A bifactor analysis of the 20-item Toronto Alexithymia Scale: Further support for a general alexithymia factor.
Bibliographic record
Abstract
The 20-Item Toronto Alexithymia Scale (TAS-20) is the most widely used measure to assess the personality construct of alexithymia and is composed of three-factor analytically derived subscales. These subscales measure and represent three critical, theoretically based facets of alexithymia. The subscales are distinct, yet highly interrelated and only as a collective body do they reflect adequately the alexithymia construct. Although different studies using both university student and community samples suggest that TAS-20 total scores are largely reflective of variation on a single construct, and that subscale scores do not provide unique and reliable information beyond total scores, many users of the scale frequently continue to employ and even rely more heavily on subscale scores rather than total scale scores when interpreting research study outcomes. Our goal in this study is to provide clinicians and researchers with replicable psychometric information for the TAS-20 estimated from bifactor modeling in an attempt to provide further support for using total rather than subscale scores. In general, our findings were consistent with previous studies indicating that TAS-20 total scores can be considered indicative of a single construct. The replication of these earlier results from previous investigations provides additional support for the use of a total TAS-20 score and questions the utility of using TAS-20 subscale scores. Based on these results, we recommend that researchers and clinicians use a single total TAS-20 score and not subscale scores. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.024 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".