Confirmatory Factor Analysis of the Athlete Sleep Behavior Questionnaire
Bibliographic record
Abstract
CONTEXT: Sleep has long been understood as an essential component for overall well-being, substantially affecting physical health, cognitive functioning, mental health, and quality of life. Currently, the Athlete Sleep Behavior Questionnaire (ASBQ) is the only known instrument designed to measure sleep behaviors in the athletic population. However, the psychometric properties of the scale in a collegiate student-athlete and dance population have not been established. OBJECTIVE: To assess model fit of the ASBQ in a sample of collegiate traditional student-athletes and dancers. DESIGN: Observational study. SETTING: Twelve colleges and universities. PATIENTS OR OTHER PARTICIPANTS: A total of 556 (104 men, 452 women; age = 19.84 ± 1.62 years) traditional student-athletes and dancers competing at the collegiate level. MAIN OUTCOME MEASURE(S): A confirmatory factor analysis (CFA) was computed to assess the factor structure of the ASBQ. We performed principal component analysis extraction and covariance modeling analyses to identify an alternate model. Multigroup invariance testing was conducted on the alternate model to identify if group differences existed for sex, sport type, injury status, and level of competition. RESULTS: The CFA on the ASBQ indicated that the model did not meet recommended model fit indices. An alternate 3-factor, 9-item model with improved fit was identified; however, the scale structure was not consistently supported during multigroup invariance testing procedures. CONCLUSIONS: The original 3-factor, 18-item ASBQ was not supported for use with collegiate athletes in our study. The alternate ASBQ was substantially improved, although more research should be completed to ensure that the 9-item instrument accurately captures all dimensions of sleep behavior relevant for collegiate athletes.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".