The psychometrics of Internet addiction and Internet Gaming Disorder: a step towards measurement unification
Bibliographic record
Abstract
Previous research on gaming addiction and Internet addiction (IA) has relied on inconsistent definitions and theoretical frameworks to define these constructs, and has negatively impacted on their assessment. However, the American Psychiatric Association (APA) included 'Internet Gaming Disorder' (IGD) as a tentative disorder in need of further investigation in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Following this preliminary recognition of gaming addiction as potential disorder, unification and standardisation in the field in terms of assessment became possible given that the DSM-5 provided a set of official criteria defining IGD that could be implemented in future research. The research in this thesis substantially contributes to knowledge by (i) systematically reviewing the inconsistencies in the psychometric assessment of IGD and IA, (ii) developing a new potentially unifying standardised psychometric assessment framework for both disorders, and (iii) identifying potential risk factors for IGD and IA. A cross-sectional design was employed across all empirical studies (Study 1, N = 1,003; Study 2, N = 1,060; Study 3, N = 1,105; Study 4, N = 1,100), and the data were analysed using structural equation modelling (i.e., measurement model and latent profile analysis), alongside traditional bivariate statistical modelling. The results indicated that, at a theoretical level, the use of inconsistent assessment tools to investigate IGD and IA has hindered progress in the field. At an empirical level, the Internet Gaming Disorder Test (IGD-20 Test) and the Internet Gaming Disorder Scale–Short-Form (IGDS9-SF) were developed to assess IGD, and the Internet Disorder Scale (IDS-15) and the Internet Disorder Scale–Short Form (IDS9-SF) were developed to assess IA based on the IGD conceptualisation provided by the APA in the DSM-5. Overall, the research in this thesis confirms the usefulness of the utilisation of the IGD framework and the \npsychometric tools developed as a potential avenue to overcome the shortcomings related to previous heterogeneity issues in the assessment of both IGD and IA.
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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 | 0.176 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.008 |
| 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".