A different perspective: Confirmatory factor analysis of a team sport imagery questionnaire
Bibliographic record
Abstract
Imagery has been shown to improve sport performance, and performance related behaviours and cognitions for athletes competing in both independent and interdependent sports (Munroe et al., 2000). Although imagery is an individual cognition, it has shown positive relationships with group based cognitions including cohesion (Hardy et al., 2003) and collective efficacy (Shearer et al., 2007). However, a potential limitation with those studies is the use of the Sport Imagery Questionnaire (Hall et al., 1998) given it measures imagery use from an individual perspective. Shearer et al. (2007) suggested that it may be advantageous to measure imagery use within interdependent sport teams from a team perspective. Therefore, the purpose of this study was to explore the construct validity of a team sport version of the Sport Imagery Questionnaire (SIQ-TS), which assessed imagery from a team-level perspective. Participants included 284 interdependent sport team athletes (male n = 177, female n = 107) with an average age of 22.89 years (SD = 5.88). Model fit statistics (i.e., χ2, RMSEA, SRMR, CFI, TLI) for the hypothesized 5 factor model was examined using CFA. Four items were deleted from the 30 item SIQ-TS yielding acceptable model fit and reliability (α’s = .76 - .86) for each subscale. Possible research applications of the SIQ-TS for interdependent sport team athletes are discussed.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".