Longitudinal assessment of trait emotional intelligence: Measurement invariance and construct continuity from late childhood to adolescence.
Bibliographic record
Abstract
Amid the growing efforts to promote positive youth development, trait emotional intelligence (TEI) has emerged as an important protective factor in the processes of resilience and adaptation. The inclusion of a brief form of the Emotional Quotient Inventory-Youth Version (EQi:YV-Brief) in the Canadian National Longitudinal Survey of Children and Youth (NLSCY) presents a unique opportunity to study the developmental dynamics of TEI during the transition from childhood to adolescence. However, before drawing any inferences about construct continuity and change, researchers must establish that the EQi:YV-Brief functions equivalently over time. This study tested configural, metric, scalar, and residual measurement invariance of the EQi:YV-Brief over a 6-year period from late childhood (age 10-11) to adolescence (age 16-17). Longitudinal mean and covariance structures models were fitted to the data from 773 NLSCY participants (51% girls) who completed the EQi:YV-Brief at 4 biennial cycles. Three of the 4 EQi:YV-Brief subscales were found to be fully invariant at ages 12-13 through 17-18 and partially invariant at age 10-11. Controlling for partial noninvariance, we also investigated patterns of rank-order stability and mean-level change in TEI. These exploratory analyses showed that individual differences in TEI became increasingly more stable with age and that changes in mean TEI levels followed a complex nonlinear pattern over time. The results supported the longitudinal utility of 3 of the 4 EQi:YV-Brief subscales used in the NLSCY, supporting their further use in research on the developmental dynamics of TEI.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".