Was the Instrument Validated in the Same Population?

Does Validation in One Population Apply to Another?

A study uses an established instrument and cites research showing that it has been validated. That sounds reassuring until you look more closely. The earlier studies involved adults, while the current participants are adolescents. Or the instrument was developed among patients with one condition but is now being used with another. Perhaps it was evaluated among university students in one country and is now being administered to a substantially different population elsewhere.

Does that make the instrument inappropriate? Not automatically.

But it does raise a measurement question that should not be skipped: are the existing data actually relevant to the population in which the instrument is now being used?

The Population Does Not Have to Be Identical, but It Must Be Relevant

Population Differences Matter When They Matter to Measurement

It would be impractical to demand a separate validation study for every conceivable combination of age, occupation, diagnosis, educational level, country, and demographic characteristic before an instrument could be used. At the same time, evidence from one group cannot simply be assumed to apply universally.

The more useful approach is to ask whether characteristics that distinguish the populations are relevant to the construct or the measurement process.

Start With the Instrument's Intended Target Population

Find out who the instrument was originally designed for. The development paper, instrument manual, official documentation, or key measurement studies may describe the intended age range, clinical group, setting, language, or other relevant characteristics.

This matters because instrument content is developed with particular respondents in mind. COSMIN treats the target population as part of the conceptual considerations involved in evaluating content validity. Its current guidance describes content validity in terms of the relevance, comprehensiveness, and comprehensibility of instrument content for the construct, target population, and context of use.

In other words, an item is not relevant in the abstract. Its relevance is partly a question of relevance for whom.

Do Not Turn “Same Population” Into a Demographic Matching Exercise

The original and current samples do not need to have identical demographic tables. A difference matters when it could plausibly influence the measurement.

Age may matter when questions assume adult responsibilities that children do not have. Clinical status may matter when symptoms overlap with instrument content. Educational background may matter when comprehension depends on specialized terminology. Cultural context may matter when behaviors or experiences have different meanings.

Other differences may have little consequence for a particular measure. There is no methodological rule that every demographic difference invalidates previous evidence.

Ask Whether the Content Still Makes Sense for These Participants

One of the simplest checks is surprisingly informative: read the instrument.

Would its questions, tasks, response options, or observations be relevant to people in the current population? Are important experiences missing? Could participants understand the content as intended?

Suppose a work-related stress instrument contains questions about supervisors, workloads, promotion opportunities, and job security. Using it among university students by simply calling the resulting score “stress” would require considerable justification. The issue is not that students cannot experience stress. It is that the instrument's content was designed around a particular manifestation and context of the construct.

This returns you to the broader question of whether the study actually measured the construct it claims to have measured.

Check Whether Participants Understand the Items as Intended

A question can be relevant yet still function poorly if respondents interpret it differently from the population for which it was developed.

Developmental differences provide an obvious example. Children may interpret abstract language differently from adults. Terms that are routine among healthcare professionals may not be equally comprehensible to community respondents. An item involving a particular social role may mean something different to retired adults than to employed adults.

Evidence from cognitive interviews, pilot testing, qualitative studies, or content-validity work can help establish whether respondents understand the content as intended. COSMIN explicitly includes comprehensibility alongside relevance and comprehensiveness in its conception of content validity.

Look for Evidence From the Current or a Closely Relevant Population

If the instrument has been widely used, the original validation paper may not be the most relevant evidence anymore. Search for subsequent measurement studies involving participants similar to those in the study you are evaluating.

Systematic reviews of measurement instruments can be especially useful because they may bring together evidence across populations and measurement properties. COSMIN maintains a database of systematic reviews specifically to support appraisal and selection of outcome measurement instruments.

The absence of an exact population match does not prove that the instrument fails. It means you must decide how far the available evidence can reasonably be generalized.

Consider Whether the Instrument Functions Differently Across Groups

For some measures, researchers can directly investigate whether items or scores function similarly across populations. This is where concepts such as measurement invariance and differential item functioning become relevant.

COSMIN defines measurement invariance in terms of whether people from different groups who have the same level of the underlying construct have the same expected measurement outcome or probability of selecting particular responses. Cross-cultural validity and measurement invariance may be evaluated by comparing factor structures or examining differential item functioning across groups.

Imagine two groups with the same underlying level of depression. If members of one group systematically endorse a particular item differently for reasons unrelated to depression itself, that item may not function equivalently across the groups.

Such analyses are not required every time an instrument crosses a demographic boundary, but they can provide valuable evidence when comparability across groups is central to the research question.

A Similar Reliability Coefficient Does Not Establish Population Equivalence

Researchers sometimes use an established instrument in a new population, calculate Cronbach's alpha, obtain a reassuring value, and consider the problem resolved.

That tells you much less than it may appear to.

Similar internal consistency does not establish that respondents understand the items similarly, that the same construct structure applies, that individual items function equivalently, or that the instrument contains the right content for the new population.

This is one reason why using a previously validated instrument does not automatically establish valid measurement in the current study.

The Required Evidence Depends on the Claim

Population comparability becomes particularly important when researchers directly compare groups.

If a study reports that adolescents score higher than adults on a psychological construct, for example, the interpretation assumes that the instrument has sufficiently comparable meaning across age groups. Otherwise, some portion of the apparent difference could reflect the measurement process rather than the underlying construct.

By contrast, a study using an instrument descriptively within one relatively similar population may raise a different set of concerns. Measurement appraisal should follow the inference the researchers actually want to make.

When an Adult Measure Is Used With Adolescents

Common Mistakes About Validation Populations

Judge Population Differences by Their Measurement Consequences

When reviewing a study, first identify the population underlying the strongest measurement evidence and compare it with the current participants. Then ask what the differences could plausibly change.

If the instrument is also administered in another language, population and language questions may overlap but should not be collapsed into one issue. The language version itself requires appropriate scrutiny.

Ultimately, the importance of any unresolved population mismatch depends on how central the measure is to the study. Serious uncertainty about a primary outcome should generally affect how much weight you place on the resulting findings more than a minor concern about a peripheral variable.

Check Whether the Validation Evidence Fits the Population

Questions About Instruments and Validation Populations

The Relevant Question Is Whether the Evidence Transfers

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