How to ask sensitive questions in conservation: A review of specialized questioning techniques

Tools for social research are critical for developing an understanding of conservation problems and assessing the feasibility of conservation actions. Social surveys are an essential tool frequently applied in conservation to assess both people’s behaviour and to understand its drivers. However, little attention has been given to the weaknesses and strengths of different survey tools. When topics of conservation concern are illegal or otherwise sensitive, data collected using direct questions are likely to be affected by non-response and social desirability biases, reducing their validity. These sources of bias associated with using direct questions on sensitive topics have long been recognised in the social sciences but have been poorly considered in conservation and natural resource management. We reviewed specialized questioning techniques developed in a number of disciplines specifically for investigating sensitive topics. These methods ensure respondent anonymity, increase willingness to answer, and critically, make it impossible to directly link incriminating data to an individual. We describe each method and report their main characteristics, such as data requirements, possible data outputs, availability of evidence that they can be adapted for use in illiterate communities, and summarize their main advantages and disadvantages. Recommendations for their application in conservation are given. We suggest that the conservation toolbox should be expanded by incorporating specialized questioning techniques, developed specifically to increase response accuracy. By considering the limitations of each survey technique, we will ultimately contribute to more effective evaluations of conservation interventions and more robust policy decisions

Multiple sensitive estimation and optimal sample size allocation in the item sum technique

For surveys of sensitive issues in life sciences, statistical procedures can be used to reduce nonresponse and social desirability response bias. Both of these phenomena provoke nonsampling errors that are difficult to deal with and can seriously flaw the validity of the analyses. The item sum technique (IST) is a very recent indirect questioning method derived from the item count technique that seeks to procure more reliable responses on quantitative items than direct questioning while preserving respondents' anonymity. This article addresses two important questions concerning the IST: (i) its implementation when two or more sensitive variables are investigated and efficient estimates of their unknown populationmeans are required; (ii) the determination of the optimal sample size to achieve minimum variance estimates. These aspects are of great relevance for survey practitioners engaged in sensitive research and, to the best of our knowledge, were not studied so far. In this article, theoretical results for multiple estimation and optimal allocation are obtained under a generic sampling design and then particularized to simple random sampling and stratified sampling designs. Theoretical considerations are integrated with a number of simulation studies based on data from two real surveys and conducted to ascertain the efficiency gain derived from optimal allocation in different situations. One of the surveys concerns cannabis consumption among university students. Our findings highlight some methodological advances that can be obtained in life sciences IST surveys when optimal allocation is achieved.This work is partially supported by Ministerio de Economía y Competitividad (grant MTM2015-63609-R, Spain), Ministerio de Educación, Cultura y Deporte (grant FPU, Spain), and by the project PRIN-SURWEY (grant 2012F42NS8, Italy)

Valid estimates for repeated randomized response methods

<p>Surveys with sensitive characteristics (e.g. cheating in exams, fiscal evasion, social fraud, insurance fraud, discrimination, political views, financial situation) need special concepts, because normal direct questioning causes answer refusal and lies. One well-established concept is the randomized response (RR) approach. RRs protect the interviewees' privacy and facilitate their cooperation. Based on the RRs of many persons, inference is possible. A recently published article suggests two repeated RR methods. That is, each interviewee must give more than one answer. Repeated RRs are a good idea to improve the estimation efficiency of RR techniques. However, this recently published article contains serious mistakes and derives invalid estimates. For this reason, we correct these mistakes and develop valid estimates in the first part of our article. Subsequently, in the second part, we present generalized considerations that cover many more repeated RR schemes.</p