Measuring What People Value: A Comparison of “Attitude” and “Preference” Surveys
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
OBJECTIVE: To compare and contrast methods and findings from two approaches to valuation used in the same survey: measurement of "attitudes" using simple rankings and ratings versus measurement of "preferences" using conjoint analysis. Conjoint analysis, a stated preference method, involves comparing scenarios composed of attribute descriptions by ranking, rating, or choosing scenarios. We explore possible explanations for our findings using focus groups conducted after the quantitative survey. METHODS: A self-administered survey, measuring attitudes and preferences for HIV tests, was conducted at HIV testing sites in San Francisco in 1999-2000 (n = 365, response rate = 96 percent). Attitudes were measured and analyzed using standard approaches. Conjoint analysis scenarios were developed using a fractional factorial design and results analyzed using random effects probit models. We examined how the results using the two approaches were both similar and different. RESULTS: We found that "attitudes" and "preferences" were generally consistent, but there were some important differences. Although rankings based on the attitude and conjoint analysis surveys were similar, closer examination revealed important differences in how respondents valued price and attributes with "halo" effects, variation in how attribute levels were valued, and apparent differences in decision-making processes. CONCLUSIONS: To our knowledge, this is the first study to compare attitude surveys and conjoint analysis surveys and to explore the meaning of the results using post-hoc focus groups. Although the overall findings for attitudes and preferences were similar, the two approaches resulted in some different conclusions. Health researchers should consider the advantages and limitations of both methods when determining how to measure what people value.
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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 | 0.004 | 0.000 |
| 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