Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Outcome Mapping× | Contribution Analysis× | |
|---|---|---|
| Область | Public Policy | Public Policy |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления | 2001 | 2001 |
| Автор метода≠ | Sarah Earl, Fred Carden & Terry Smutylo (IDRC) | John Mayne |
| Тип≠ | Actor-centred planning, monitoring and evaluation approach | Theory-based approach to causal inference about contribution |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | OM, IDRC Outcome Mapping, Behavioural Change Mapping | Mayne's Contribution Analysis, Contribution Story Analysis, Theory-Based Contribution Analysis |
| Связанные≠ | 4 | 3 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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