Validated for Whom? Why Validity Evidence May Not Automatically Transfer Across Populations

If an Instrument Was Validated Elsewhere, Can You Use It With Your Population?

You find an established questionnaire that appears perfect for your study. It has been published, cited repeatedly, and supported by reliability and validity evidence. There is only one complication: the original validation involved a different population.

Perhaps the instrument was developed with adults and you are studying adolescents. It was evaluated among nurses but your participants are university students. It was developed in another country or language. Or the population appears broadly similar, yet differs in ways that could affect how participants understand the construct and respond to particular items.

Can you simply cite the original validation study and proceed?

Sometimes the existing evidence may transfer reasonably well. Sometimes it may not. The important methodological question is not whether the instrument has ever been validated, but whether the available evidence supports the interpretation you intend to make from scores in the population you are actually studying.

Validity Evidence Is Relevant Only to the Extent That It Supports Your Intended Use

The Instrument May Be the Same While the Measurement Context Changes

Researchers sometimes talk about validity as though it were permanently attached to an instrument. Once a scale has been “validated,” later researchers cite the validation paper and inherit its validity.

Contemporary measurement theory takes a more qualified position. The Standards for Educational and Psychological Testing frames validity around evidence and theory supporting interpretations of scores for proposed uses. That shifts the question from “Is this instrument valid?” to “Does the available evidence support this interpretation of these scores for this purpose?”

The distinction becomes especially important when the population changes.

Why Can Population Differences Matter?

A questionnaire item is not merely a sentence that produces a number. Respondents must interpret the wording, connect it to their experiences, decide what information is relevant, and map their judgment onto the available response options.

Those processes can differ across populations.

An item about “managing a classroom,” for example, may carry quite different meaning for preservice teachers, novice teachers, and educators with twenty years of professional experience. A question about independent technology use may operate differently among participants who encounter the technology daily and those who have rarely used it.

Population differences do not automatically make the measurement invalid. They create a question that the researcher needs to consider: does the interpretation supported in the original population remain justified in the new one?

Validated for Whom Is Part of the Validity Question

Suppose a scale was developed to measure research self-efficacy among doctoral students. You want to administer it to first-year undergraduate students.

Both groups are students, and both may participate in research-related activities. Yet some items may presuppose experiences that doctoral students routinely have and first-year students do not. An item about confidence defending methodological decisions to reviewers could function very differently for respondents who have never written a research manuscript.

The problem is not merely demographic. The relationship between the item and the underlying construct may have changed.

This is why using a previously validated instrument does not automatically validate its use in a new study. Researchers need to inspect the evidence behind the instrument and judge its relevance to the new application.

Language and Culture Can Change More Than the Wording

Translation creates an obvious reason to reconsider existing validity evidence, but the issue extends beyond whether words have been translated accurately.

An item can be linguistically correct while functioning differently because the behavior, social expectation, institutional practice, or experience described by the item has a different meaning in another context.

For example, an item referring to seeking assistance from an instructor may partly reflect attitudes toward authority, classroom norms, or access to faculty rather than only the intended construct. Those contextual influences could differ substantially across educational systems.

COSMIN treats cross-cultural validity and measurement invariance broadly. Its guidance considers differences not only between language or ethnic groups but also between groups defined by characteristics such as age, gender, or patient population when those differences could affect measurement.

What Is Measurement Invariance?

Measurement invariance concerns whether a measurement model functions sufficiently similarly across groups for the intended comparisons or interpretations.

At a conceptual level, suppose two people from different groups have the same level of the underlying construct. If an item systematically produces different responses because of group membership rather than differences in the construct, the item may not be functioning equivalently.

COSMIN describes measurement invariance in terms of respondents from different groups with the same latent-trait level having the same expected score or probability of selecting a response option. Cross-cultural validity may be investigated using methods such as multigroup confirmatory factor analysis or differential item functioning analysis.

These are not the same phenomenon. If groups obtain different mean scores, researchers should not immediately assume that the underlying construct itself differs. Depending on the measurement model and intended comparison, measurement equivalence may need to be considered first.

What Is Differential Item Functioning?

Differential item functioning, commonly abbreviated as DIF, concerns items that behave differently across groups after accounting for the underlying construct being measured.

Imagine an assessment of digital competence containing an item about configuring a desktop computer. Two groups may have comparable overall digital competence, but younger respondents who predominantly use mobile devices might respond differently because the item reflects a particular technological experience.

If that occurs systematically, the item may be measuring more than the intended construct.

DIF analysis is one way to investigate such problems. It is not required mechanically for every questionnaire used with any new sample, but it becomes particularly relevant when researchers intend to compare scores across groups and there is reason to question whether items function equivalently.

The Same Language Does Not Guarantee the Same Meaning

Cross-population concerns are sometimes reduced to translation. Yet two populations can speak the same language and still interpret an instrument differently.

Consider a workplace-autonomy questionnaire developed for corporate employees and later administered to university faculty. Terms such as “supervisor,” “work schedule,” or “organizational decision” may be linguistically identical but embedded in substantially different professional structures.

Similarly, an educational scale developed for conventional face-to-face instruction may require scrutiny when applied to fully online education. The respondents may speak the same language and belong to the same age group, but the context in which the construct operates has changed.

Reliability Can Also Change Across Populations

A reliability coefficient reported in the original validation study is not a permanent characteristic of the questionnaire.

Reliability estimates depend partly on the population and measurement conditions. Differences in score variability, item interpretation, administration, and sample characteristics may change estimates such as internal consistency or test-retest reliability.

This means you should not copy the original study's reliability coefficient into your methodology as evidence that scores in your own study are reliable. The reliability evidence relevant to your measurements should be considered in relation to your actual use of the instrument.

Does Every New Population Require Complete Revalidation?

No. That would turn instrument use into an unnecessarily repetitive exercise.

The amount of additional evidence needed should be proportionate to how much uncertainty the new application creates. An unchanged instrument used for the same construct and purpose in a closely comparable population may require relatively modest additional evaluation. A translated, shortened, culturally adapted instrument used with a substantially different population for a new purpose creates much greater uncertainty.

Population Transfer Is an Evidence Question, Not a Yes-or-No Rule

The objective is not to prove that every instrument behaves identically everywhere. Nor should researchers assume that any demographic difference invalidates previous evidence.

Instead, ask what differences between the original validation context and the new study could plausibly matter for the intended score interpretation.

That requires substantive knowledge of the construct as well as statistical analysis. Measurement invariance cannot rescue a poorly defined construct, and demographic similarity cannot guarantee that respondents interpret items in the same way.

The broader principle remains the same as for the different sources of validity evidence: gather evidence that addresses the interpretation you actually intend to make rather than completing methodological procedures simply because they appear on a checklist.

When an Established Scale Moves to a New Population

Common Mistakes When Transferring Validity Evidence

Evaluate How Far the Existing Evidence Can Reasonably Travel

When adopting an instrument, do not begin by asking whether your population is identical to the original population. Exact replication is rarely possible.

Instead, identify differences that could plausibly affect the construct, response process, item relevance, measurement structure, or intended use. Then determine what additional evidence those differences warrant.

The key is proportionality. Ask enough methodological questions to justify your interpretation without pretending that every new sample requires rebuilding the measurement instrument from zero.

Before Using an Instrument With a New Population

Frequently Asked Questions About Validity Across Populations

A Validated Instrument Still Has a Population and Context

How to Cite This Guide

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