Online Surveys: Effect of Research Design on Rates of Invalid Participation and Data Credibility
Bibliographic record
Abstract
Designing online research often involves a trade-off between procedures that maximize administration efficiency and those that encourage valid and considerate participation. This article reports on three different online research design conditions (n = 413), differing in participant screening, incentive, and anonymity, that were used in a separate study on cognition in smoking cessation. High rates of invalid participation were observed in a condition in which participants participated anonymously, received a $20 incentive, and were screened for eligibility online. In addition, this condition produced significantly different data (variance, covariance, and central tendency) than the other two conditions, which involved less incentive or personal eligibility screening without participant anonymity. Removal of apparent “invalid” participants on the basis of a data screening protocol corrected some, but not all, of these differences. Results indicate that online designs offering monetary incentives should implement procedures to enhance data integrity even at the cost of increased participation barriers.
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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 | 0.201 | 0.032 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; 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".