What is the structure of alexithymia? - Modern Test Theory analysis of the Perth Alexithymia Questionnaire
Bibliographic record
Abstract
Background – The level of alexithymia affects a person's mental and physical health. Nevertheless, this trait is still measured using problematic measurement tools. The Perth Alexithymia Questionnaire (PAQ) is suitable measuring tool, but no study has examined its psychometric characteristics using Modern Test Theory (MTT). Thus, we aimed to explore psychometric features of the PAQ using MTT and Classical Test Theory.Methods – A sample of Czech adults (n=848,age:M=34.95,SD=11.90,females:81.13%) participated in the survey. We measured alexithymia, empathy, sensory processing sensitivity (SPS), neuroticism, anxiety, and depression. Results – The confirmatory factor analysis provided evidence of the excellent fit of the modified bifactor solution: Reliability of the PAQ was good (omega= 0.70–0.96). Measurement invariance testing revealed that the PAQ measures alexithymia invariantly between people who are single and in partnership. The PAQ items had high discrimination, and their measurement precision was highest in individuals with above-average alexithymia. Higher alexithymia was present in males. Finally, alexithymia was positively associated with depression, anxiety, neuroticism, and also with SPS after neuroticism was taken into account.Conclusions – The PAQ represents reliable and valid instrument for assessing 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".