Choice Experiment Attributes Selection: Problems and Approaches in a Modal Shift Study in Klang Valley, Malaysia
Bibliographic record
Abstract
Choice experiment (CE) is a questionnaire based method that the accuracy of research questionnaire determines the validity of the research outcomes. Attribute selection has a prime importance in every CE studies. If respondents do not understand or do not have preference for a certain attribute, the attribute non-attendance problem might happen that biases overall results of the research. Qualitative approaches such as literature review, focus group discussion, and in depth discussion commonly applied in CE researches. However, especially in the developing countries context where ethnical and cultural diversity is a challenge in conducting survey based questionnaires, qualitative methods are not sufficient in selecting attributes. Present study investigates the application of relative importance index (RII) in respondents’ preference for attributes in a modal shift study in Klang Valley, Malaysia. The 5 point Likert scale questions were employed to enhance respondents’ preferences for initial 24 selected attributes. The results of this study showed that from 24 pre-selected attributes, only 18 of them had RII>0.5 and could be included in the final CE design. The results of this study could help researchers to control for unobserved problems in selecting the attributes which could not be discovered through qualitative approaches.
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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 | 0.061 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".