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A success metric supports an outcome claim only if it measures the intended result—or has a defensible, evidenced connection to it. If it records activity, treat it as evidence that activity occurred, not proof that the result followed.
Start by defining the result you mean
Before interpreting a signal, write down the intended outcome in plain language. Specify who or what should change, the direction of change, and the time period in which it is expected. Then state the evaluation question the evidence is meant to answer.
This keeps an activity or output from being mistaken for an outcome. An activity is something done; an output is an immediate product or service delivered; an outcome is a change that follows. The categories depend on the result being evaluated, so name the intended change rather than relying on a metric label. The CDC recommends aligning indicators with evaluation questions and expected outcomes in its program evaluation guidance.
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An indicator describes the observable evidence that would represent a concept. A measure is how that evidence is quantified or collected. For example, an evaluation might use “participants can complete a task independently” as an indicator, while the measure specifies how independence is assessed and recorded. The example illustrates the distinction; it is not a recommended measure for every setting.
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State what the signal actually records, including its units, collection method, population, and timing. A number can be precise and consistently collected while still representing the wrong concept.
Decide whether the signal is direct or a proxy
A direct measure observes the outcome itself as defined for the evaluation. A proxy measures something else that is expected to reflect the outcome. Proxies can be useful when direct measurement is unavailable or impractical, but their movement alone does not establish that the outcome occurred.
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For any proxy, make the assumed link explicit: why should this observable signal change when the intended outcome changes? Identify what evidence supports that link and what other factors could move the proxy independently. Archived White House guidance warns against letting a proxy stand in for a complex objective without demonstrating a close relationship to the intended result; it discusses modeling as one possible way to examine that link. The guidance is archived, so it is useful here for its measurement reasoning rather than as current policy authority: Performance Measurement Challenges and Strategies.
Check whether the measure is dependable for this use
Reliability: does it behave consistently?
Reliability concerns whether a measure produces consistent results when circumstances have not changed. If the same conditions produce substantially different readings because of the collection process, the signal is difficult to interpret.
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Validity: does it measure the intended thing?
Validity concerns whether the measure assesses what it is intended to assess. Consistency is not enough: a measure can repeatedly capture the wrong thing. GOV.UK describes reliability as a prerequisite to the validity and usefulness of an outcome measure and advises identifying measures early in evaluation design. See Outcome evaluation: evaluation in health and wellbeing.
Sensitivity and outside influences
Ask whether the measure would register a meaningful change in the outcome, and whether unrelated changes could produce the same signal. A proxy may respond to the outcome and to other contributing factors, which makes attribution difficult. OECD guidance highlights this risk and recommends keeping indicator sets proportionate because each additional measure adds collection and reporting burden: Development of a results framework.
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Choose evidence that fits the decision
There is no universally best measure. Compare candidates against the evaluation purpose and the decision the evidence must inform. Useful criteria include:
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- Directness: how closely the measure observes the intended outcome rather than an intermediate activity or proxy.
- Reliability and validity: whether it is consistent and appropriate for the construct and population.
- Sensitivity: whether it can detect a meaningful change.
- External influences: what else could affect the reading.
- Feasibility: the cost, effort, access, and burden on people providing data.
- Timing and frequency: when change can reasonably be observed and how often collection is useful.
- Disaggregation: whether results need to be examined separately for groups relevant to the decision.
Using more than one measure can help when a single signal is ambiguous or exposed to outside influences. The measures need not be combined into one score; keep each one interpretable. ONS guidance connects outputs and outcomes through a theory of change, with corresponding indicators and evidence collected at appropriate times: ONS Evaluation Strategy.
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Match the strength of the claim to the evidence
Distinguish two questions: did the outcome happen, and did a particular action cause it? Evidence that a result occurred does not by itself establish which intervention or other factor produced it.
Use language that reflects what the measure can support. If the connection is indirect or uncertain, say that the signal is consistent with, suggests, or tracks the outcome, and explain the limitation. Reserve a direct outcome claim for evidence that actually measures the outcome; reserve a causal claim for evidence that supports attribution.
Quick Recap
A practical verification sequence
- Write the outcome: state the intended change, population, direction, and time horizon.
- Name the construct: identify the concept the signal is intended to represent, then distinguish the indicator from the measure used to collect it.
- Classify the signal: decide whether it measures the outcome directly or acts as a proxy. For a proxy, document the hypothesized link and evidence for it.
- Test its quality: consider consistency, validity, sensitivity to meaningful change, and susceptibility to unrelated influences.
- Seek proportionate corroboration: add evidence where it addresses a real ambiguity, while accounting for collection burden and timing.
- State the conclusion carefully: describe what happened, what the signal supports, and whether the evidence can attribute the result to a specific intervention.
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