Personality traits and examination anxiety: Moderating role of gender
Bibliographic record
Abstract
This study is aimed at examining the moderating effect of gender on the relationship between personality traits and state anxiety. The participants were 375 Iranian high school students (193 males and 182 females). The instruments used were the NEO-FFI-3 Inventory and State Anxiety Inventory. Results of the structural model showed that from the five personality dimensions, extraversion and conscientiousness negatively related to state anxiety whereas neuroticism affected it positively. Results of multigroup analysis also revealed that gender moderated the paths between extraversion and state anxiety in female students and conscientiousness and state anxiety in male students. Overall, findings suggested that both personality traits and gender differences can be determinant factors in state anxiety. Cette étude porte sur l’effet modérateur qu’a le sexe sur le rapport entre les traits de caractère et l’état d’anxiété. Les participants consistaient en 375 élèves iraniens au secondaire (193 hommes et 182 femmes). Le questionnaire NEO-FFI-3 et le questionnaire SAI sur l’anxiété (State Anxiety Inventory) ont servi d’outils à l’étude. Les résultats du modèle structurel ont indiqué que des cinq dimensions du caractère, l’extraversion et la conscience sont en relation négative avec l’anxiété, alors que le névrosisme est en relation positive. Les résultats d’une analyse multi-groupe ont également révélé que le sexe atténuait le rapport entre l’extraversion et l’anxiété chez les femmes, et entre la conscience et l’anxiété chez les hommes. Globalement, les résultats portent à conclure que tant les traits de caractère que les différences entre les sexes peuvent être des facteurs déterminants dans l’état d’anxiété.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".