方法对比

	Most Significant Change for Development	Theory-Based Impact Evaluation
领域	Development Studies	Development Studies
方法族	Process / pipeline	Process / pipeline
起源年份	2005	2009
提出者	Rick Davies & Jess Dart	Carol Weiss; Howard White (3ie)
类型	Participatory, story-based monitoring and evaluation technique	Evaluation approach / framework
开创性文献	Davies, R., & Dart, J. (2005). The 'Most Significant Change' (MSC) Technique: A Guide to Its Use. CARE International, Oxfam, et al.	White, H. (2009). Theory-Based Impact Evaluation: Principles and Practice. Journal of Development Effectiveness, 1(3), 271–284.
别名	MSC technique, Story-based monitoring, Most significant change stories, Monitoring without indicators	Theory of Change Evaluation, Contribution Analysis, Theory-Driven Evaluation, Causal-Chain Impact Evaluation
相关	4	4
摘要	The Most Significant Change (MSC) technique is a participatory, story-based approach to monitoring and evaluating development programmes that dispenses with predefined indicators. Developed by Rick Davies and elaborated with Jess Dart in their widely used 2005 guide, it works by systematically collecting stories of significant change from those closest to a programme and then filtering and selecting the most significant of them through deliberative panels at successive levels of the organisational hierarchy. The result is a structured, dialogical account of what stakeholders themselves judge to be the most important outcomes of an intervention.	Theory-based impact evaluation evaluates a programme by first making explicit the theory of change — the causal chain of assumptions and mechanisms through which inputs are expected to produce outcomes and impacts — and then gathering evidence to test whether each link in that chain holds. Rather than treating the programme as a black box and estimating only the net effect, it asks not just whether a programme worked but why, for whom, and under what conditions. Articulated by Carol Weiss and brought into development practice by Howard White and 3ie, it complements, rather than competes with, counterfactual designs.