Does it matter how we measure conspiracy beliefs? A test of three measurement approaches
Bibliographic record
Abstract
Abstract Objectives In this article, we examine the extent to which respondents’ support for conspiratorial claims depends on question format. Measuring conspiracy theory beliefs in the general population is challenging, and properly capturing these beliefs is necessary if we are to understand them. Methods We conducted a preregistered vignette experiment of Canadian respondents (N = 3,518) in February 2024. In our experiment, we introduced survey respondents to conspiratorial claims in one of three ways: (1) by asking them whether they support a conspiratorial claim directly through a Likert scale with the response set from very likely to not likely at all, (2) by offering respondents a binary choice between a conspiratorial claim and an alternative claim, and (3) by offering respondents a trichotomous choice between a conspiratorial claim, an alternative claim, and an equally likely option. Results We find the trichotomous format produces the most conservative estimates of conspiracy endorsement, while the Likert format produces the most permissive estimates. In some cases, the percentage of respondents who endorsed conspiracy theories in the Likert questions was more than three times as high as in the trichotomous format, and in many cases was around twice as large. Conclusion Different question formats lead to substantially different estimates of conspiracy beliefs. While we believe that it is impossible to create completely bias‐free questions measuring conspiracy belief, researchers must acknowledge the likely biases (in particular, social desirability bias and acquiescence bias) that may be present in their survey designs.
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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 | 0.297 | 0.569 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".