How Do Researchers Question Children and Adolescents? A Systematic Assessment of Developmental Research Methods
Bibliographic record
Abstract
Both the kinds of exchanges and the context under which children are questioned may affect the quality of data. Yet, little is known about how developmental scientists communicate with children for research. Using manifest content analysis, the 3,119 manuscripts published in the top 20 developmental outlets in 2018 were coded for methodology, examining whether researchers communicated directly with children, how they did so, and how they contextualized questioning. We found that over 65% of empirical publications presenting new data questioned children. Researchers used a variety of methodologies (e.g., 64% questionnaires, 51% assessments, 5% interviews). As age increased, the odds of giving children standardized questionnaires, closed-ended questions, and Likert-type questions increased. Researchers rarely reported how they contextualized questioning and rarely utilized supplemental materials. Researchers should consider collecting more qualitative data, better reporting methodologies, and utilizing online spaces to share supplemental materials. These modifications can ensure that we produce the strongest data.
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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 | 0.673 | 0.767 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".