Addressing Nonresponse Bias in Postal Surveys
Bibliographic record
Abstract
Postal surveys are sometimes thought of as a simple option for collecting data in community-based studies; however, nurse researchers must exercise care in appropriately addressing the issue of nonresponse. In particular, both the reporters and the users of such research should look beyond survey response rates when considering nonresponse bias. This article describes the benefits of using postal surveys in public health nursing research, while noting the various potential sources of survey error. Particular attention is directed to the implications of low survey response rates, including decreased power, increased standard error, and nonresponse bias. The belief that increasing response rates will necessarily reduce nonresponse bias is discussed, with an emphasis on the need to identify the reasons for nonresponse and to be judicious in the use of strategies to reduce nonresponse bias. Common response-enhancement strategies are identified, while noting the potential for these strategies to increase nonresponse bias. Assessment of the presence and magnitude of nonresponse bias is discussed, and techniques for postsurvey data adjustment are noted. The need to consider nonresponse bias in designing all phases of the study is highlighted, and is exemplified with a case study.
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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 | 0.529 | 0.725 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".