Comparing forced and unforced choices in a choice experiment: the role of reference dependent preferences
Bibliographic record
Abstract
In the discrete choice experiment literature, it has been argued that the choice sets from which respondents choose should include an unforced choice because this is more realistic and accounts for status quo bias. However, we propose a much stronger set of arguments for preferring to use unforced choices where relevant. These relate to the concepts of loss aversion and reference dependent preferences from prospect theory. The introduction of a third alternative representing the respondent’s current situation changes the reference point, which may be different for each respondent. This, in turn, changes the size of any losses or gains when comparing Job A or Job B with their current situation, and since losses are valued more than gains, affects the marginal utility of each attribute. The aim of this paper is to test this using two separate discrete choice experiments, one examining choices for nursing jobs, and one examining choices for GP jobs. Each experiment includes both a forced choice (which job do you prefer, A or B) followed by an unforced choice (which job would you choose, A or B or current job) for each choice set. The levels for the current job are constructed from responses to other questions in the survey. This enables us to directly compare the results of the forced and unforced choices for the same individual. We use a generalised multinomial logit model to account for the effects of scale. On average, the introduction of the status quo option led to respondents more likely experiencing losses than gains, due to status quo bias. This corresponded to the marginal utilities being generally higher in the unforced choice model, confirming the well-known finding that losses are valued more than gains. Including an unforced choice in DCEs is necessary (when appropriate) not only for the purposes of ‘realism’, but also because they produce different marginal utilities due to loss aversion.
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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 | 0.244 | 0.526 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".