A mixed methods approach for measuring topic sensitivity in conservation

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Abstract
Conservationists increasingly aim to understand human behaviour to inform intervention design. However, obtaining information from people about their behaviour can be challenging, particularly if the research topic is considered sensitive. Topic sensitivity may raise methodological, ethical, political and legal concerns which, if poorly addressed, can have significant impacts on research participants, the research process, data quality and the success of conservation outcomes that are informed by research findings. While considerable effort has been invested in developing techniques for reducing bias when collecting data on sensitive topics, less attention has been focused on identifying if, and why, a topic is sensitive. We use a mixed methods approach to explore how willing people are to discuss topics that could be considered sensitive (e.g. illegal wildlife hunting). Collecting data from people living near protected areas in Indonesia (n = 362) and Tanzania (n = 345), we developed and tested a psychometric scale to measure topic sensitivity at the respondent level and conducted group exercises (free‐lists and pile sorts) to gain a deeper understanding of peoples' willingness to discuss different topics. The perceived sensitivity of topics varied both within and between the two focal contexts, with more topics being perceived as sensitive in Tanzania than Indonesia. Participants' knowledge of rules, and their experiences of living alongside protected areas affected how sensitive they considered topics to be. Mixed methods approaches can provide holistic and nuanced understanding of topic sensitivity. However, recognising that in‐depth studies are not always feasible to implement, we demonstrate that methods, such as our Sensitivity Index, can easily be adapted for different contexts and deployed to rapidly obtain valuable insights on topic sensitivity, to help inform conservation research and practice. Read the free Plain Language Summary for this article on the Journal blog.
Funding Information
  • H2020 European Research Council (755956)

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