Rules for Assessing Data Quality of Activity-Scheduling Survey Respondents
Bibliographic record
Abstract
The development of standards and guidance for applying quality measures in travel surveys is receiving increased attention. The most commonly reported quality measures are unit nonresponse and trip rates. How such a standard would be applied for assessing the quality of a single respondent's data remains unexplored. Furthermore, new quality indicators are needed for emerging travel survey methods, such as multiday and computerized scheduling surveys that capture observed activity travel patterns as well as information on underlying activityscheduling decision processes. A new set of rules is proposed for assessing data of individual respondents by using results from a recent survey of this type. The use of computers and a focus on decision processes led to developing several new quality indicators related to respondent login durations, data entry delays, entry timing, and recall rates. Analysis of these indicators was used to propose six rules for identifying goodquality data. A combined single rule was also developed by using principal component analysis. Further analysis of this combined indicator revealed that sociodemographic characteristics of the respondents were largely insignificant—people of all ages, genders, marital status, and employment or student status were equally able to provide good data. It also suggests that use of the combined indicator to filter out poorquality data will not necessarily introduce any additional bias into the resulting reduced sample set. The weak correlation of respondent activity or trip rates with the combined indicator implies that their usefulness for assessing data quality at an individual level is questionable.
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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 | 0.239 | 0.469 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".