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Record W2768881033 · doi:10.1037/ipp0000042

Adaptation of Assessment Scales in Cross-National Research: Issues, Guidelines, and Caveats

2015· article· en· W2768881033 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Perspectives in Psychology · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicPsychometric Methodologies and Testing
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEquivalence (formal languages)CriticismScale (ratio)Set (abstract data type)Adaptation (eye)PsychologyComputer scienceManagement scienceData sciencePolitical scienceMathematicsGeographyEngineering

Abstract

fetched live from OpenAlex

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.

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 armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.125
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.125
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.902
GPT teacher head0.723
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it