Who Signs Up for Singlehood and Romantic Relationship Studies? Examining Volunteer Bias in Online Recruitment
Bibliographic record
Abstract
Research involving self-report methods risks volunteer bias, which can undermine validity by attracting particular participant types. What is the risk of such bias when participants choose studies on online recruitment platforms? The current study (Study 1: N = 1,595; M age = 28.45; Study 2: N = 2,777; M age = 31.18) examined volunteer bias in online studies, using the context of recruiting individuals for singlehood and romantic relationship research. Participants were recruited via Prolific for a study about “people’s lifestyles,” or “singlehood [or romantic relationships] and people’s lifestyles.” We assessed and compared their demographics, individual differences, feelings about singlehood/partnership, and well-being. No consistent differences emerged across recruitment framings, suggesting that advertisement wording did not selectively attract distinct Prolific participants. These data support one aspect of the validity of online singlehood and relationship research, suggesting that low-effort studies conducted on online platforms may suffer less from volunteer bias than other research recruitment strategies.
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.
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 | 0.233 | 0.306 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier 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".