Preliminary investigation of the validity of the instructional perspective inventory with an international sample of masters coaches
Bibliographic record
Abstract
Callary et al. (2017) advocated for the incorporation of andragogic principles when investigating coaching practices related to Masters athletes. No valid survey instruments exist for examining adult-learning principles in the coached Masters sport context. Targeted literature searches within Google Scholar, Pubmed, Psychinfo, Jstor, and Proquest uncovered 13 tools for assessing andragogy outside of sport. We identified the Instructional Perspectives Inventory (IPI; Henschke, 1989; Lubin, 2013) for use within sport because (a) it is the only andragogic tool that measures coaching behaviours from the perspective of the coach, and (b) there is preliminary evidence of reliability, content and factor validity. Our study investigated the suitability of a sport-modified IPI for assessing Masters sport coaches' use of andragogic principles with their athletes. We detail our initial process of modifying items and vetting content validity, with researchers (n = 3) and coaches (n = 12), to ensure relevance to the sporting context. Next, we analyzed 185 sport coaches' (51 % female; M age = 53.4 yrs, range 19 – 87) responses on the modified IPI, which comprised 38 items. Exploratory Factor Analyses (oblique rotations) assessed Lubin's (2013) 8 factor structure. Results showed good model fit: CFI = .903, SRMR = .042, RMSEA = .054 (90% CI = .042 – .065), ?² (703) = 2260.299, p < .001, and ?²/df = 3.215. However, two items failed to load on any factor, and nine had problematic cross-loadings. Subsequent Exploratory Structural Equation Modeling analyses suggested a 27 item solution, loading onto 3 factors based on adult coaches' data.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".