Measurement Invariance of English and French Language Versions of the 20-Item Toronto Alexithymia Scale
Bibliographic record
Abstract
Abstract. The alexithymia construct is commonly measured with the 20-Item Toronto Alexithymia Scale (TAS-20), with more than 20 different language translations. Despite replication of the factor structure, however, it cannot be assumed that observed differences in mean TAS-20 scores can be interpreted similarly across different languages and cultural groups. It is necessary to also demonstrate measurement invariance (MI) for language. The aim of this study was to evaluate MI of the English and French versions of the TAS-20 using data from 17,866 Canadian military recruits; 71% spoke English and 29% spoke French as their first language. We used confirmatory factor analyses (CFAs) to establish a baseline model of the TAS-20, and four increasingly restrictive multigroup CFA analyses to evaluate configural, metric, scalar, and residual error levels of MI. The best fitting factor structure in both samples was an oblique 3-factor model with an additional method factor comprised of negatively-keyed items. MI was achieved at all four levels of invariance. There were only small differences in mean scores across the two samples. Results support MI of English and French versions of the TAS-20, allowing meaningful comparisons of findings from investigations in Canadian French-speaking and English-speaking groups.
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How this classification was reachedexpand
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".