Methodological and Practical Challenges in Longitudinal, Large-Scale, Collaborative Questionnaire Survey Research
Bibliographic record
Abstract
This paper presents an experience report on the de-sign and deployment of a large-scale, longitudinal questionnaire survey aimed at understanding the factors influencing voluntary job turnover among software professionals. Rather than the specific topic of the study, this paper discusses the main challenges encountered, including the complexities of longitudinal survey design, issues raised by large-scale collaboration, ensuring par-ticipant diversity, measuring industry-specific factors for which good scales do not exist, and managing uncertainties in data collection. By discussing these challenges and our strategies in addressing them, this work aims to encourage large-scale survey research in the field of software engineering, and to provide practically useful advice for implementing such projects.
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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 | 0.707 | 0.741 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".