Coaching competency and satisfaction with the coach: A multi-level structural equation model
Bibliographic record
Abstract
The purpose of this initial predictive validity study was to determine the ability of measures derived from the Athletes' Perceptions of Coaching Competency Scale II - High School Teams (APCCS II-HST) to predict satisfaction with the head coach. Specification of the statistical model was informed by the mediational model of coach-athlete interactions. The technical quality of the satisfaction measure was evaluated before testing the predictive validity of the coaching competency measures. Data were collected from athletes of seven sports. Athlete observations (N = 748) were clustered within teams (G = 74). Multi-group confirmatory factor analyses (CFA) provided evidence for factorial invariance of a reduced version of the satisfaction measure by athlete gender. Multi-level CFA provided evidence of model-data consistency for a reduced version of the satisfaction measure. Multi-level structural equation modelling provided evidence for the ability of latent coaching competency to positively predict latent satisfaction at both the athlete level (technique competency and motivation competency) and the team level (coaching competency) and for close model-data fit. Implications of this study include: that the APCCS II-HST should be viewed as a replacement for the Coaching Competency Scale when the intended population is appropriate; a preliminary multi-level measurement model for satisfaction with one's coach that should be considered as a potential starting point in subsequent studies; and empirical support for a key relationship proposed in the mediational model of coach-athlete interactions.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".