Using Cognitive Interviewing to Develop an Online Survey of Parent Perspectives on Data Sharing
Bibliographic record
Abstract
Cognitive interviewing is a qualitative method to identify survey problems. This method can advance survey validity and reliability by incorporating participant perspectives during questionnaire development. Despite its utility, cognitive interviewing is rarely used in pediatric and perinatal epidemiology. This paper discusses the use and implications of cognitive interviewing in the development of an online survey of a complex, uncommon topic in the parenting population: parental perspectives on data sharing with secondary researchers via repositories. Participants were recruited randomly from two Alberta birth cohorts. Participation entailed a one-on-one interview, where participants completed a draft online questionnaire and answered probing questions. The cognitive interviews yielded three major insights for survey improvement. First, the interviewer witnessed varied participant experiences with the survey: some participants enjoyed the process, while others struggled to point of frustration. Reframing the language and adding polar questions aimed to promote comprehension. Second, the topic’s complexity revealed the utility of “educational” questions, which may not provide new information, but would allow participants to think through issues. Third, “educational” questions and sufficiency of background information must be tempered to avoid the survey length being overly-burdensome to participants. By increasing comprehension and lessening frustration, researchers increase the accuracy of data collected from parents on a complex, uncommon topic.
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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 | 0.079 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".