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A success metric supports an outcome claim only when it measures that outcome reliably and validly—or when there is strong evidence connecting an indirect signal to it. Activity, output, outcome, and causal impact are different things: a signal that shows work happened does not, by itself, establish that the intended result occurred or that a particular intervention caused it.
What does the success signal actually show?
Start by stating the intended outcome in plain language. Specify who or what should change, the direction of change, and the time horizon. Then ask what the observed signal records. For example, counts of tasks completed or people reached can show activity or output; they do not automatically show a change in the intended condition.
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Keep the concept, indicator, and measure distinct. The outcome is the change you want to understand. An indicator is observable evidence chosen to represent that concept. The measure is how that evidence is quantified or collected. The CDC recommends aligning indicators with evaluation questions and expected outcomes; its program evaluation guidance distinguishes activity, output, and outcome indicators.
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Is the measure reliable and valid?
Reliability asks whether a measure gives consistent results when circumstances have not changed. Validity asks whether it measures what it is intended to measure. A measure can be consistently recorded and still capture the wrong thing. GOV.UK describes reliability as a prerequisite to validity and usefulness, and advises identifying measures early in evaluation design in its guidance on outcome evaluation.
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- Reliability: Would the same method produce a similar result if the underlying circumstances were unchanged?
- Validity: Does the measure represent the intended outcome for this particular use?
Also consider sensitivity: would the measure register a meaningful change in the outcome, rather than only a large or obvious shift? And ask whether unrelated events could move the signal even if the outcome did not change.
When is a proxy useful—and what can it prove?
A proxy is an indirect measure used when a direct measure is unavailable, impractical, or too burdensome. It can be useful evidence, but its interpretation depends on the assumed link between the proxy and the outcome. State that link explicitly and identify what supports it. A proxy’s movement alone does not establish that the outcome occurred.
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The archived White House guidance on performance measurement challenges cautions against relying on a single measure when it may only proxy a complex objective. It recommends tying the proxy closely to the objective and demonstrating the relationship, for example through modeling. That page is archived; its measurement reasoning should not be read as current policy authority.
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How should you compare candidate measures?
There is no universal best measure. Compare plausible options against the decision you need to make and the context in which evidence will be collected.
- Directness: How closely does the measure capture the outcome rather than an activity or proxy?
- Reliability and validity: Is the measure consistent, and does it represent the intended concept?
- Sensitivity: Can it detect a meaningful change within the relevant period?
- Outside influences: What else could change the signal?
- Feasibility: What are the collection costs and burden on respondents or staff?
- Timing and frequency: When should evidence be collected, and how often is useful?
- Disaggregation: Does the decision require results to be examined separately for different groups or contexts?
More measures are not automatically better. OECD cautions that additional indicators add collection and reporting burden, so keep the set proportionate to the evaluation purpose. The ONS Evaluation Strategy connects outputs and outcomes through a theory of change, aligns indicators to them, and emphasizes collecting evidence at appropriate times.
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A practical way to verify the claim
- Define the outcome. Write the intended change, the population or subject it concerns, its direction, and the time horizon.
- Identify what the signal measures. Name the concept, indicator, and actual collection method separately. Do not label activity or output as an outcome.
- Decide whether the measure is direct. If it is a proxy, state the hypothesized connection and the evidence behind that connection.
- Check measurement quality. Assess reliability, validity, sensitivity to meaningful change, and exposure to unrelated influences.
- Seek corroboration where needed. If one signal is ambiguous or vulnerable to outside factors, compare it with other relevant evidence. The measures can remain separate; they do not have to be collapsed into one score.
- Match the wording of the conclusion to the evidence. Say a signal is “consistent with,” “suggests,” or “tracks” an outcome when that is all it establishes. Reserve claims that the outcome occurred—or that an intervention caused it—for evidence that supports those claims.
How to report what you know
A clear report names the outcome, identifies the measure, and states the limits of inference. Explain whether the evidence is direct or indirect, what could have influenced the signal, and whether the conclusion concerns outcome occurrence or causal attribution. This lets readers see exactly what the metric supports without treating a convenient indicator as proof of more than it measures.
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