Toronto Alexithymia Scale–20: Examining 18 Competing Factor Structure Solutions in a U.S. Sample and a Philippines Sample
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it