The Five Most Common Mistakes in Scale Adaptation

From the outside, scale adaptation looks like a translation job. That is precisely why it is one of the article types most often rejected in peer review.

Moving a scale into another language and culture does not end with translating the items. The aim is to show that the instrument measures the same construct in the same way in the new culture. The five mistakes below are the most common points at which that aim is lost.

1. Settling for a one-way translation

Having one expert translate the items and treating the process as complete is the most basic gap in adaptation work. The accepted approach has several stages: forward translation by two independent translators, reconciliation of the two, back-translation by a translator whose first language is the source language, and a final review by an expert committee.

This chain prevents a single translator’s interpretation from settling quietly into the scale.

2. Confusing cultural equivalence with linguistic equivalence

An item may be translated flawlessly in grammatical terms and still have no counterpart in the target culture. “I make decisions for myself”, developed in an individualist culture, does not carry the same psychological meaning in a collectivist context.

3. Running EFA and CFA on the same dataset

Running confirmatory factor analysis on the same sample you used for exploratory factor analysis is asking the same data the same question twice. CFA loses its confirmatory character.

The correct route is to split the sample randomly in two, or to collect two separate datasets. Studies that make this separation attract noticeably fewer objections in peer review.

4. Skipping measurement invariance

This is the step most often omitted from adaptation studies, and the one most often criticised. If you intend to compare two groups with the adapted scale — the target and original samples, say, or women and men — you must first show that the instrument measures the same construct in those groups.

Comparing group means without establishing scalar invariance is like adding two values measured in different units.

5. Reducing reliability to a single coefficient

Cronbach’s alpha is still the most reported reliability coefficient, but its assumptions are strict: it requires items to be equally weighted and error terms to be uncorrelated. Most scales do not meet those assumptions.

Composite reliability (McDonald’s omega) does not require them, and is increasingly preferred. Keep reporting alpha, but report omega alongside it.

The HTMT criterion is more sensitive than the classical Fornell–Larcker criterion at detecting discriminant validity problems (Henseler et al., 2015).