The Relationship Between Athletic Identity and Motivation in Masters Athletes
Bibliographic record
Abstract
Athletic identity (AI) is associated with increased motivation across a broad age range of exercisers. The study of AI and Masters athletes (MAs) has received less attention. The relationship between AI and motivation was tested with a large sample of MAs (n = 455; Mage = 51.97, SD = 11.51). MAs completed the Athletic Identity Questionnaire (AIQ) and Behavioral Regulation in Sport Questionnaire (BRSQ). Confirmatory factor analyses supported the four-factor (i.e., appearance, importance, competence, encouragement) structure of the AIQ (X2(183) = 384.02, p < .005, CFI = .944, RMSEA = .049) and the six-factor (i.e., intrinsic, integrated, identified, introjected, external and amotivation) structure of the BRSQ (X2(237) = 646.26, p < .005, CFI = .872, RMSEA = .062). The structural model (X2(900) = 1682.28, p < .005, CFI = .901, RMSEA = .044) showed significant relationships between the importance of sport and MAs' intrinsic (B = .167), integrated (B = .227), identified (B = .249), and external (B = -.239) motives. MAs' competence was related to intrinsic (B = .171), identified (B = .173), introjected (B = -.312) motives, and amotivation (B = -.229). MAs' perceived encouragement was related to integrated (B = .127), identified (B = .169), and introjected (B = .124) motives, and amotivation (B = -.137). Generally, when MAs identify strongly with their athletic role, they are also likely to have high levels of self-determined and low levels of non-self-determined motives for sport. The results are encouraging considering the established link between self-determined motives and sport commitment.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".