Using Questionnaires to Identify Travel Barriers: How (Not) to Ask Questions
Bibliographic record
Abstract
Travel barriers can turn everyday journeys into challenges that hinder activity participation. For two decades, researchers have frequently sought to measure travel barriers through surveys. Questionnaire design shapes respondents’ cognitive processing of questions, making survey reliability, validity, and usability crucial to yielding policy-relevant insights. This study applied a system-based approach called the Problem Classification Coding Scheme (CCS) to critically review existing travel barrier survey questions to assess their consistency with best practices. We carried out a keyword search relating to equity, transportation, and surveys. After carefully considering their relevance to the study and based on the availability of the full questionnaires, we evaluated 29 questionnaires used in 34 studies using the CCS. Overall, we identified 920 problems across 1,850 questions; over 32% of the questions had at least one problem. Six issues—vague or unclear questions, unclear respondent instructions, undefined periods, rolling periods, high detail requirements, and complex mental calculations—represented over two-thirds of the identified problems. Over 58% of the problems also occurred in the “comprehension” category of problem classification. We discuss example questions from the reviewed questionnaires to explain why they are likely to cause reliability and validity issues. We also present a checklist as a practical tool to assist researchers and users of travel barrier research in designing robust instruments.
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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 | 0.279 | 0.490 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".