Motives to play videogames across seven countries: Measurement invariance of the Videogaming Motives Questionnaire
Bibliographic record
Abstract
INTRODUCTION: Gaming motives appear to be an important predictor of time spent gaming and disordered gaming. The Videogaming Motives Questionnaire (VMQ) has shown adequate psychometric properties to assess gaming motives among Spanish college students. However, the utility of this measure has not yet been explored in other cultures. This research aimed to examine the structure and measurement invariance of the VMQ across seven countries and gender groups, and to provide criterion-related validity evidence for VMQ scores. METHOD: College students who reported having played videogames in the last year (n = 5192; 59.07 % women) from the US, Canada, South Africa, Spain, Argentina, England, and Uruguay completed an online survey to measure time spent gaming, disordered gaming, and the VMQ. RESULTS: Findings support a 24-item 8-intercorrelated factor model structure for the VMQ in the total sample. Our results also support configural, metric, and scalar invariance of the VMQ across gender groups and countries. Students from North America (US and Canada) scored higher on most gaming motives (except recreation and cognitive development) than students from the other countries. The correlations between VMQ and non-VMQ variables were similar across gender and countries, except in England where VMQ correlations with time spent gaming were stronger. DISCUSSION: These results suggest that the VMQ is a useful measure for assessing gaming motives across young adults from different countries.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".