Does the Chinese version of 20-item Toronto alexithymia scale (TAS-20-C) measure alexithymia in Chinese young adolescents? Evidence from confirmatory factor analysis, network analysis, and latent profile analysis
Bibliographic record
Abstract
The 20-item Toronto Alexithymia Scale (TAS-20) is a measure of three components of alexithymia: difficulty identifying feelings (DIF), difficulty describing feelings (DDF), and externally oriented thinking (EOT). Although TAS-20 is being increasingly used to measure the alexithymia construct, ongoing controversies remain regarding its internal structure and cross-cultural and cross-group applicability. This study evaluated the psychometric properties of the Chinese version of TAS-20 (TAS-20-C) among 1,355 Chinese young adolescents (mean age = 13.13; SD = 1.00; 52.6% boys) through multiple analytic approaches (i.e., confirmatory factor analysis, network analysis, and latent profile analysis). The confirmatory factor analysis showed reasonable goodness-of-fit for the bi-factorial model with three-dimensional structure and a negatively keyed item factor. However, the results derived from all analytic approaches suggested several items with poor psychometric properties (items 5, 10, 16, 18, 19, 20 from EOT and item 12 from DDF), including poor factor loading on their intended factor, low connectivity and predictability in the item network, and insufficient discrimination across heterogeneous groups. These issues could be attributed to an overabundance of negatively keyed items, translation biases, and the poor readability of certain items. The findings highlight the need for targeted revisions to both the wording and the content for these items, and offer insights into higher priority interventions aimed at improving alexithymia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| 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) | 0.001 | 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".