Suchergebnisse

Development researchers face many challenges in producing robust and persuasive analyses, often within a short time-frame. This edited volume tackles these challenges head-on, using examples from other fields to provide practical guidance to research producers and users

An expansion in funding for 'basic' research has provided space for development researchers to reflect on their practice and on their ethical responsibility to do research that is 'accountable and of the highest quality' (ESRC Framework 2006). The growth in qualitative as well as quantitative data archiving, which is now a requirement of many funders, brings these issues to the fore. For secondary data to be usable there needs to be a robust methodological account reflecting on the challenges of data production and the implications of these for potential conclusions. The recent emphasis on evidence-based policy making by DFID means it is doubly important to ensure that quantitative and qualitative studies make full disclosure of their methods of data production and analysis, although there is little guidance provided in relation to this. This volume responds to these challenges, drawing on best practice from other fields, and provides a fresh perspective on perennial debates such as how to integrate qualitative and quantitative approaches and the relationship between data and theory.

In the large international projects where many qualitative researchers work, generating qualitative Big Data, data sharing represents the status quo. This is rarely acknowledged, even though the ethical implications are considerable and span both process and product. I argue that big-team qualitative researchers can strengthen claims to rigor in analysis (the product) by drawing on a growing body of knowledge about how to do credible secondary analysis. Since this necessitates a full account of how the research and the analysis are done (the process), I consider the structural disincentives for providing these. Debates around credibility and rigor are not new to qualitative research in international development, but they intensify when new actors such as program evaluators and quantitative researchers use qualitative methods on a large scale. In this context, I look at the utility of guidelines used by these actors to ensure the quality of qualitative research. I ask whether these offer pragmatic suggestions to improve its quality, recognizing the common and hierarchized separation between the generation and interpretation of data, or conversely, whether they set impossible standards and fail to recognize the differences between and respective strengths of qualitative and quantitative research.

This paper explores children's understandings of poverty, ill‐being and well‐being in Ethiopia using data collected through group exercises with children aged 5–6 and 11–13 participating in Young Lives, an international study of childhood poverty. In some respects the characteristics of poverty reported by children resemble those reported by adults participating in similar exercises. However, the children's addition of appearance and clothing, and their explanations of the reasoning behind the importance of these indicators of well‐being reflect growing inequalities in Ethiopia, where experiences of relative poverty and social exclusion are increasingly common. This evidence argues for broadening the focus of child poverty reduction to include the psychosocial costs of lacking the culturally specific resources required for full participation in society. The paper also illustrates ways in which poverty can be explored by asking about ill‐being and that children as young as five years are able to address these themes through well‐designed research methods.

The added value of mixed methods research in poverty and vulnerability is now widely established. Nevertheless, gaps and challenges remain. This volume shares experiences from research in developed and developing country contexts on how mixed methods approaches can make research more credible, usable and responsive to complexity