Athlete Satisfaction and Leadership: Assessing Group-Level Effects
Bibliographic record
Abstract
In a group context (e.g., athletic team), group-level effects may be present in constructs typically measured at the individual level (e.g., athlete satisfaction, leadership behavior). If a group-level effect is present, constructs should be analyzed using the group as the unit of analysis and failure to do so can lead to skewed relationships with other constructs. The purpose of this study is to examine the existence and magnitude of group-level effects when athletes rate athlete satisfaction and leadership behavior. The authors hypothesize: (a) group-level effects emerge when group members rate a shared property of the group, and (b) group-level effects may be present when group members rate an individual-level construct that exist within the context of the group. A total of 212 team athletes (members of 16 interactive athletic teams; mean age 20.1 ± 1.96 years) completed subscales of the Leadership Scale for Sports and the Athlete Satisfaction Questionnaire. Results show large group-level effects for all leadership behavior dimensions and satisfaction dimensions associated with group-level constructs, whereas smaller group-level effects were found for satisfaction dimensions associated with individual-level constructs. The results support the hypotheses that group-level effects can emerge for constructs previously viewed solely as individual-level constructs when measured in a group setting. Recognition of these effects should play a factor in determining the appropriate unit of analysis. Implications for handling groups without a group-level effect, while the majority of groups show an effect, are also 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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".