Pragmatic Validation Without Experimental Design

Triangulation as a pragmatic validation strategy in applied contexts

Anyone who works with measurement knows what a methodological bottleneck feels like.

There is a phenomenon that needs to be measured. There is a timeline that will not budge. There is a client or stakeholder waiting for results. And there is a methodological conscience reminding you that the instrument should be properly validated.

Traditional validation requires an exploratory phase, a confirmatory phase, a sufficiently large sample for both, and often an experimental or quasi-experimental design. The timeline easily stretches to six months or more. In applied contexts, this combination rarely materializes.

So what then? Do we abandon validation altogether? Do we settle for ad hoc measures and hope for the best?

We do not have to. There is a third, pragmatic path.

Methodological scope

The approach described here does not aim to replace experimental or quasi-experimental research designs when their use is possible and justified. Validation based on triangulation, stakeholder validation, and construct validity is intended as a pragmatic solution for contexts where timeline, sample size, purpose, or ethical constraints do not allow for a full validation process.

The focus is on achieving sufficient reliability of the instrument and phenomenon identification to support decision-making, not on establishing causal relationships.

Requirements of traditional validation

Classical instrument validation follows a well-known pattern.

First, the exploratory phase: data is collected, factor analysis is performed, items are trimmed, and structures are evaluated. Then the confirmatory phase: new data, confirmatory factor analysis, and model fit assessment. If needed, criterion validity is also examined, such as correlations with other instruments or predictive validity in relation to later outcomes.

This approach is methodologically sound. It is also often practically impossible.

The reasons are familiar: the timeline does not allow for two separate data collections, the sample size is too small, resources are lacking, or the context of use is so specific that broad generalizability is not even a goal.

The result is often one of two extremes: either a heavy validation process that delays everything, or skipping validation entirely.

Triangulation as an alternative

Triangulation offers a third path here. When combined with stakeholder validation and consistent adherence to construct validity principles, the validation of both the measures and the phenomenon itself becomes substantially more reliable, without the long timeline and heavy experimental design that traditional research requires.

The logic of triangulation is simple: if multiple independent measurement approaches point in the same direction, the reliability of conclusions increases. This is about multi-method validity, not a substitute for causal proof.

In practice, this means approaching the same phenomenon from different angles. Self-assessments, assessments by others, observable data, registry information, and traces of activity in systems. When these sources converge, there is a stronger foundation for conclusions than any single measure could provide.

This is not a methodological shortcut or a replacement for full experimental design. It is an alternative approach to validity in contexts where the traditional process is not realistic.

Stakeholder validation: face validity systematically

Content validity and face validity often remain implicit in applied measurement. A mere impression that the instrument appears to measure the right thing is not enough.

Stakeholder validation makes this explicit. It means that the targets of measurement, the people whose behavior or experience is being measured, systematically evaluate whether the instrument corresponds to their experienced reality.

In practice, this means reviewing the instrument items with the target group and asking: Do you recognize this phenomenon? Is this relevant? Is something essential missing? Is any item unclear or misleading?

This does not replace statistical validation, but it complements it in a way that is particularly valuable when the traditional process cannot be implemented. It also produces qualitative information that supports the interpretation of later results.

Construct validity: theoretical grounding and coherence

Triangulation does not mean that any combination of multiple measures will suffice. The measures must be based on an explicit theoretical understanding of the phenomenon being measured.

Construct validity requires defining what is being measured, why these specific indicators were chosen, and how they relate to each other and to the broader construct. Building an instrument begins with defining the phenomenon, not with inventing questions.

Furthermore, different measurement approaches must be theoretically compatible. They measure different aspects or manifestations of the same phenomenon, not disconnected things.

When triangulation is based on a theoretically coherent structure, it forms a coherent whole rather than just a collection of numbers.

When this is sufficient – and when it is not

Triangulation together with stakeholder validation and construct validity forms a pragmatic solution. It does not replace a full validation process in all situations.

This approach is justified when:

•      the instrument is used in a bounded, context-specific environment

•      the goal is to track change, not to diagnose individuals

•      decisions are based on the overall picture, not individual scores

•      timeline and resources do not allow for a traditional validation process

•      triangulation is implemented systematically

It is not sufficient when:

•      the instrument is used for clinical diagnosis or screening

•      individual-level decisions are made based on the instrument

•      the instrument is intended for broad, context-independent use

•      legal or ethical requirements demand full validation

•      the consequences of incorrect conclusions are severe

This is about risk assessment. The higher the stakes, the stricter the validation requirements.

Automated statistical analysis

Although triangulation reduces dependence on the psychometric properties of any single instrument, statistical analysis remains valuable.

evaluoi.ai provides automated statistical analysis when the data allows. This includes polychoric correlations for ordinal data, IRT analysis for evaluating item functioning, and reliability checks for examining internal consistency.

These analyses are not prerequisites for triangulation to work, but they provide additional information about instrument quality and help identify problematic items or unexpected structures in the data.

The key point is that these analyses are available without specialized statistical expertise. The system produces the analysis; the expert is responsible for interpretation.

Framework: triangulation in pragmatic validation

Conclusion

Validation is not a binary choice between a perfect process and its absence.

Triangulation, stakeholder validation, and construct validity principles together form a pragmatic but scientifically grounded alternative. This is not a shortcut, but a different route to the same destination: sufficiently reliable information on which to base justified decisions.

When multiple measurement approaches point in the same direction, when instruments are grounded in theoretical understanding, and when the target group recognizes the instrument as relevant, we are closer to valid measurement than any single reaction survey or smiley-face questionnaire could achieve.

evaluoi.ai enables triangulation-based measurement by combining dialogue-based goal setting, AI-assisted instrument building, and automated statistical analysis, without months of methodological groundwork.