Cognitive interviews with children as a research tool for instrument validation in adapted physical activity
Bibliographic record
Abstract
The development and use of instruments to assess individuals with impairments in various domains is common practice in the field of Adapted Physical Activity. Test developers and users often question the validity of their instruments and use different conceptual and/or empirical strategies for validation purposes. One validation strategy, still rarely used in the sport sciences, is cognitive interviews with participants. This study is an attempt to show the utility of cognitive interviews for instrument development with children with relevance to the field of adapted physical activity. Â Specifically, we investigated the question-and-answer processes of children with impairments when responding to the Athletic Competence Domain Subscale from Harter’s (1985) Self-Perception Profile for Children. Eight children with different diagnoses (ages 8-13 years) took part in cognitive interviews. The study revealed sources of validity and invalidity in the instrument. Differences and concerns in the children’s comprehension of questions and interpretation of words, leading to potentially limited and varied sources of information for response production, emerged. While judgment generally appeared to be unproblematic, topic sensitivity and limited response options were a common constraint. The data obtained from this study could be used to further refine the instrument.
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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 | 0.156 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".