Examining the Psychometric Properties of the Behavioral Regulation in Exercise Questionnaire
Bibliographic record
Abstract
The purpose of this study was to examine select psychometric properties of the Behavioral Regulation in Exercise Questionnaire (BREQ; Mullen, Markland, & Ingledew, 1997). To accomplish this aim, data were gathered in 2 phases to evaluate the BREQ's factorial composition and structure and the relation between the BREQ, need satisfaction, exercise behavior, and relevant motivational constructs. Participants completed measures assessing psychological need satisfaction, optimism, perceived behavioral control, exercise behavior, and the BREQ. Phase 1 results supported the original 4-factor measurement model and a simplex model of structural relations between latent BREQ constructs suggested within self-determination theory (Deci & Ryan, 1985), and results supported the positive relations between more self-determined motives and greater psychological need satisfaction and frequent exercise behavior. Phase 2 results provided further construct validity evidence for the BREQ by linking subscale scores with greater perceived behavioral control in a manner consistent with theory (Deci & Ryan, 1985). Collectively, these results further support the construct validity of the BREQ and lend credence to the notion of measuring exercise motivation from a multidimensional perspective using self-determination theory as a guiding framework in the exercise domain.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".