Factorial validity and measurement invariance of the Athlete Burnout Questionnaire (ABQ)
Bibliographic record
Abstract
The Athlete Burnout Questionnaire (ABQ) is the gold standard measure for burnout in athletes. However, previous assessments of factorial validity have: (a) tested overly restrictive measurement models; (b) provided mixed support for factorial validity; and (c) not been applied to assess measurement invariance across gender, sport type, or age. To address these issues, we used ABQ data provided by 914 athletes (Mage = 21.75 years, SD = 8.79) and examined factorial validity using confirmatory factor analysis (CFA) and exploratory structural equation modelling (ESEM) techniques. We also examined measurement invariance of the ABQ data across reported gender (female, male), sport type (individual, team), and age (≤18 years, >18 years) groups. The analyses revealed that an ESEM model provided superior fit over the corresponding CFA model. In terms of measurement invariance, support was provided for the equivalence of the ABQ across each group. This means that researchers using the ABQ can collect data across these groups and examine potential differences with confidence that the ABQ is approximately invariant. In all, we provide evidence that the majority of ABQ items are key target construct indicators and the burnout construct (as measured by the ABQ) has the same structure and meaning to different athlete groups.
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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 | 0.020 | 0.042 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".