Alexithymia or general psychological distress? Discriminant validity of the Toronto Alexithymia Scale and the Perth Alexithymia Questionnaire
Bibliographic record
Abstract
BACKGROUND: Alexithymia is an important transdiagnostic risk factor for emotion-based psychopathologies. However, it remains unclear whether alexithymia questionnaires actually measure alexithymia, or whether they measure emotional distress. Our aim here was to address this discriminant validity concern via exploratory factor analysis (EFA) of the 20-item Toronto Alexithymia Scale (TAS-20) and the Perth Alexithymia Questionnaire (PAQ). METHOD: United States general community adults (N = 508) completed the TAS-20, PAQ, and the Depression Anxiety Stress Scales-21 (DASS-21). EFA was used to examine the latent dimensions underlying these measures' scores. RESULTS: Our EFA extracted two higher-order factors, an "alexithymia" factor and a "general distress" factor (i.e., depression, anxiety, stress). All PAQ scores loaded cleanly on the alexithymia factor, with no cross-loadings on the distress factor. However, for the TAS-20, Difficulty Identifying Feelings (DIF) facet scores cross-loaded highly on the distress factor. LIMITATIONS: Our sample consisted of general community adults; future work in clinical settings will be useful. CONCLUSIONS: Our data indicate that the PAQ has good discriminant validity. However, the TAS-20 appears to have significant discriminant validity problems, in that much of the variance in its DIF facet reflects people's current levels of distress, rather than alexithymia. The TAS-20, which has traditionally been the most widely used alexithymia questionnaire, may therefore not be the optimal alexithymia tool. Our findings add to the body of evidence supporting the validity and utility of the PAQ and suggest that, moving forward, it is a superior option to the TAS-20 for alexithymia assessments.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".