The Effect of Including a Monetary Motive Item on the Gambling Motives Questionnaire in a Sample of Moderate Gamblers
Bibliographic record
Abstract
This study explored the factor structure of the Gambling Motives Questionnaire (GMQ) with a large stratified sample of 839 moderate gamblers (49% female; median age category = 45-54 years) and examined the effect of including a monetary motive item on GMQ factor structure. Participants responded to a telephone survey in which they were asked how often they gamble for each of 16 reasons, including the 15 GMQ motives and an additional motive: "to win money". Exploratory principal components analysis of the 15 GMQ items revealed three factors, together accounting for 49.04% of the total variance in GMQ scores. The factors tapped enhancement, coping and social motives, although only the coping subscale displayed strong internal consistency. A second exploratory principal components analysis of the 15 GMQ items and the monetary motive item continued to reveal three factors tapping enhancement, coping and social motives. The addition of the monetary motive item strengthened the independence of the components and dramatically improved the internal consistency of the enhancement factor. The results suggest that the psychometric properties of the GMQ, when used with a population of moderate gamblers, may be considerably strengthened with only minor modifications.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".