Validating the GMQ-F in a Canadian Sample of University Students
Bibliographic record
Abstract
This study assesses the psychometric properties of the Gambling Motivation Questionnaire for Financial Motivations (GMQ-F) in a Canadian sample of emerging adult university students. Confirmatory factor analysis was first used to test a 16-item GMQ-F for model fit, and then multiple regression analysis was used to test the predictive utility of the four subscales/factors. Results confirmed that the GMQ-F is a valid and reliable measure of university student gambling motivations, in accordance with the four-factor structure proposed by Dechant (2014) despite poor initial results. Regression results were mixed: Three of the four subscales (enhancement, social, and coping) were significant predictors of problem gambling and gambling variety, only the enhancement and coping subscales were significant predictors of gambling frequency, and none of the subscales significantly predicted average monthly expenditure on gambling. The implications of this study support further refinement of the GMQ-F, as differing results from the current study and from prior work on normative samples point to a different reliance on motivational categories or different motivational categories entirely.RésuméCette étude évalue les propriétés psychométriques de l’échelle de motivations financières relatives aux jeux de hasard et d’argent (GMQ-F) auprès d’un échantillon canadien d’étudiants universitaires jeunes adultes. La première analyse factorielle confirmatoire (AFC) a été utilisée pour tester l’ajustement du modèle du GMQ-F à 16 éléments, puis une régression multiple a été utilisée pour tester l’utilité prédictive des quatre sous-échelles/facteurs. Les résultats ont confirmé que le GMQ-F est une mesure valide et fiable des motivations au jeu des étudiants universitaires, suivant la structure à quatre facteurs proposée par Dechant (2014), malgré de faibles résultats initiaux. Les résultats de la régression étaient mitigés : trois des quatre sous-échelles (amélioration, aspect social et adaptation) étaient des prédicteurs significatifs du jeu problématique et de la variété de jeu; tandis que seules l’amélioration et l’adaptation étaient des prédicteurs significatifs de la fréquence du jeu. Aucune des sous-échelles n’a prédit de manière significative les dépenses mensuelles moyennes consacrées aux jeux de hasard. Ce projet a pour effet d’encourager une amélioration du GMQ-F, car la différence des résultats entre l’étude actuelle et des travaux antérieurs sur des échantillons normatifs indique une dépendance différente à l’égard des catégories de motivation, voire différentes catégories de motivation entièrement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".