Toronto Alexithymia Scale–20: Examining 18 Competing Factor Structure Solutions in a U.S. Sample and a Philippines Sample
Bibliographic record
Abstract
The Toronto Alexithymia Scale-20 is arguably the most utilized measure of alexithymia. Although a three-factor solution has been found by numerous studies, these findings are not universal. This article examined and compared 18 competing factor structures for the Toronto Alexithymia Scale-20, which included between one and four correlated latent factor structures, common methods models that accounts for negatively worded items, and bifactor models. Although the two-factor bifactor model with a common methods factor had the better model fit compared with the other 17 models examined, it still did not achieve the requisites of a good model fit across all model fit indices. Issues stemmed primarily from the externally oriented thinking factor and the negatively worded items. Post hoc analyses indicated that a two-factor bifactor model with the negatively worded items dropped achieved the requisites of a good model fit and can be treated as a unidimensional measure despite the presence of multidimensionality. Multiple-group analysis indicated that the factor loadings were invariant across U.S. and Philippines samples. After controlling for noninvariance at the item intercept level, the Philippines sample had a higher alexithymia general score compared with the U.S. sample.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".