Cross-Cultural Validation of the Compulsive Internet Use Scale in Four Forms and Eight Languages
Bibliographic record
Abstract
The 14-item Compulsive Internet Use Scale (CIUS) is one of the most frequently internationally adapted psychometric instruments developed to assess generalized problematic Internet use. Multiple adaptations of this instrument have led to versions in different languages (e.g., Arabic and French), and different numbers of items (e.g., from 5 to 16 items instead of the original 14). However, to date, the CIUS has never been simultaneously compared and validated in several languages and different versions. Consequently, the present study tested the psychometric properties of four CIUS versions (i.e., CIUS-14, CIUS-9, CIUS-7, and CIUS-5) across eight languages (i.e., German, French, English, Finnish, Spanish, Italian, Polish, and Hungarian) to (a) examine their psychometric properties, and (b) test their measurement invariance. These analyses also identified the optimal versions of the CIUS. The data were collected via online surveys administered to 4,226 voluntary participants from 15 countries, aged at least 18 years, and recruited from academic environments. All brief versions of the CIUS in all eight languages were validated. Dimensional, configural, and metric invariance were established across all languages for the CIUS-5, CIUS-7, and CIUS-9, but the CIUS-5 and CIUS-7 were slightly more suitable because their model fitted the ordinal estimate better, while for cross-comparisons, the CIUS-9 was slightly better. The brief versions of the CIUS are therefore reliable and structurally stable instruments that can be used for cross-cultural research across adult populations.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".