The Impact of Alternative Incentives on Response and Retention in a Mixed-Mode Survey
Bibliographic record
Abstract
We examine the influence of incentives on response, retention, drop-out, completeness and speed of response, consistency of response and respondent characteristics in a mixed-mode survey in which initial contact was via regular mail and respondents completed the survey online. We study four incentive groups: no incentive, prepaid incentive ($2), low promised incentive (lottery, 10 @ $25), and high promised incentive (lottery, 2 @ $250). Prepaid incentives extract the highest response and retention rates compared to no incentive and both promised lottery incentives. Lotteries only increase response and retention rates when of high value. High-prize lotteries result in speedier response while low-prize lotteries decrease response consistency. Cost-effectiveness analysis indicates that the high-prize lottery incentive was most cost-effective per completed survey.
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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 | 0.156 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads 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".