Mixed Methods Research in Poverty and Vulnerability
Mixed Methods Research in Poverty and Vulnerability
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贫困和脆弱性的混合方法研究
DOI：
10.1057/9781137452511_6
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Copestake J
中科院分区：
文献类型：
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作者：
Copestake J
本章报告了定性影响协议（称为 QUIP）的试点测试，该协议旨在根据农村生计干预措施预期受益人的证词提供可信、及时且具有成本效益的影响证据，而无需对照组。 QUIP 旨在解决国际发展机构如何评估其工作影响这一长期存在的问题，特别是寻求帮助小农进行与市场一体化和适应气候变化相关的复杂农业和农村生计转型的非政府组织所面临的挑战。计划影响的证据对于组织学习和通过改进外部问责制建立合法性都可能有用。尽管人们担心这种方法是不民主的（Eyben，2013）并且会助长 Natsios（2010）所说的“强迫性测量障碍”，但基于结果和绩效管理文化在发展实践中看似势不可挡的兴起（Gulrajani，2010；Ramalingam，2013）强化了其重要性。尽管经常用技术术语来表述，但如何现实、可信地评估发展干预措施的影响这一问题一直是这些争论的战场之一（Camfield 和 Duvendack，2014）。一个核心方法论问题是归因：或者如何将特定结果可靠地与不同背景下的特定项目、干预措施或机制建立因果联系。主流方法将影响定义为特定人群在特定干预或“治疗”(X) 后的结果指标 (Y1) 值与同一人群在以下情况下的值之间的差异：
This chapter reports on pilot testing of a qualitative impact protocol–referred to as the QUIP–that aims to provide credible, timely and costeffective evidence of impact based on the testimonies of intended beneficiaries of rural livelihood interventions without the need for a control group. The QUIP aims to address the perennial question of how international development agencies evaluate the impact of their work, with particular reference to the challenges faced by NGOs seeking to assist smallholder farmers with often complex agricultural and rural livelihood transformations associated with market integration and adaptation to climate change. Evidence of programme impact is potentially useful both for organisational learning and for building legitimacy through improved external accountability. Its importance has been reinforced by the seemingly inexorable rise of results-based and performance management culture in development practice (Gulrajani, 2010; Ramalingam, 2013) notwithstanding concern that this approach is undemocratic (Eyben, 2013) and can encourage what Natsios (2010) refers to as ‘obsessive measurement disorder’. While often framed in technical terms, the issue of how the impact of development interventions can realistically and credibly be evaluated has been one battleground for these debates (Camfield and Duvendack, 2014).A central methodological issue is attribution: or how particular outcomes can reliably be causally linked to specific projects, interventions or mechanisms in different contexts. The dominant approach defines impact as the difference in the value of an outcome indicator (Y1) for a given population after a particular intervention or ‘treatment’(X) compared to what the value would have been for the same population if