When Who Matters: Interviewer Effects and Survey Modality
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
When and how to survey potential respondents is often determined by budgetary and external constraints, but choice of survey modality may have enormous implications for data quality. Different survey modalities may be differentially susceptible to measurement error attributable to interviewer assignment, known as interviewer effects. In this paper, we leverage highly similar surveys, one conducted face-to-face (FTF) and the other via phone, to examine variation in interviewer effects across survey modality and question type. We find that while there are no cross-modality differences for simple questions, interviewer effects are markedly higher for sensitive questions asked over the phone. These findings are likely explained by the enhanced ability of in-person interviewers to foster rapport and engagement with respondents. We conclude with a thought experiment that illustrates the potential implications for power calculations, namely, that using FTF data to inform phone surveys may substantially underestimate the necessary sample size for sensitive questions.
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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 | 0.213 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 it