The Statistical Analysis of Misreporting on Sensitive Survey Questions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
What explains why some survey respondents answer truthfully to a sensitive survey question, while others do not? This question is central to our understanding of a wide variety of attitudes, beliefs, and behaviors, but has remained difficult to investigate empirically due to the inherent problem of distinguishing those who are telling the truth from those who are misreporting. This article proposes a solution to this problem. It develops a method to model, within a multivariate regression context, whether survey respondents provide one response to a sensitive item in a list experiment, but answer otherwise when asked to reveal that belief openly in response to a direct question. As an empirical application, the method is applied to an original large-scale list experiment to investigate whether those on the ideological left are more likely to misreport their responses to questions about prejudice than those on the right. The method is implemented for researchers as open-source software.
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.
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 | 0.003 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it