Item response theory and differential test functioning analysis of the HBSC-Symptom-Checklist across 46 countries
Bibliographic record
Abstract
BACKGROUND: The Symptom Checklist (SCL) developed by the Health Behaviour in School-aged Children (HBSC) study is a non-clinical measure of psychosomatic complaints (e.g., headache and feeling low) that has been used in numerous studies. Several studies have investigated the psychometric characteristics of this scale; however, some psychometric properties remain unclear, among them especially a) dimensionality, b) adequacy of the Graded Response Model (GRM), and c) measurement invariance across countries. METHODS: Data from 229,906 adolescents aged 11, 13 and 15 from 46 countries that participated in the 2018 HBSC survey were analyzed. Adolescents were selected using representative sampling and surveyed by questionnaire in the classroom. Dimensionality was investigated using exploratory graph analysis. In addition, we investigated whether the GRM provided an adequate description of the data. Reliability over the latent variable continuum and differential test functioning across countries were also examined. RESULTS: Exploratory graph analyses showed that SCL can be considered as one-dimensional in 16 countries. However, a comparison of the unidimensional with a post-hoc bifactor GRM showed that deviation from a hypothesized one-dimensional structure was negligible in most countries. Multigroup invariance analyses supported configural and metric invariance, but not scalar invariance across 32 countries. Alignment analysis showed non-invariance especially for the items irritability, feeling nervous/bad temper and feeling low. CONCLUSION: HBSC-SCL appears to represent a consistent and reliable unidimensional instrument across most countries. This bodes well for population health analyses that rely on this scale as an early indicator of mental health status.
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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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".