方法对比

	Realist Evaluation	现实主义综合
领域	Public Policy	证据综合
方法族	Process / pipeline	Process / pipeline
起源年份	1997	2005
提出者	Ray Pawson & Nick Tilley	Ray Pawson (2005)
类型	Theory-driven, generative evaluation approach	Framework
开创性文献	Pawson, R., & Tilley, N. (1997). Realistic Evaluation. London: SAGE Publications. ISBN: 9780761950097	Pawson, R., Greenhalgh, T., Harvey, G., & Walshe, K. (2005). Realist review—a new method of systematic review designed for complex policy and programme evaluation. Journal of Health Services Research & Policy, 10(S1), 21–35.
别名	Realistic Evaluation, Theory-Driven Realist Evaluation, CMO Configuration Analysis, Pawson-Tilley Evaluation	Realist Review, CMO Configuration, Mechanism-Based Synthesis
相关	4	1
摘要	Realist evaluation is a theory-driven approach to evaluating programs and policies that asks not simply 'does it work?' but 'what works, for whom, in what circumstances, and why?'. Developed by Ray Pawson and Nick Tilley in their 1997 book Realistic Evaluation, it treats interventions as theories incarnate: programs offer resources or opportunities that trigger underlying mechanisms of reasoning and response in participants, and those mechanisms only fire in particular contexts. The unit of analysis is the Context-Mechanism-Outcome (CMO) configuration, and the goal is to build and refine middle-range theory that explains differential outcomes across settings.	Realist synthesis is a theory-driven, interpretive method for evidence synthesis developed by Ray Pawson (2005) that focuses on understanding HOW and WHY interventions work, rather than WHETHER they work. Grounded in realist philosophy, realist synthesis examines Context-Mechanism-Outcome (CMO) configurations: how specific contextual conditions activate mechanisms that produce outcomes. Unlike traditional systematic reviews, which typically answer 'Does intervention X reduce outcome Y?', realist synthesis asks 'Under what conditions, through what mechanisms, for which populations does X work?' This approach is particularly valuable for complex interventions (policies, programs, multi-component treatments) where effectiveness varies dramatically across contexts, and for understanding why interventions succeed in some settings but fail in others.