A confirmatory factor analysis of a revised motives for playing drinking games (MPDG-33) scale among university students in the United States
Bibliographic record
Abstract
AIM: Participating in a drinking game (DG) is common practice among university students and can increase students' risk for heavy drinking. Given the theoretical link between motivations to drink and alcohol use, careful consideration should be given to students' motivations to play DGs. In this study, we examined the factor structure, internal consistency, and concurrent validity of a revised version of the motives for playing drinking games (MPDG) scale, the MPDG-33. METHODS: University students (n = 3345, Mage = 19.77 years, SDage = 1.53; 68.8% = women; 59.6% = White) from 12 U.S. universities completed a confidential online self-report survey that included the MPDG-33 and questions regarding their frequency of DG participation and typical drink consumption while playing DGs. RESULTS: Confirmatory factor analysis indicated the 7-factor model fit the data adequately, and all items had statistically significant factor loadings on their predicted factor. All subscales had adequate to excellent internal consistency and were positively correlated with the frequency of DG participation and the typical number of drinks consumed while playing DGs (though the correlations were small). CONCLUSION: Findings suggest that the MPDG-33 can be reliably used in research and clinical settings to assess U.S. university students' motives for playing DGs.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".