Development and Assessment of the Personal Emotional Capital Questionnaire for Adolescents
Bibliographic record
Abstract
Higher emotional capital is associated with enhanced positive emotions, social relationships, social capital and human capital. The present study developed and evaluated a personal emotional capital questionnaire for adolescents (PECQ-A) that assessed 10 components of this capital. The PECQ-A was administrated to two samples of Iranian 15-year-olds from two Iranian cities ( N1 = 600, N2 = 300, total N = 900) recruited using multistage random cluster sampling. A confirmatory factor analysis of the first sample confirmed the ten-factor structure of the PECQ-A. The reliability of PECQ-A was acceptable (Cronbach’s α = .90, McDonald’s ω = .88, AVE = .57, Composite reliability CR = .89). Analyses of the second sample revealed that the PECQ-A and its components exhibited convergent validity when compared to the Mental Health Continuum–Short Form (MHC–SF), the students’ GPAs, and the students’ mathematics and natural sciences scores. The PECQ-A demonstrated divergent validity when contrasted with the Depression Anxiety and Stress Scale (DASS21). Test-retest reliability was acceptable. Invariance measurement was confirmed for the PECQ-A. A MANOVA identified several gender differences. PECQ-A scores were not sensitive to the order that the questionnaires were administered. The results suggest that the PECQ–A is a valid and reliable measure of personal emotional capital suitable for use with adolescents.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".