The Internet Gaming Disorder Scale 9-Short Form: longitudinal measurement invariance across a three-year interval
Bibliographic record
Abstract
Objective Internet gaming disorder (IGD) refers to persistent, recurrent, and excessive involvement with computer or video games that cannot be controlled, despite associated problems. Given that it has been relatively stable for several years, questionnaires measuring IGD need to demonstrate measurement invariance (i.e. consistency in its measurement properties when administrated repeatedly over time), to ensure accurate measurement over time. This longitudinal study examined invariance of the Internet Gaming Disorder Scale 9-Short Form (IGDS9-SF5), which measures Internet gaming disorder (IGD) symptoms, based on the Diagnostic and Statistical Manual of Mental Disorders-5 criteria.Method Participants were recruited from English speaking countries (e.g. Australia, USA, UK and Canada). A total of 276 adults (mean age = 31.86 years; SD = 9.94; males = 71%) provided responses to an online survey (including demographic questions and the IGDS9-SF) at three time points one year apart (2020/21/22).Results When the chi-square difference (∆χ2) test was applied, the results supported configural invariance, full metric invariance, error variances, and invariance for all structural components (latent variances and covariances). However, scalar invariance was not observed for three item intercepts (tolerance, preoccupation, and giving up other activities). For all three items, the scores were higher at time 1 than time 2 and time 3.Conclusion The findings indicate that IGDS9-SF observed scores across yearly intervals are generally free from scaling and measurement biases, making them reliable for monitoring the progression of IGD symptoms and evaluating clinical treatment effects over time.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".