Initial development of the Dance Imagery Questionnaire for Children (DIQ-C): establishing content validity
Bibliographic record
Abstract
Abstract Given the differences between young dancers’ and adult dancers’ use of imagery, a valid and reliable questionnaire specific to young dancers was necessary. The current study is the first phase of a multi-phase study in the development of the Dance Imagery Questionnaire for Children (DIQ-C). Specifically, the purpose of this study was to establish content validity of the DIQ-C. This was achieved through the following three stages: (1) definition, item, and scale development, (2) assessment of item clarity and appropriateness via cognitive interviews, and (3) assessment of item-content relevance via an expert rating panel. Guided by previous qualitative research with young dancers, 46 items representing seven subscales (i.e., imagery types) were developed. The initial item pool was then implemented during cognitive interviews with 16 dancers (15 females; M age =10.63, SD=1.82), which led to the removal of 13 items and the modification of 21 items. Consequently, the revised 33-item pool was then administered to an expert panel of four imagery researchers and four dance instructors to measure item-content relevance. This resulted in the removal of eight items, the revision of four items, and the merging of two subscales. Overall, the current study provides content validity evidence for a 25-item pool (representing five subscales) to be used in further development of the DIQ-C (i.e., identifying and establishing factor structure).
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".