방법 비교

	Network Governance Analysis	Realist Evaluation
분야	Public Administration	Public Policy
계열	Process / pipeline	Process / pipeline
기원 연도	2008	1997
창시자	Keith G. Provan & Patrick Kenis	Ray Pawson & Nick Tilley
유형	Interorganizational network analysis framework	Theory-driven, generative evaluation approach
원전	Provan, K. G., & Kenis, P. (2008). Modes of Network Governance: Structure, Management, and Effectiveness. Journal of Public Administration Research and Theory, 18(2), 229–252.	Pawson, R., & Tilley, N. (1997). Realistic Evaluation. London: SAGE Publications. ISBN: 9780761950097
별칭	Governance Network Analysis, Public Network Governance Assessment, Collaborative Governance Network Analysis, Interorganizational Governance Network Analysis	Realistic Evaluation, Theory-Driven Realist Evaluation, CMO Configuration Analysis, Pawson-Tilley Evaluation
관련	4	4
요약	Network governance analysis studies how public problems are addressed not by single hierarchical agencies but by networks of interdependent organizations — government bodies, nonprofits, firms and community groups — coordinating to deliver services or make policy. It combines the relational tools of social network analysis with Keith Provan and Patrick Kenis's influential 2008 typology of network governance, which distinguishes shared (participant-governed) networks, lead-organization-governed networks, and network administrative organizations. By mapping the structure of ties, computing network metrics, classifying the governance mode and relating these to outcomes, the method explains how a collaborative network is held together and why it performs as it does.	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.