The Modified Gambling Motivation Scale: Confirmatory Factor Analysis and Links With Problem Gambling
Bibliographic record
Abstract
The Gambling Motivation Scale (GMS), a scale based on self-determination theory, consists of seven motivations: to learn the game, to feel competent, to experience excitement, to socialize, to feel important, to win money, and to continue gambling aimlessly (Chantal, Vallerand, & Vallieres, 1994). The GMS has never been tested with confirmatory factor analysis to determine the appropriate structure of gambling motivation. In the present study, we developed the Modified Gambling Motivation Scale (MGMS) to improve the reading comprehension and psychometrics of the GMS. We also proposed a simpler interpretation of motivation scores than that applied to the previous scoring system. Confirmatory factor analysis, structural equation modelling, and measurement invariance were performed on the GMS and the MGMS, which suggested that six motivations were distinct and important to gambling behaviour: to experience an intellectual challenge (combined motivations to learn and to feel competent), to experience excitement, to socialize, to feel important, to win money, and to continue gambling aimlessly. This six-factor structure of gambling motivation aligns more closely with self-determination theory and removes problems with estimations in the seven-factor structure. The results showed that gamblers who were motivated to experience excitement and to socialize had more problem gambling than did other gamblers.RésuméL’Échelle de motivation envers les jeux de hasard et d’argent, fondée sur la théorie de l’autodétermination, est constituée de sept motivations : apprendre le jeu, se sentir compétent, vivre une expérience excitante, socialiser, se sentir important, gagner de l’argent et continuer à jouer pour le simple plaisir (Chantal, Vallerand, et Vallières, 1994). La structure de l’échelle n’a jamais été soumise à des tests avec analyse factorielle confirmatoire pour déterminer la structure appropriée de la motivation du jeu. Dans la présente étude, nous avons mis au point une échelle modifiée de motivation de jeu pour améliorer la compréhension de la lecture et la psychométrie de l’échelle. Nous proposons également une simplification de l’interprétation des pointages de motivation par rapport au système précédent. L’analyse factorielle confirmatoire, la modélisation de l’équation structurelle et l’invariance de mesure ont été réalisées sur les deux échelles de motivation, et les résultats démontrent que six motivations étaient distinctes et importantes pour le comportement du jeu. Il s’agissait des motivations relatives au défi intellectuel (combinées à des motivations d’apprendre et de se sentir compétent), de vivre une expérience excitante, de socialiser, de se sentir important, de gagner de l’argent et de continuer à jouer pour le simple plaisir. Cette structure de motivation envers les jeux à six facteurs correspond davantage à la théorie de l’autodétermination et supprime les problèmes d’estimation avec la structure à sept facteurs. Les résultats ont montré que les joueurs qui étaient motivés à vivre une expérience excitante et à socialiser avaient plus de problèmes de jeux compulsifs.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".