Using survey experiments for construct validation: “strong leader” questions and support for authoritarian leadership
Bibliographic record
Abstract
Survey experiments are an increasingly popular tool for causal inference in political science. We argue that an under-utilized application for survey experiments is nomological/construct validation, where researchers evaluate whether indicators really measure systematized concepts. We demonstrate this approach by examining respondents’ preferences for autocratic leadership by asking whether those that say they want strong leaders who bend the rules or ignore parliament and elections really want undemocratic leadership in the context of an experimental task. While approaches that measure construct validity with observational data support the validity of these measures, our experimental data tell a different story. We find that respondents – even those who indicate a preference for “strong” leaders in survey questions – are less likely to choose hypothetical candidates who ignore democratic institutions and refuse to compromise with other parties. Our study contributes to the literatures on survey measurement and support for democracy and authoritarian values in established democracies.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".