Longitudinal assessment of trait emotional intelligence: Measurement invariance and construct continuity from late childhood to adolescence.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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 it