Assessing the Validity of Prevalence Estimates in Double List Experiments
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
Abstract Social scientists use list experiments in surveys to estimate the prevalence of sensitive attitudes and behaviors in a population of interest. However, the cumulative evidence suggests that the list experiment estimator is underpowered to capture the extent of sensitivity bias in common applications. The literature suggests double list experiments (DLEs) as an alternative to improve along the bias-variance frontier. This variant of the research design brings the additional burden of justifying the list experiment identification assumptions in both lists, which raises concerns over the validity of DLE estimates. To overcome this difficulty, this paper outlines two statistical tests to detect strategic misreporting that follows from violations to the identification assumptions. I illustrate their implementation with data from a study on support toward anti-immigration organizations in California and explore their properties via simulation.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it