Assessing a replication of the self-regulation of sport practice-short form survey
Bibliographic record
Abstract
Self-regulated learning (SRL) is a potential antecedent of the development of sport expertise (McCardle et al., 2017). The Self-Regulation of Sport Practice survey (SRSP; Wilson et al., 2021) assesses SRL—the awareness and control of self-processes in pursuit of practice goals—among athletes. A short form of the survey (SRSP-S; Wilson et al., 2019) was created with two factors representing 'motivational' and 'metacognitive' SRL processes, which were both positively associated with athlete skill levels. The main purpose of this study was to replicate factorial and criterion validity (skill group discrimination) for the SRSP-S. Competitive athletes (N = 162; M Age = 20.8, SD = 4.4) from three skill levels (provincial, national, and international) completed the SRSP-S survey. Acceptable confirmatory factor analysis fit indices (RMSEA = .057, SRMR = .063, CFI = .905) replicated a two-factor solution. A MANOVA tested criterion validity by comparing SRL scores across the three skill levels, finding no main effect, Wilk's lambda = .98, F(4,238) = 0.73, p = .571, partial-eta-squared = .012. Following preliminary analysis, a 2-way MANOVA considering sport type (individual vs. team) showed significant differences in motivational scores (p = .012), but neither skill level nor the interaction term were significant, ps > .211). The SRSP-S's two-factor structure was replicated, but counter to hypothesis, criterion validity was not supported in this sample. This presentation will explore potential explanations for this unexpected finding and implications for the SRSP-S's alternative use as a conversation tool for coaches and mental performance consultants and their athletes.Acknowledgments: This project is supported in part by funding from the Social Sciences and Humanities Research Council of Canada.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".