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.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".