Exploratory factor analyses and initial inspection of coaches' responses to a survey of adult-oriented coaching practices
Bibliographic record
Abstract
Qualitative research has shown that adults athletes have particular preferences for how they want to be coached (Callary, Rathwell & Young, 2015; Ferrari, Bloom, Gilbert & Caron, 2016), which are often discrepant from dominant approaches with younger cohorts (Callary, Rathwell & Young, 2017). A prior presentation outlined the process by which information about adults' preferences was articulated as items and vetted for content validity. This study examined the initial factor structure of these survey items using 185 coaches' (91 m, 94 f; M yrs coaching adults = 12.9, derived from USA, Canada, UK and Australia) responses relating to adult-oriented sport coaching practices. Coaches responded to 51 items representing 13 initial factors (6 factors for 'accommodating adults', 7 factors for 'coaching behaviours and attributes'). An initial exploratory factor analysis (MLE with oblique rotation, forced 13-factor model) fell short of criteria for good fit, chi square (690) = 1072.3, p = .00, CFI = .85, TLI = .73, RMSEA = .06 [90% CI = .060 - .075], SRMR = .034. Factor loadings indicated 13 flagrant items and modification indices suggested many issues with cross-loading. Consequently, exploratory structural equation modeling was employed to refine the model, resulting in seven factors, with reduced multicollinearity and divergent validity. Discussion focuses on the nature of the resulting survey instrument, including: (a) how it captures pertinent categories for Masters coaching self-assessment; and (b) its utility for examining congruency associations between coach-report and athletes' perceptions of adult-appropriate coaching behaviours, and whether congruency is associated with quality sport experiences.
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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 | 0.029 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".