Characterizing individual and methodological risk factors for survey non-completion using machine learning: findings from the U.S. Millennium Cohort Study
This research highlights the importance of accounting for potential respondent bias due to survey non-completion and identifies factors associated with this type of missing data.
Authors
- Sheila F. Castañeda (ORCID: https://orcid.org/0000-0002-1975-0544)
- Rudolph P. Rull (ORCID: https://orcid.org/0000-0003-0989-3629)
- Claire A. Kolaja (ORCID: https://orcid.org/0000-0002-4693-0460)
- Crystal Fuller Lewis (ORCID: https://orcid.org/0000-0003-1798-1311)
- Nate C Carnes
Institutions
- Leidos (United States) (US)
- Naval Health Research Center (US)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2025-07-14
- DOI
- https://doi.org/10.1186/s12874-025-02620-3
- Citations
- 1
- Primary Topic
- Health disparities and outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.17
Funders
- Leidos