A Comparison of Ordered Categorical versus Discrete Choices within a Stated Preference Survey of Whole-Blood Donors
Bibliographic record
Abstract
There are different stated preference (SP) approaches, including discrete choice experiments (DCEs). DCEs are a popular SP approach, but in some settings, alternative ways of framing survey questions may be more appropriate. The Health Economic Modelling Of Alternative Blood Donation Strategies (HEMO) study required choice tasks to be framed so that the study could estimate the effect of attribute levels on the frequency of a behavior—in this case, blood donation. SP questions were formulated to require ordered categorical responses from a single profile of attribute levels. However, it is unknown whether this way of framing SP questions leads to estimates of marginal rates of substitution (MRS) that are different from traditional DCE choices between 2 alternative profiles. The aim of this article is to compare estimates of relative preferences from SP questions requiring ordered categorical versus discrete choice responses. We compared relative preferences elicited from the 2 approaches for a common set of attributes and levels, formulated as choice tasks for 8,933 whole-blood donors. We found that the 2 forms of survey questions provided similar MRSs estimates. For example, respondents were willing to trade off only a small increase in travel time to receive a health report, irrespective of whether the choice given was binary (DCE response; approximately 3 min) or from an ordered category (about 8 min). The finding that any differences in the estimated MRSs are not of substantive importance offers some reassurance for policy makers in that estimates of relative preference may be robust to alternative ways of framing the survey questions. These findings can encourage future studies to frame choice tasks that align with the study’s objective. Highlights This article compares the relative preferences from stated preference (SP) questions requiring ordered categorical versus discrete choice responses. The approaches were contrasted for blood donation service characteristics that offer opportunities to donate blood. The estimates of relative preferences for alternative blood donation service characteristics were similar between the 2 forms of SP approach. This study illustrates how SP survey questions can be formulated to provide responses on an ordered categorical scale and to estimate marginal rates of substitution between different attributes, which can be compared with those derived from discrete choice experiment (DCE) choices. The article highlights the potential value of considering alternative choice framings rather than relying solely on DCEs.
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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 | 0.055 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".