Adaptation of Assessment Scales in Cross-National Research: Issues, Guidelines, and Caveats
Bibliographic record
Abstract
Increasingly, over the past 2 decades, there has been a growing interest in cross-national comparisons. This activity, in turn, has precipitated an escalating number of assessment scales being translated into other languages for use in countries and cultures that differ from those of the original scales (typically developed and normed in the United States). Recent criticism of these translated scales has highlighted the singularity of focus on linguistic equivalence albeit with little to no regard for equivalence of the measured constructs, relevance of item content, familiarity with item format, and insufficient rigor of the methodological strategy, thereby leading to serious biasing effects that ultimately yield a multiplicity of complexities in cross-national research and practice. Intended as an aid to researchers confronted with the task of translating and adapting an assessment scale for use in a country and culture that differs from that of the original scale, this article (a) highlights the critical importance of equivalence as it relates to the translated and adapted scale, in addition to the construct(s) it is designed to measure, (b) identifies the major threats to such equivalence and exemplifies several ways by which they can bias cross-national comparisons, (c) outlines a recommended series of psychometric analytic stages that can lead to both a close translation and a rigorously adapted assessment scale, (d) describes and explicates the hierarchical set of steps necessary in testing equivalence of the adapted instrument within and across national groups, and (e) presents the advantages and disadvantages of the adaptation approach recommended for use in this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.551 | 0.749 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.008 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".