The Relationship Between Longevity and a Leader’s Emotional Intelligence and Resilience
Bibliographic record
Abstract
The role of an educational leader is complex, challenging and, at times, fraught with adversity. Overcoming the many challenges and hardships, and flourishing as an educational leader, requires resilience and an instinct for survival. According to Maulding, Leonard, Peters, Roberts and Sparkman (2012), understanding how to prevail in the face of difficult conditions, by employing one’s emotional strengths as well as vulnerabilities and how to increase one’s ability to remain resilient, is valuable for an educational leader to succeed in the face of adversity. The purpose of this study was to research Montana educational leaders to discern whether emotional intelligence (EI) is necessary to remain resilient and successful in a leadership role despite adversity. This quantitative research was undertaken as a non-experimental, ex post facto, or after-the-fact research. Participants for this study included sixty-one superintendents, principals, and assistant principals, from a population of 935 educational leaders, who held a leadership position in the State of Montana during the 2017-2018 school year. A linear regression was used to examine the proportion of variance in years in a leadership position that can be explained by emotional intelligence and resilience. This analysis demonstrated that some EI competencies appear to have an effect on the longevity of an educational leader in a position. However, the effects vary between assistant principals, principals, and superintendents, not all competencies were equal. The coefficient of determination showed assistant principals and principals’ years of service is more strongly influenced by all emotional intelligence competencies than is that of the superintendent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".