Conservation researchers are aware of the need to work with social sciences to manage human-wildlife interactions for better conservation outcomes, but extending natural science research approaches to a social science domain can compromise data quality and validity. As part of the interdisciplinary exchange between the natural and social sciences, this review contrasts structured questionnaire-based surveys with qualitative approaches to collecting social data, and clarifies contexts in which particular qualitative methods might be more effective when investigating human behaviour in conservation research. Although welldesigned questionnaire-based surveys may be useful for population-level generalizations, complementary use of qualitative approaches can significantly enhance understanding of research context, underpinning formulation of representative sampling frames and pertinent research questions, and permitting greater accuracy in interpretation and analysis of data. Good qualitative data are necessary to an accurate understanding of categories, processes, relationships and perceptions, particularly critical in a cross-cultural context, significantly strengthening the internal validity of subsequent structured work. Furthermore, stand-alone qualitative research can also constitute a valuable and valid approach to matters critical to conservation research and to designing successful conservation initiatives, which cannot be effectively researched in any other way.
We analyse the use of the concept of household in sample surveys, with evidence drawn from a review of survey definitions, a series of in-depth interviews with data producers and users, and a systematic study of recent literature. We consider the place of the concept within the discipline of demography, and demonstrate how its definition and use interact with cultural values and core concepts integral to the discipline. Focusing on Tanzania as a case study, we examine the diversity of factors that influence the construction of household-level data from cross-sectional household surveys. Throughout the survey process, contrasting interpretations of the meaning of household and different motivations for using specific definitions of the term interact. This generates data and outputs with potential for undercounting, bias, and misrepresentations, with adverse effects on the quality of data used for monitoring development indicators. Some ways of improving data collection on households are proposed.
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