Revisiting the academic self‐concept transcultural measurement model: The case of Spain and China
Bibliographic record
Abstract
BACKGROUND: Modelling academic self-concept through second-order factors or bifactor structures is an important issue with substantive and practical implications; besides, the bifactor model has not been analysed with a Chinese sample and cross-cultural studies in the academic self-concept are scarce. Likewise, latent structure validity evidence using network psychometrics has not been carried out. AIMS: The aim of this study is twofold: to analyse (1) the internal structure of ASC through the Self-Description Questionnaire II-Short (SDQII-S) in Chinese and Spanish samples using two approaches, structural equation modelling and network psychometrics conducting an exploratory graph analysis; and (2) the measurement invariance of the best model across countries and investigate the cross-cultural differences in ASC. SAMPLE: The sample was composed by 651 adolescents. Seven models of ASC were tested. RESULTS: Results supported the multi-dimensional nature of the data as well as the reliability. The best-fitted model for the two subsamples was the three-factor ESEM model, but only the configural invariance of this model was supported across countries. The graph function shows that the school dimension appears more related to the verbal factor in the Spanish subsample and to the math dimension in the Chinese subsample. Likewise, the relationship between verbal and math factors in Spanish students is non-existent, but this connection is more relevant for Chinese students. CONCLUSION: These two differences may be behind the difficulty in finding invariance using SEM models. It is a question of the construct's nature, less related to analytical phenomena, and deserves deeper discussion.
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.
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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".