Development and psychometric properties of Chinese social emotional competence measurements
Bibliographic record
Abstract
Introduction: Social-emotional competence (SEC) plays a critical role in the personal and academic development of university students. However, there is a lack of culturally appropriate tools to assess SEC in Chinese populations. This study aimed to develop and validate Chinese versions of two established SEC measurements: the Social Emotional Competence Questionnaire (C-SECQ) and the Social-Emotional Learning Scale (C-SELS). Methods: = 540) provided an independent validation. Both internal consistency and construct validity were examined for the C-SECQ. For the C-SELS, exploratory factor analysis (EFA) was conducted in Study 1, and the resulting model was tested in Study 2 for convergent and discriminant validity. Results: The C-SECQ demonstrated high internal consistency and strong construct validity across both samples, supporting its suitability for use in the Chinese context. In contrast, EFA of the C-SELS revealed a new three-factor structure: Emotional and Social Awareness, Goal Setting and Problem Solving, and Emotional Regulation and Responsibility. However, this revised model showed limited convergent and discriminant validity in Study 2, indicating insufficient psychometric support. Discussion: These findings support the C-SECQ as a reliable and valid tool for assessing SEC among Chinese university students. The study also highlights the challenges in adapting and validating the C-SELS, emphasizing the need for further refinement and cross-cultural validation. Overall, this research contributes to the development of context-appropriate SEL assessment tools in Chinese educational settings.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".