Log-Linear Randomized-Response Models Taking Self-Protective Response Behavior Into Account
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Bibliographic record
Abstract
Randomized response (RR) is an interview technique designed to eliminate response bias when sensitive questions are asked. In RR the answer depends partly on the true status of the respondent and partly on the outcome of a randomizing device. Although RR elicits more honest answers than direct questions do, it is susceptible to self-protective response behavior; that is, the respondent gives an evasive answer irrespective of the outcome of the randomizing device. The authors present a log-linear RR model that accounts for this kind of self-protection (SP). The main results of this SP model are estimates of (1) the probability of SP, (2) the log-linear parameters describing the associations between the sensitive characteristics, and (3) the prevalence of the sensitive characteristics that are corrected for SP. The model is illustrated with two examples from a Dutch survey measuring noncompliance with social welfare rules.
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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 | 0.303 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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