Stretched Rating Scales Cause Guided Responding
Bibliographic record
Abstract
Decision making by policymakers, public health professionals, and health care providers is often guided by the extent to which individuals feel at risk for certain adverse health events. Such health risk perceptions can be assessed in surveys using different types of probability rating scales. It has recently been suggested that rating scales that offer decomposed numeric values at the lower end of the scale (stretched scales) improve the accuracy of estimates of small risks. However, the authors suggest that respondents use the differentiated small numeric values as cues to guide them to the correct response. Study 1 supports this proposition by showing that response distributions are substantially skewed toward the lower end of stretched rating scales and have restricted variances as compared with equal-interval scales. Study 2 provides experimental evidence that scores on the stretched scale are a result of guided responding. The results show that scores on stretched rating scales are not a valid reflection of respondents' risk perceptions, but, instead, guide responses to the end of the scale that has been stretched. The findings suggest that stretched rating scales result in biased risk estimates, which may hinder effective communication about health risks between decision- and policymakers as well as between individuals and their health care providers.
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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 | 0.047 | 0.346 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".