Assessment of the Psychometric Properties of the Drinking Motives Questionnaire – Revised Among Irish Drinkers
Bibliographic record
Abstract
Abstract. Motives ascribed to drinking represent an important area of investigation in alcohol research. The most commonly used measure is the 20-item Drinking Motives Questionnaire – Revised (DMQ-R: Cooper, 1994 ), which assesses four motives: Enhancement, Social, Coping, and Conformity. Although researchers in Europe have begun to assess the DMQ-R, to date, no published assessment has been undertaken among English-speaking, non-American samples. The current study addressed this omission by conducting exploratory ( N = 437) and confirmatory ( Ns = 437 and 344) factor analyses with Irish participants. A three-factor solution was optimal: Coping (four items), Conformity (five items), and Positive Motives (seven items). The need to conduct culturally specific psychometric testing is discussed as are directions for future research.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".