Measuring personality in Libyan Arabs: validating the big five aspect scale with 10 factors domain
Bibliographic record
Abstract
Research has developed the Big-Five Aspect Scale (BFAS), supporting a five-domain model that includes 10 related aspects. In Arabic societies, there is currently a lack of validation evidence for a scale with these 10 aspects. Thus, this study develops and examines the psychometric properties of the short version of the BFAS (BFAS-SV) within Libyan Arab adults. The sample (N = 1136; 74.6% women, Mage = 25.30, SDage = 8.44) completed the original BFAS and the Arabic version of the International Personality Item Pool (IPIP) to assess the BFAS-SV's convergent validity. Confirmatory Factor Analysis (CFA) was applied. The findings provide strong support for the presence of 10 distinct aspects within the Big Five personality domains. Additionally, a robust positive and negative correlation was found among the 10 BFAS-SV aspects, as well as between the BFAS-SV domains of Conscientiousness, Extraversion, Introversion, and Openness/Intellect and their corresponding dimensions in the IPIP, further confirming its concurrent and discriminant validity. Furthermore, the Cronbach's alpha coefficients for the five domains and their respective 10 aspects ranged from 0.61 to 0.85, indicating good internal consistency. Significant gender differences were observed in the Neuroticism domain, particularly in its two aspects (Volatility and Withdrawal), as well as in the Openness/Intellect domain and the Politeness aspect, with women scoring higher in all cases.In conclusion, this study establishes the reliability, validity, and applicability of the Arabic BFAS among the Libyan Arab population. The insights gained into the personality traits and behaviors of Libyan Arab individuals provide valuable implications for personal development and professional success.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".