Factors Affecting Interview Duration in Web-Based Travel Surveys
Bibliographic record
Abstract
Historically, travel surveys have been conducted face-to-face, by mail, or by phone. With the increasing share of households having access to the Internet, other survey modes have been deployed. This paper focuses on web surveys. Among other advantages, using the web to conduct surveys reduces costs and helps mitigate poor response rates among young households. Very few studies have been conducted on interview duration and its determinant using paradata from web travel surveys. Such knowledge is necessary to validate the context in which travel data are gathered and can be used to understand sample and data quality. Interview duration modeling is also essential for allocating survey servers and monitoring interviews during the data collection phase. This paper models interview duration using paradata from nine web surveys conducted in the Quebec province from 2010 to 2014. The main objectives of the model are to assist the monitoring of interviews by detecting outliers, provide a better estimate of the interview duration to respondents and survey managers during the interview, and allow a more precise evaluation of the server performance needed before conducting web travel surveys. Using a multiple regression model, we observed that the most important variables in explaining interview duration were number of car and transit trips as well as number of unique places visited during a day. Conducting the interview on a small-screen device also increased interview duration. The model also provides a baseline estimate of interview duration on the basis of demographic features and questionnaire design.
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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 | 0.049 | 0.212 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".