Addressing Nonresponse Bias in Postal Surveys
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.183 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it