The Interaction of Survey Mode and Social Desirability in Measuring Behavior during Wartime
Bibliographic record
Abstract
Measuring individuals' behavior from survey data during an active war raises distinct challenges. To investigate survey mode and social desirability effects, we conduct a multi-mode study (opt-in web, RDD telephone) with two independently drawn samples of the Ukrainian population during Russia's full-scale invasion of Ukraine in the summer of 2022. We employ identical demographic and behavioral questions in both surveys to investigate cross-mode reliability. We also embed a pre-registered priming experiment to examine whether respondents may inflate prosocial behavior. Observationally, we find that because web studies cannot reach the oldest Ukrainians, they likely contain significant population bias with respect to war-related demographic variables. Experimentally, we find evidence of inflation in reporting volunteering activities only in the telephone survey. Our findings demonstrate that, given modern survey techniques, wartime attitudes and behavior can be reliably measured, but there is a trade-off in survey modes between representativeness and response bias. The manuscript contributes to our knowledge about measuring behavior and survey methods.
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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 | 0.122 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".