Contribution Analysis for Programme Results Evaluation Training Course

Course overview

Programme teams are often required to explain whether their intervention contributed to observed results when randomised trials, comparison groups or complete monitoring data are unavailable. Donors, boards and affected stakeholders still expect credible answers: what changed, how the programme plausibly influenced that change, which assumptions held, and which external factors mattered. This course equips monitoring, evaluation and programme professionals to build and test evidence-based contribution claims without overstating attribution.

Participants learn the full contribution analysis process, drawing on John Mayne's six-step approach and applying it to development and humanitarian programmes. They will reconstruct a theory of change, identify and prioritise causal assumptions, develop a contribution story, map rival explanations, create an evidence plan, assess the strength of evidence, and revise claims in response to findings. The course also addresses contribution analysis in complex settings, including adaptive programmes, multi-partner initiatives, policy influence work and interventions affected by conflict, climate shocks or political change.

Teaching combines instructor-led method demonstrations with a running programme-results case, small-group evidence reviews and structured critique sessions. Participants work with practical tools including causal-link tables, contribution claim matrices, evidence rubrics and alternative-explanation logs. By the end of the week, each participant leaves with a draft contribution analysis protocol and contribution story for a live or realistic programme, including priority evidence questions, data sources, stakeholder roles and a defensible reporting structure.

The course is designed for professionals who already work with results frameworks, theories of change or evaluation evidence and need a stronger approach to answering causal questions in real operating conditions. It is particularly valuable where programmes need credible learning and accountability findings but cannot support an experimental or quasi-experimental impact evaluation.

Course objectives

By the end of this course, participants will be able to:

• Construct a testable contribution story from a programme theory of change and results framework
• Define causal assumptions and intermediate results for each critical link in a results pathway
• Develop a contribution claim matrix linking claims, evidence requirements, sources and assessment criteria
• Identify and assess rival explanations, including contextual, policy, partner and external actor influences
• Design an evidence-gathering plan using monitoring data, document review, interviews and outcome evidence
• Apply evidence-strength rubrics to judge the plausibility of programme contribution claims
• Facilitate stakeholder validation of a contribution story without treating consensus as proof
• Produce a contribution analysis protocol and results narrative suitable for an evaluation report

Benefits of attending

Target competencies

Who should attend

• Monitoring and Evaluation Managers — who must commission or lead credible programme-results evaluations
• Evaluation Specialists — who need a theory-based method for assessing contribution where attribution designs are impractical
• Programme Managers — who must explain how delivery choices and partnerships influenced intended results
• Humanitarian MEAL Coordinators — who need to assess contribution amid volatile contexts and incomplete data
• Donor Programme Officers — who review evidence claims and require defensible reporting from grantees
• Research and Learning Advisers — who translate monitoring, evaluation and contextual evidence into programme adaptation decisions

Requirements and prerequisites

Participants should have practical familiarity with a logical framework, theory of change or results framework, including outputs, outcomes, assumptions and indicators. Experience reviewing monitoring data, evaluation reports or programme documentation is strongly recommended, as exercises require participants to judge evidence quality and causal reasoning. Participants should be comfortable using spreadsheets for simple evidence matrices and should bring a laptop if attending online or if they wish to develop their own programme case during classroom sessions. Statistical modelling, experimental design expertise, NVivo experience and advanced qualitative research training are not required.

Training methodology

The course uses short instructor-led inputs to introduce each stage of contribution analysis, followed by guided application to a development or humanitarian programme case. Participants work in small groups to reconstruct causal pathways, interrogate assumptions, review mixed evidence and test alternative explanations. Facilitated peer critique mirrors an evaluation quality-assurance process, requiring participants to defend the strength and limits of their claims. Daily exercises build toward an individual end-of-course application plan, with instructor feedback on each participant's contribution analysis protocol and evidence strategy.

Course outline

Day 1: Framing contribution questions and causal pathways

• Attribution, contribution and causal inference in programme evaluation
• John Mayne's six-step contribution analysis process
• Defining evaluative questions for programme results claims
• Reconstructing theories of change from logframes and programme documents
• Distinguishing outputs, outcomes, impacts and causal mechanisms
• Identifying critical causal links and boundary conditions
• Writing clear, testable contribution claims

Workshop: Participants reconstruct a theory of change for a multi-partner programme and draft three testable contribution claims.

Day 2: Testing assumptions and planning evidence

• Causal assumptions versus implementation assumptions
• Assumption surfacing techniques for programme teams
• Contribution claim matrices and causal-link tables
• Evidence requirements for each causal link
• Selecting monitoring, qualitative, administrative and document evidence
• Developing evaluation questions and evidence-gathering priorities
• Assessing data quality, credibility and coverage limitations

Workshop: Participants create a contribution claim matrix that specifies assumptions, evidence sources, data limitations and priority questions.

Day 3: Examining alternative explanations

• Rival explanations and the logic of causal challenge
• Contextual factors, policy changes and external actor influence
• Contribution analysis in conflict-affected and rapidly changing settings
• Using process tracing tests within contribution analysis
• Stakeholder interviews for causal mechanism evidence
• Triangulating monitoring data, documents and participant accounts
• Alternative-explanation logs and disconfirming evidence

Workshop: Teams investigate competing explanations for an observed outcome and produce an alternative-explanation assessment log.

Day 4: Judging evidence and strengthening the contribution story

• Evidence-strength rubrics for contribution claims
• Assessing plausibility, coherence, consistency and completeness
• Handling contradictory evidence and unresolved uncertainty
• Revision of the contribution story after evidence review
• Stakeholder validation methods and their evidential limits
• Equity, gender and inclusion considerations in causal narratives
• Using contribution findings for adaptive management decisions

Workshop: Participants score a case evidence pack against an evidence-strength rubric and revise the programme contribution story.

Day 5: Reporting and applying contribution analysis

• Contribution analysis protocol structure and evaluation work planning
• Reporting causal claims with calibrated language
• Visualising causal pathways, evidence and uncertainty
• Writing findings, conclusions and recommendations from contribution evidence
• OECD DAC criteria and contribution analysis reporting alignment
• Commissioning contribution analysis through evaluation terms of reference
• Embedding contribution analysis in routine monitoring and learning cycles

Workshop: Participants complete and present a draft contribution analysis protocol, including a contribution story, evidence plan and reporting outline.

Tools & standards covered

Microsoft Excel, NVivo, OECD DAC Evaluation Criteria, DCED Standard for Results Measurement

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions