Toward the Development of a Subgroup Questionnaire in Sport
Bibliographic record
Abstract
An emerging body of literature has noted the salience of subgroups in sport, emphasizing important implications for athletes and teams (Martin et al., 2020). With the advancement of a conceptual framework for subgroups (McGuire et al., 2021), a warranted next step is to advance a psychometrically sound questionnaire. Measuring subgroups is nevertheless complex, given the need for an innovative tool that describes not only the distribution of teammates into subgroups but also perceptions of how subgroups relate to member behaviour. Thus, a collaborative, iterative, and critical process that differs from how many self-report tools are developed is needed for measurement development. Purpose: To initiate the development of a subgroup measure in sport. Methods: A three-phase process was adopted that involved literature/questionnaire reviews and research team collaborative meetings (Phase 1), topic expert document review and semi-structured interviews (Phase 2; N = 5), and athlete think-aloud interviews (Phase 3; N = 7). Results: Phase 1 resulted in a proposed subgroup questionnaire, with Phases 2 and 3 leveraging the knowledge of experts and experiences of athletes beyond the approval of items for representativeness and readability. Specifically, changes to the questionnaire structure (e.g., context descriptions) and participant orientation were made. In addition, considerations for including more in-depth discussions with experts and athletes will be put forward (e.g., discussing expert intention and conducting virtual think-aloud interviews). Implications: This study contributes to the advancement of the subgroup literature and provides an example of how expert input can be further incorporated into questionnaire development protocols.
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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 | 0.139 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".