Online Survey Data Quality and Its Implication for Willingness‐to‐Pay: A Cross‐Country Comparison
Bibliographic record
Abstract
Using online surveys to elicit consumer preference is gaining popularity because of several advantages offered by this method. Past research mainly focuses on the comparison between online surveys and other survey modes. Few have explored methods of using online survey tools to improve data quality for consumer willingness‐to‐pay (WTP) estimates. This article determines the impact of using a validation question (VQ) approach that asked survey respondents to select a particular answer on improving online survey data quality across six countries. Results show that survey data quality is a common problem in online surveys across countries and the severity of this problem differs significantly. Using VQs might detect the respondents who are less careful in answering survey questions, thus providing less reliable answers. The econometric models for respondents who correctly answer VQs (pass VQs) perform significantly better than the models for respondents who incorrectly answer VQs (fail VQs). The WTP estimates for respondents who pass and fail VQs differ significantly; and in general the WTP estimates for respondents passing VQs have smaller variances than those for all respondents and for respondents failing VQs.
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 | 0.096 | 0.272 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".