Assessing and adjusting for non-response in the Millennium Cohort Family Study
Bibliographic record
Abstract
BACKGROUND: In conducting population-based surveys, it is important to thoroughly examine and adjust for potential non-response bias to improve the representativeness of the sample prior to conducting analyses of the data and reporting findings. This paper examines factors contributing to second stage survey non-response during the baseline data collection for the Millennium Cohort Family Study, a large longitudinal study of US service members and their spouses from all branches of the military. METHODS: Multivariate logistic regression analysis was used to develop a comprehensive response propensity model. RESULTS: Results showed the majority of service member sociodemographic, military, and administrative variables were significantly associated with non-response, along with various health behaviours, mental health indices, and financial and social issues. However, effects were quite small for many factors, with a few demographic and survey administrative variables accounting for the most substantial variance. CONCLUSIONS: The Millennium Cohort Family Study was impacted by a number of non-response factors that commonly affect survey research. In particular, recruitment of young, male, and minority populations, as well as junior ranking personnel, was challenging. Despite this, our results suggest the success of representative population sampling can be effectively augmented through targeted oversampling and recruitment, as well as a comprehensive survey weighting strategy.
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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 | 0.229 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".