The Factorial Structure of the Toronto Alexithymia Scale (TAS-20) Among a Sample of Al-Quds Open University Students
Bibliographic record
Abstract
The study aimed to investigate the factor structure of the Toronto Alexithymia Scale (TAS-20) among a Palestinian sample of students from Al-Quds Open University, using a descriptive approach. The study also aimed to test the fit of five competing models. To achieve these objectives, the TAS-20 scale, developed by Bagby et al. (1994), was administered to a cluster random sample of 381 university students, consisting of 145 males and 236 females. The (WLSMV) was used to examine the factor structure of the scale, using Mplus 8 software. Results indicated that the one-factor and two-factor models did not fit the data, while the three-factor, four-factor, and second-order models did fit the data. The best fit was achieved by the third-order model, with the following fit indices: χ2= 506.804*, df=167, RMSEA= .073, CFI= .985, TLI= .983. The study also demonstrated the convergent and discriminant validity of the scale, as well as its internal consistency, as evidenced by Cronbach's alpha coefficient of .884 and McDonald's omega coefficient of .849.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".