Compensating for Low Topic Interest and Long Surveys
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
Certain survey characteristics proven to affect response rates, such as a survey's length and topic, are often under limited control of the researcher. Therefore, survey researchers sometimes seek to compensate for such undesired effects on response rates by employing countermeasures such as material or nonmaterial incentives. The scarce evidence on those factors' effects in web survey contexts is far from being conclusive. This study is aimed at filling this gap by examining the effects of four factors along with selected interactions presumed to affect response rates in web surveys. Requests to complete a web-based, self-administered survey were sent to 2,152 owners of personal websites. The 2 × 2 × 2 × 2 fully crossed factorial design encompassed the experimental conditions of (a) high versus low topic salience, (b) short versus long survey, (c) lottery incentive versus no incentive, and (d) no feedback and general feedback (study results) versus personal feedback (individual profile of results). As expected, highly salient and shorter surveys yielded considerably higher unit-response rates. Moreover, partial support was found for interaction hypotheses derived from the leverage-salience theory of survey participation. Offering personalized feedback compensated for the negative effects of low topic salience on response rates. Also, the lottery incentive tended to evoke more responses only if the survey was short (versus long), but this interaction effect was only marginally significant. The results stress the usefulness of a multifactorial approach encompassing interaction effects to understand participation differences in web surveys.
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 | 0.121 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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 it