Using Publicly Available Directories to Trace Survey Nonresponders and Calculate Adjusted Response Rates
Bibliographic record
Abstract
In population-based surveys, sample lists are often out of date by the time data collection begins. Consequently, response rates, and the perceived validity of the survey, may be compromised by the unknowing inclusion of ineligible subjects. A strategy to address this issue is ascertainment of survey nonrespondents' eligibility status, enabling post hoc adjustment of response rates. In 1995-1996, population surveys were carried out in two Ontario, Canada, communities. Despite intensive follow-up, the status of 8949 (18.6%) of the 48218 potential subjects in these surveys remained unknown. In response, 500 "unknowns" from each community were randomly selected for tracing by using publicly available telephone directories and, where applicable, city directories. These tracing efforts classified persons into one of three groups: "ineligible" (moved before the mailing), "true nonresponder" (present when the survey was mailed), and "remains unknown" (no directory listing found). Publicly available directories clarified the status of 76.0% of potential participants, reducing the proportion of "unknowns" from 18.6% to 4.6%. Applying the estimated proportions of "ineligibles" from each area resulted in response rates adjusted from 63.8% to 71.2% and from 72.8% to 74.9% in the survey areas. Publicly available directories were used to successfully trace the majority of survey nonresponders, thus strengthening confidence in the survey's results.
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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 | 0.127 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".