Examining the associations between grit, self-control and sport expertise: A replication study
Bibliographic record
Abstract
To develop expertise, athletes need to amass a high volume of deliberate practice activities over a long duration. Two personality traits that relate to long-term goal pursuits and to achievement within sport are self-control (Tedesqui & Young, 2017a) and grit (Tedesqui & Young, 2017b). We compared the contribution of grit and self-control facets to explain criteria of sport expertise development. Athletes (n = 164, 87 female, Mage = 31.62, SD = 12.45) completed survey items assessing grit (perseverance of effort; consistency of interests) and self-control (self-discipline; impulse control), questions to determine skill group (beginner/intermediate; advanced; expert), and sport-specific practice amounts. We submitted all scale scores to criterion validity tests for group discrimination and associations with practice. Separate MANCOVAs for grit and self-control variables (controlling for age) showed grit variables significantly distinguished higher from lower skill groups, Pillai's Trace = .06, F(4, 314) = 2.48, p < .05, partial eta-squared = .03. Post-hoc tests showed only perseverance of effort distinguished groups, F(2, 157) = 5.08, p < .01, partial eta-squared = .06 (Mbeginner/intermediate = 4.37, Madvanced = 4.25, Mexpert = 4.58). Although we replicated prior effects of perseverance of effort on skill groups, we failed to replicate any associations with practice. The tendency to persevere in long-term goals despite setbacks might enable athletes to achieve higher skill levels in pursuit of expertise development. We problematize the non-significant findings regarding practice.Acknowledgments: This research was supported by a Social Sciences and Humanities Research Council of Canada (SSHRC) funding (430-215-00904) to Bradley W. Young and Joseph Baker.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".