTs within (n=32), and beyond conservation (n=66). Our results show increased application of RRTs in conservation since 2000. We compare the performance of RRTs against known prevalence of the sensitive behaviour and relative to other questioning techniques to assess how successful RRTs are at reducing bias (indicated by securing higher estimates). Findings suggest that RRT applications in conservation were less likely than those in other disciplines to provide prevalence estimates equal to, or higher than those derived from direct questions. Across all disciplines, we found reports of non-compliance with RRT instructions were common, but rarely accounted for in study design or analysis. For the first time, we provide conservationists considering RRTs with evidence on what works, and provide guidance on how to develop robust designs suitable for conservation research contexts. We highlight when alternate methods should be used, how to increase design efficiency and improve compliance with RRT instructions. We conclude RRTs are a useful tool, but their performance depends on careful design and implementation.


    This dataset contains all the information extracted from each article as part of the systematic review. The spreadsheet contains two datasets, and two code sheets. One dataset reflects data extracted at the article level, the other reflects data extracted for each study included in the articles (e.g. some articles used RRT in two separate studies, or used 1+ RRT design).

Funding

European Research Council (ERC) Horizon 2020 Grant Agreement No. 755956

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