Assessing gender role attributes in native Persian speakers: translation, cultural adaptation, and validation of the Persian version of the personal attribute questionnaire
Bibliographic record
Abstract
The Personal Attributes Questionnaire (PAQ) assesses gender roles, including expressivity (femininity) and instrumentality (masculinity), which reflect socially and culturally defined feminine and masculine ways of thinking, feeling, and behaving. The PAQ allows the assessment of gendered traits, beyond the traditional binary view. With the inclusion of gender-related factors in various research fields, the PAQ has been validated in multiple languages and cultures, including German, Chinese, and French. However, a Persian version has not yet been validated. This study aims to validate and examine the test-retest reliability of the culturally adapted Persian PAQ. A total of 436 native Persian speakers (302 females, 134 males) completed the questionnaire. Exploratory and confirmatory factor analyses were conducted to evaluate the factorial structure and validity of the Persian PAQ. In addition, test-retest reliability was assessed to ensure its consistency over time. Exploratory factor analysis confirmed a two-factor structure, although 'Active' loaded on both factors. The results showed a good fit (RMSEA = 0.070, GFI = 0.91 and AGFI = 0.88), acceptable internal consistency (expressivity: α = 0.70, instrumentality: α = 0.72), and moderate to excellent test-retest reliability for instrumentality (ICC = 0.92) and expressivity (ICC = 0.69). The results indicate that women and younger adults were more likely to show lower expressivity and instrumentality (undifferentiated), or higher expressivity and instrumentality (androgynous) compared with males and older adults, respectively. These findings support the validity and reliability of the Persian PAQ and show that gender role attributes are influenced by sex and age. The Persian PAQ will enable to consider the influence of gender in health, sociology, and psychology research.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".