विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| Outcome Mapping× | Contribution Analysis× | |
|---|---|---|
| क्षेत्र | Public Policy | Public Policy |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष | 2001 | 2001 |
| प्रवर्तक≠ | Sarah Earl, Fred Carden & Terry Smutylo (IDRC) | John Mayne |
| प्रकार≠ | Actor-centred planning, monitoring and evaluation approach | Theory-based approach to causal inference about contribution |
| मौलिक स्रोत≠ | Earl, S., Carden, F., & Smutylo, T. (2001). Outcome Mapping: Building Learning and Reflection into Development Programs. Ottawa: International Development Research Centre (IDRC). ISBN: 9780889369597 | Mayne, J. (2012). Contribution analysis: Coming of age? Evaluation, 18(3), 270–280. DOI ↗ |
| उपनाम | OM, IDRC Outcome Mapping, Behavioural Change Mapping | Mayne's Contribution Analysis, Contribution Story Analysis, Theory-Based Contribution Analysis |
| संबंधित≠ | 4 | 3 |
| सारांश≠ | Outcome Mapping is a planning, monitoring and evaluation methodology developed by the International Development Research Centre (IDRC) and set out by Sarah Earl, Fred Carden and Terry Smutylo in 2001. It redefines results as changes in the behaviour, relationships, activities and actions of the people and organisations a program works with directly — its 'boundary partners' — rather than as downstream development impacts. By focusing on the behavioural changes a program can plausibly influence, Outcome Mapping addresses the attribution problem head-on and shifts evaluation toward learning and contribution. | Contribution analysis is a theory-based evaluation approach that addresses the attribution problem — establishing whether and how an intervention made a difference — without relying on an experimental counterfactual. Developed by John Mayne from 2001 onward, it works by articulating the program's theory of change, gathering evidence along that chain, and then assembling a 'contribution story' that is progressively stress-tested against rival explanations. The aim is not statistical attribution but a credible, evidence-based conclusion that the program plausibly contributed to observed results, in the face of other influencing factors. |
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