Find out more: Learning about evaluation with small cohorts: pilots to test the methodologies

—

Overview

As described by Mayne (2008, p. 1), ‘Contribution analysis explores attribution through assessing the contribution a programme is making to observed results.’

Four conditions are needed to infer causality in Contribution Analysis (Befani and Mayne 2014; Mayne 2008):

  • Plausibility: The programme is based on a reasoned Theory of Change.
  • Fidelity: The activities of the programme were implemented.
  • Verified Theory of Change: The Theory of Change is verified by evidence such that the evaluator is confident that the chain of expected results occurred.
  • Accounting for other influencing factors: Other factors influencing the programme were assessed and were either shown not to have made a significant contribution or, if they did, the relative contribution was recognised.

What is involved?

Mayne (2008) sets out six steps in Contribution Analysis.

Step 1: Set out the attribution problem to be addressed: It is important to determine the specific cause-effect question being addressed and the level of confidence required before exploring the type of contribution expected and assessing the plausibility of the expected contribution in relation to the size of the programme.

Step 2: Develop the Theory of Change and the risks to it: Contribution Analysis is based on a well-developed Theory of Change that specifies the results chain that links the programme to outcomes.

Step 3: Gather existing evidence on the Theory of Change: The evaluator should next gather evidence to assess the logic of the links in the Theory of Change. Evidence will cover programme results and activities as well as underlying assumptions and other influencing factors.

Step 4: Assemble and assess the contribution story and challenges to it: The contribution story can now be assembled and assessed critically. This will involve examining links in the results chain and assessing which of these are strong and which are weak, assessing the overall credibility of the contribution story and ascertaining whether stakeholders agree with the story.

Step 5: Seek out additional evidence: Based on the assessment of how robust the contribution story is, the evaluator should next identify the new data needed to address challenges to the credibility of the story. At this stage, it may be useful to update the Theory of Change or look at certain elements of the theory in more detail. If it is possible to verify or confirm the Theory of Change with empirical evidence, then it is reasonable to conclude that the intervention in question was a contributory cause for the outcome (Befani and Mayne 2014).

Step 6: Revise and strengthen the contribution story: Contribution Analysis works best as an iterative process and should, ideally, be seen as an ongoing process that incorporates new evidence as it emerges.

Download a Contribution Analysis case study here

Download a longer briefing on Contribution Analysis here

Useful resources

An interesting lecture featuring John Mayne talking about Contribution Analysis (19 minutes in) and starting with a worked example (3 mins 25 seconds in) can be found here.

Key reading

The originator of Contribution Analysis, John Mayne, has published several articles. The most commonly cited one, setting out the key elements of Contribution Analysis in a concise and accessible form, is:

Mayne, J. (2008) Contribution Analysis: An approach to exploring cause and effect. Brief 16, Institutional Learning and Change (ILAC) Initiative.

A few years later, Mayne took stock of developments in Contribution Analysis and wrote another, widely cited, article on the topic:

Mayne, J. (2012) ‘Contribution analysis: Coming of age?’ Evaluation, 18(3) 270–280. https://doi.org/10.1177/1356389012451663

More recently, he has again revisited Contribution Analysis:

Mayne, J. (2019) ‘Revisiting Contribution Analysis’, Canadian Journal of Program Evaluation, 34(2) 171–191.