Comparer des méthodes

	Process Evaluation	Contribution Analysis
Domaine	Public Policy	Public Policy
Famille	Process / pipeline	Process / pipeline
Année d'origine	2015	2001
Auteur d'origine	Health-promotion & MRC evaluation tradition (Saunders et al.; Moore et al.)	John Mayne
Type	Implementation-focused program evaluation	Theory-based approach to causal inference about contribution
Source fondatrice	Moore, G. F., Audrey, S., Barker, M., Bond, L., Bonell, C., Hardeman, W., et al. (2015). Process evaluation of complex interventions: Medical Research Council guidance. BMJ, 350, h1258.	Mayne, J. (2012). Contribution analysis: Coming of age? Evaluation, 18(3), 270–280.
Alias	Implementation Evaluation, Implementation Fidelity Evaluation, Program Process Evaluation	Mayne's Contribution Analysis, Contribution Story Analysis, Theory-Based Contribution Analysis
Apparentées	3	3
Résumé	Process evaluation examines how a program or policy was actually implemented, rather than only whether it achieved its outcomes. It documents what was delivered, to whom, how much, how well and in what context, so that outcome findings can be interpreted correctly. By assessing implementation fidelity, dose, reach, and the mechanisms and contextual factors at work, process evaluation explains why an intervention succeeded or failed and distinguishes a flawed program theory from a sound theory that was poorly delivered. The UK Medical Research Council's 2015 guidance and earlier health-promotion frameworks consolidated it as a core component of evaluating complex interventions.	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.