Factorial validity and measurement invariance of the 20-item Toronto Alexithymia scale in clinical and non-clinical 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 6 published factor models and metric invariance across clinical and nonclinical samples.It is 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 3 factorsdifficulty identifying feelings; difficulty describing feelings and externally oriented thinkingis the best fitting model.Partial measurement invariance across samples was illustrated, but needs further study.A weakness of the model is the low internal consistency of the 3 rd factor.As 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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".