Improving the measurement of alexithymia in autistic adults: a psychometric investigation of the 20-item Toronto Alexithymia Scale and generation of a general alexithymia factor score using item response theory
Bibliographic record
Abstract
BACKGROUND: Alexithymia, a personality trait characterized by difficulties interpreting emotional states, is commonly elevated in autistic adults, and a growing body of literature suggests that this trait underlies several cognitive and emotional differences previously attributed to autism. Although questionnaires such as the 20-item Toronto Alexithymia Scale (TAS-20) are frequently used to measure alexithymia in the autistic population, few studies have investigated the psychometric properties of these questionnaires in autistic adults, including whether differential item functioning (I-DIF) exists between autistic and general population adults. METHODS: This study is a revised version of a previous article that was retracted due to copyright concerns (Williams and Gotham in Mol Autism 12:1-40). We conducted an in-depth psychometric analysis of the TAS-20 in a large sample of 743 cognitively able autistic adults recruited from the Simons Foundation SPARK participant pool and 721 general population controls enrolled in a large international psychological study. The factor structure of the TAS-20 was examined using confirmatory factor analysis, and item response theory was used to generate a subset of the items that were strong indicators of a "general alexithymia" factor. Correlations between alexithymia and other clinical outcomes were used to assess the nomological validity of the new alexithymia score in the SPARK sample. RESULTS: The TAS-20 did not exhibit adequate model fit in either the autistic or general population samples. Empirically driven item reduction was undertaken, resulting in an 8-item general alexithymia factor score (GAFS-8, with "TAS" no longer referenced due to copyright) with sound psychometric properties and practically ignorable I-DIF between diagnostic groups. Correlational analyses indicated that GAFS-8 scores, as derived from the TAS-20, meaningfully predict autistic trait levels, repetitive behaviors, and depression symptoms, even after controlling for trait neuroticism. The GAFS-8 also presented no meaningful decrement in nomological validity over the full TAS-20 in autistic participants. LIMITATIONS: Limitations of the current study include a sample of autistic adults that was majority female, later diagnosed, and well educated; clinical and control groups drawn from different studies with variable measures; only 16 of the TAS-20 items being administered to the non-autistic sample; and an inability to test several other important psychometric characteristics of the GAFS-8, including sensitivity to change and I-DIF across multiple administrations. CONCLUSIONS: These results indicate the potential of the GAFS-8 to robustly measure alexithymia in both autistic and non-autistic adults. A free online score calculator has been created to facilitate the use of norm-referenced GAFS-8 latent trait scores in research applications (available at https://asdmeasures.shinyapps.io/alexithymia ).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".