পদ্ধতির তুলনা করুন

	Policy Implementation Analysis	Realist Evaluation
ক্ষেত্র	Public Administration	Public Policy
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1973	1997
প্রবর্তক	Jeffrey Pressman & Aaron Wildavsky	Ray Pawson & Nick Tilley
ধরন	Process-tracing policy analysis	Theory-driven, generative evaluation approach
মৌলিক উৎস	Pressman, J. L., & Wildavsky, A. (1973). Implementation: How Great Expectations in Washington Are Dashed in Oakland. Berkeley: University of California Press. ISBN: 9780520053311	Pawson, R., & Tilley, N. (1997). Realistic Evaluation. London: SAGE Publications. ISBN: 9780761950097
অপর নাম	Implementation Studies, Top-Down Implementation Analysis, Implementation Gap Analysis	Realistic Evaluation, Theory-Driven Realist Evaluation, CMO Configuration Analysis, Pawson-Tilley Evaluation
সম্পর্কিত	4	4
সারসংক্ষেপ	Policy implementation analysis studies what happens between the moment a policy is decided and the moment it reaches its intended effect, asking why outcomes so often fall short of stated objectives. The field was founded by Jeffrey Pressman and Aaron Wildavsky's 1973 study of a federal employment program in Oakland, which showed how a long chain of required agreements and clearances steadily eroded great expectations. The method traces the implementation chain — the actors, decision points and conditions through which a policy must pass — to locate where and why it succeeds or fails. Its central object is the implementation gap between policy as legislated and policy as delivered.	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.