Persistence and Attrition among Participants in a Multi-Page Online Survey Recruited via Reddit’s Social Media Network
Bibliographic record
Abstract
Participant attrition is a major concern for the validity of longer or complex surveys. Unlike paper-based surveys, which may be discarded even if partially completed, multi-page online surveys capture responses from all completed pages until the time of abandonment. This can result in different item response rates, with pages earlier in the sequence showing more completions than later pages. Using data from a multi-page online survey administered to cohorts recruited on Reddit, this paper analyses the pattern of attrition at various stages of the survey instrument and examines the effects of survey length, time investment, survey format and complexity, and survey delivery on participant attrition. The participant attrition rate (PAR) differed between cohorts, with cohorts drawn from Reddit showing a higher PAR than cohorts targeted by other means. Common to all was that the PAR was higher among younger respondents and among men. Changes in survey question design resulted in the greatest rise in PAR irrespective of age, gender or cohort.
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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 | 0.019 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".