Measurement Invariance and Relationships Among School Connectedness, Cyberbullying, and Cybervictimization: A Comparison Among Canadian, Chinese, and Tanzanian Adolescents
Bibliographic record
Abstract
The aim of the present study was to evaluate the measurement invariance of the School Connectedness Scale for Chinese, Canadian, and Tanzanian adolescents, and to explore the inter association between school connectedness and cyberbullying/cybervictimization. Participants included 3872 adolescents from urban settings in China ( N= 2053, M age =16.36 years, SD = 1.14 years; 44.6% boys), Canada ( N = 642, M age = 12.13 years, SD = 0.77 years; 50.1% boys), and Tanzania ( N = 1056 , M age =15.87 years, SD = 2.03 years; 52.8% boys). Adolescents self-reported their cybervictimization and cyberbullying experiences, as well as their perceived school connectedness. Multigroup Confirmatory Factor Analysis revealed an approximate measurement invariance of the scale across the three countries. Chinese students showed the lowest levels of school connectedness while Tanzanian students showed the highest. The findings of the multivariate multigroup regression analyses across the three countries revealed similar relationships between school connectedness and cyberbullying/cybervictimization, thus broadening our understanding of school connectedness and its relationship to cyberbullying/cybervictimization across these three different countries.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".