Asking sensitive questions in conservation using Randomised Response Techniques

Harriet Ibbett

doi:10.1016/j.biocon.2021.109191

2021-08-01

Harriet Ibbett *

Julia P. G. Jones

Freya A. V. St. John

DOI：10.1016/j.biocon.2021.109191

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摘要

En 中文

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.

Keyword:

Specialised questioning techniques

Indirect questioning

Non-compliance

Rule-breaking

Natural resource management

Sensitivity bias