Pilot Testing as a Strategy to Develop Interview and Questionnaire Skills for Scholar Practitioners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This essay presents the reflections of four Education Doctorate (EdD) students on the pilot testing strategies used during an online research methods course. Rigorous questionnaire and interview development skills are challenging to acquire. Pilot testing is an under-researched stage of instrument design, yet it is crucial to ensure validity and reliability, reduce bias, and psychologically prepare researchers for data collection. A structured, multi-step pilot testing process led to the collective development of stronger scholar-practitioner identities, the use of innovative synchronous/asynchronous methods during COVID-19 and increased academic rigor. These reflections demonstrate how several types of pilot testing can support the development of rigorous data collection instruments and prepare post-graduate students for the psychological and technical challenges they may encounter in future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.223 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it