Compare methods
Review your selected methods side by side; rows that differ are highlighted.
| Realist Evaluation× | Contribution Analysis× | |
|---|---|---|
| Field | Public Policy | Public Policy |
| Family | Process / pipeline | Process / pipeline |
| Year of origin≠ | 1997 | 2001 |
| Originator≠ | Ray Pawson & Nick Tilley | John Mayne |
| Type≠ | Theory-driven, generative evaluation approach | Theory-based approach to causal inference about contribution |
| Seminal source≠ | Pawson, R., & Tilley, N. (1997). Realistic Evaluation. London: SAGE Publications. ISBN: 9780761950097 | Mayne, J. (2012). Contribution analysis: Coming of age? Evaluation, 18(3), 270–280. DOI ↗ |
| Aliases≠ | Realistic Evaluation, Theory-Driven Realist Evaluation, CMO Configuration Analysis, Pawson-Tilley Evaluation | Mayne's Contribution Analysis, Contribution Story Analysis, Theory-Based Contribution Analysis |
| Related≠ | 4 | 3 |
| Summary≠ | 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. | 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. |
| ScholarGateDataset ↗ |
|
|