To assess a project’s outcomes credibly, start by naming who will use the findings and which decision they should inform. Then describe how the project is expected to lead to results, choose a small set of evaluation questions, match indicators and data sources to those questions, select a design strong enough for the causal claim you want to make, and report limitations alongside the results. The approach below draws on CDC’s Program Evaluation Framework (2024) and the OECD DAC evaluation criteria. Both present themselves as adaptable guides rather than universal formulas, so treat every step as something to tailor to your intervention, stakeholders, and intended decisions.
The six-step workflow
CDC’s CDC Program Evaluation Framework (dated August 20, 2024) lays out a practical sequence that is useful well beyond public health. It runs through six steps:
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- Assess the context in which the project operates.
- Describe the program or project in enough detail to test its logic.
- Focus the evaluation questions and design.
- Gather credible evidence.
- Generate and support conclusions.
- Act on the findings.
Three actions run through all six steps: engage stakeholders collaboratively, promote fair and just evaluation practices and outcomes, and learn from insights and use them. The framework is nonprescriptive. It is best read as a checklist of things to think through, not a fixed procedure, and the same caution applies to anything in this article.
The framework also sets five quality standards. In its own words: “High-quality evaluation should consider these five evaluation standards: Relevance and utility; Rigor; Independence and objectivity; Transparency; Ethics.” (Centers for Disease Control and Prevention, Program Evaluation Framework, 2024.) Those standards are a useful test for any evaluation plan: if a design cannot meet them, it should be changed before data collection starts.
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Start with users, decisions, and context
An evaluation that has no clear audience usually produces a long report that nobody acts on. Before choosing methods, write down three things:
- Who will use the results. Funders, project managers, partners, beneficiaries, and oversight bodies often need different answers.
- What decision or learning the evaluation should support. Examples include continuing or stopping an activity, changing delivery, reallocating budget, or planning a second phase.
- The context. Political, organizational, economic, and social conditions shape both what the project can do and what a fair assessment looks like.
Next, describe the project itself: the need it addresses, its inputs, its activities, its expected results, and the contextual factors that could affect those results. A logic model or roadmap is the most practical tool here. It connects activities to outputs and then to short-term, intermediate, and long-term outcomes. Its real value is making assumptions visible. If a training program is expected to change behavior, the logic model should state what must be true for that to happen, such as participants having time to apply new skills. Assumptions that are written down can be tested; assumptions left implicit tend to surface only when results disappoint.
Choose criteria and questions
The OECD’s evaluation criteria for development and similar interventions provide six complementary lenses. Each looks at a different attribute of an intervention:
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| Criterion | Question it helps answer |
|---|---|
| Relevance | Does the project respond to the needs it was designed for? |
| Coherence | Does it fit with other efforts in the same setting? |
| Effectiveness | Is the intervention achieving its objectives? |
| Efficiency | Are resources being used well to produce results? |
| Impact | Does the project produce broader changes, intended or not? |
| Sustainability | Do the benefits continue after support ends? |
These criteria should be selected and interpreted according to purpose, context, and stakeholder needs. They are not a method and not a checklist to run mechanically. A short pilot may need only relevance and effectiveness; a multi-year program may need all six. Note that the effectiveness question is the one most often reduced to “did we hit our targets?”, which is narrower than the criterion itself.
Whatever criteria you choose, limit the work to a manageable number of questions. Each question should be specific enough that a reader can tell what evidence would answer it.
Match the evaluation type to the question
CDC describes several types of evaluation, distinguished by purpose: formative, process or implementation, outcome, impact, and economic. No type is universally superior, so the choice follows the question. The main distinctions are:
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| Type | What it examines |
|---|---|
| Formative | Information to shape the project while it is being designed or adjusted. |
| Process or implementation | How implementation relates to the intended theory of change. |
| Outcome | The extent to which intended outcomes were achieved. |
| Impact | Whether outcomes can be attributed to the intervention through a comparison. |
| Economic | How program effects relate to program costs. |
A common mistake is to run an impact design when the real need is a process review, or to report outcomes as if they were impacts. Matching type to question prevents both.
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Good indicators measure what the expected result actually says. CDC’s guidance on gathering credible evidence recommends SMART indicators (Specific, Measurable, Achievable, Relevant, and Timebound) and emphasizes that measures may be quantitative or qualitative, depending on the question. Before collecting anything, check the following:
- Each indicator names a specific measure and a specific data source.
- The quantity and quality of data are sufficient for the conclusions you intend to draw.
- Existing instruments have been considered and can be adapted rather than rebuilt.
- Collection protocols will be applied consistently across sites, staff, and time periods.
- The data plan addresses security and confidentiality, particularly for personal or sensitive information.
Qualitative evidence such as interviews, observation, and document review often explains why a quantitative change did or did not occur. Treat it as part of the evidence base, not as an afterthought.
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Outcome results are not proof of causation
Perhaps the most important distinction in this area is between describing outcomes and claiming that the project caused them. An outcome evaluation measures the extent to which intended outcomes were achieved. On its own, it cannot attribute causality. An impact evaluation compares outcomes with and without the intervention and is designed to assess whether a causal relationship can be established. The OECD’s guidance on evaluating development co-operation makes the same point: the strength of a causal claim depends on the design behind it.
In practice, this means that when outcomes improve, the report should say what could also explain the change, such as a concurrent policy, a seasonal trend, or the people who chose to participate. Stating plausible alternative explanations and limitations is not a weakness in the report. It is what allows readers to judge how much weight the findings can bear.
Choose a design that can support the claim
When comparing possible designs, weigh the following together rather than in isolation:
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- The evaluation purpose and the specific questions.
- The context and the scale of the project.
- The quality of the evidence that is realistically available.
- The strength of causal claim required by the decision.
- Feasibility, timeline, and budget.
- The skills and resources available to the team.
- Ethical considerations, including whether a comparison group can be justified.
- How useful the results will be to the intended users.
CDC’s framework frames method choice as a balance among evaluation goals, scale, timeline, feasibility, and resources. A design that is stronger on paper but cannot be completed in time is less useful than a credible design that delivers findings while decisions are still open.
Turn evidence into conclusions and action
Analysis should interpret the evidence against the questions and criteria you set at the start. The write-up should explain what the evidence supports, where it is limited, and what remains uncertain. It should also cover intended and unintended results, and where relevant, differences across groups. A program that works well on average may fail a specific population, and that difference is often the most actionable finding.
CDC treats generating conclusions and acting on findings as separate steps. Preparing findings is not the same as getting them used. Facilitating use means identifying who needs each finding, in what form, and when, and linking it to a concrete decision: improving the program, allocating resources, meeting accountability requirements, or planning the next phase. Findings that arrive after the decision has been made have little value, so timing is part of quality.
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Pre-publication reporting checklist
- Does the report name the intended users and the decision each finding informs?
- Is each conclusion linked to a named question and a named source of evidence?
- Are outcome findings described separately from any causal claim?
- Are alternative explanations and data limitations stated in plain language?
- Are results broken down by relevant groups, including where results differ?
- Does each recommendation say what action it supports and who owns it?
Used this way, the framework does not guarantee a particular result, but it makes the evidence, its limits, and its implications visible enough for decision-makers to act on it.
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