Internet addiction assessment tools: dimensional structure and methodological status
Bibliographic record
Abstract
AIMS: Excessive internet use is becoming a concern, and some have proposed that it may involve addiction. We evaluated the dimensions assessed by, and psychometric properties of, a range of questionnaires purporting to assess internet addiction. METHODS: Fourteen questionnaires were identified purporting to assess internet addiction among adolescents and adults published between January 1993 and October 2011. Their reported dimensional structure, construct, discriminant and convergent validity and reliability were assessed, as well as the methods used to derive these. RESULTS: Methods used to evaluate internet addiction questionnaires varied considerably. Three dimensions of addiction predominated: compulsive use (79%), negative outcomes (86%) and salience (71%). Less common were escapism (21%), withdrawal symptoms (36%) and other dimensions. Measures of validity and reliability were found to be within normally acceptable limits. CONCLUSIONS: There is a broad convergence of questionnaires purporting to assess internet addiction suggesting that compulsive use, negative outcome and salience should be covered and the questionnaires show adequate psychometric properties. However, the methods used to evaluate the questionnaires vary widely and possible factors contributing to excessive use such as social motivation do not appear to be covered.
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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 | 0.054 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".