Factorial Validity and Measurement Invariance of the 20-Item Toronto Alexithymia Scale in Clinical and Nonclinical Samples
Bibliographic record
Abstract
The most widely used instrument to measure alexithymia is the 20-item Toronto Alexithymia Scale (TAS-20). However, different factor structures have been found in different languages. This study tests six published factor models and metric invariance across clinical and nonclinical samples. It also investigated whether there is a method effect of the negatively keyed items. Second-order models with alexithymia as a higher order factor are tested. Confirmatory factor analyses showed that the original factor model with three factors-difficulty identifying feelings (DIF); difficulty describing feelings (DDF) and externally oriented thinking (EOT)-is the best fitting model. Partial measurement invariance across samples was illustrated but requires further study. A weakness of the model is the low internal consistency of the third factor. Because models with a method factor had a better fit, future reconsideration of the negatively formulated items seems necessary. No evidence was found for the second-order models.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".