Exploring and Evaluating the Two-Factor Model of Perfectionism in Sport
Bibliographic record
Abstract
Perfectionism is a multidimensional personality trait with two higher-order dimensions; perfectionistic strivings and perfectionistic concerns. The purpose of the present study was to explore and evaluate the two-factor model for the first time using three instruments developed to measure perfectionism in sport. In doing so, we (i) assessed the fit of two-factor models when including and excluding various contentious subscales (other-oriented perfectionism, parental pressure, coach pressure, organisation, and negative reactions to imperfection) and (ii) compared two-factor models to alternative one-factor (or unidimensional) models. Participants were recruited from community and university sports clubs in the UK ( N = 527; M age = 18.07 years, SD = 0.49) and completed the Sport-Multidimensional Perfectionism Scale-2, the Multidimensional Inventory of Perfectionism in Sport, and the Performance Perfectionism Scale-Sport. Support was found for the two-factor model, with superior fit displayed each time the aforementioned subscales were excluded and, in all cases, when compared to a unidimensional model. The findings suggest that the two-factor model is an adequate representation of the underlying structure of instruments designed to measure perfectionism in sport with better fit and conceptual clarity offered by more parsimonious models.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".