IMPACT OF LOTTERY INCENTIVE ON RESPONSE RATE AND DATA QUALITY: EVIDENCE FROM ORGANIC FOOD CONSUMPTION SURVEY OF CONVENTIONAL SHOPPERS
Bibliographic record
Abstract
Incentives of different forms and at different stages are used for motivating people to participate in human subject research. Although it is widely accepted that incentives, in general, play a positive role in increasing participation rate and are widely used, there are exceptions that they may not increase response rate and may even contaminate the quality of data resulting in poor research findings. This study examines the impact of pre- and post-disclosed committed lottery incentives on response rate and data quality in a face-to-face survey of conventional consumers for organic food consumption. A survey was conducted at the premises of four conventional grocery stores in Edmonton, Alberta, Canada. Half of the randomly approached and agreed upon respondents were disclosed the lottery incentives at the beginning, and the rest half were told at the end. Data quality was measured using three indicators – edit occurrences, imputation occurrences, and proportion of incomplete answers. Our study finds little difference in response rate between pre- and post-disclosed committed lottery payments. However, the useability of incomplete questionnaires among post-disclosed lottery was significantly higher than those of pre-disclosed. Our study also shows that people with likings of organic food and buying organic food more frequently are likely to offer a better quality of information.
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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 | 0.127 | 0.380 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".