Examining the factor structure of the Motives for Playing Drinking Games measure among Australian university students
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Given the prevalence of drinking games among university students and the health risks associated with playing drinking games, it is important to consider motivations for participation. The psychometric properties of the Motives for Playing Drinking Games (MPDG) measure have been examined among US college student samples, but mixed findings have been reported regarding the number of factors in the measure. This is the first study to examine the factor structure and applicability of the MPDG measure among a sample of Australian university students. DESIGN AND METHODS: University students (N = 254; aged 18-46 years; 62% female) with prior drinking experience completed an online survey which included questions pertaining to demographics, drinking game frequency and consumption, drinking game consequences and the MPDG measure. RESULTS: Confirmatory factor analyses demonstrated that the originally proposed 8-factors within the MPDG measure were problematic in the current sample and a revised 7-factor solution was preferred. Analyses examining the relations of the revised 7 MPDG factors with drinking game behaviours (e.g. gaming-specific consequences and amount consumed during play) highlighted the importance of some MPDG (enhancement/thrills, conformity and sexual pursuit motives). DISCUSSION AND CONCLUSIONS: While the MPDG measure shows promise for assessing drinking game-specific motives, the need to consider the applicability of MPDG subscales across different samples was apparent.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".