Guidelines for online questionnaire design
Bibliographic record
Abstract
As a new medium for questionnaire delivery, the internet has the potential to revolutionize the survey process. Online (web-based) questionnaires provide several advantages over traditional survey methods in terms of cost, speed, appearance, flexibility, functionality, and usability. Designers of online-questionnaires are faced with a plethora of design tools to assist in the development of their electronic questionnaires. Little, if any, support is incorporated, however, within these tools to guide online-questionnaire designers according to best practice. In essence, an online-questionnaire combines questionnaire-based survey functionality with that of a webpage/site. As such, the design of an online-questionnaire should incorporate principles from both contributing fields. Drawing on existing guidelines for paper-based questionnaire design, website design (paying particular attention to issues of accessibility and usability), and existing but scarce guidelines for electronic surveys, we have derived a comprehensive set of guidelines for the design of online-questionnaires. This article introduces this comprehensive set of guidelines – as a practical reference guide – for the design of online-questionnaires.
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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 | 0.126 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.065 | 0.055 |
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".