Relationship between Personality Traits and Performance among School Principals
Bibliographic record
Abstract
This research seeks to explore the relationship between personality traits and performance among school principals. The main objective of this research is to analyze the relationship between principals’ personality traits such as introversion, extroversion neuroticism and emotional stability between several performance dimensions. A descriptive correlation research method was employed a sample size of 50 principals and 176 teachers in elementary schools, middle schools and high schools were selected from the total population of principals and teachers in the academic year of 2008-2009 through the stratified sampling method. The Eysenck questionnaire used to assess principal's personality. Weiss K performance evolution questionnaire used to assess principals' performance. Reliability of the questionnaires determined 0.81 and 0.98 respectively. Data analyzed using Pearson correlation coefficient, multiple regression, one way- analysis of variance (ANOVA), and Tukey test. Results showed that there is a positive relationship between principal’s personality characteristics of introversion and extroversion and performance dimensions, and 35% of score variance in leadership, human relationships and organizational dimensions was explainable by extroversion and introversion scores. Also there is a negative relationship between neuroticism and emotional stability and performance dimensions, and 40% of scores variance in professional and educational dimensions was explainable by neuroticism and emotional stability of principals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".