Frequencies and predictors of missing values as an indicator of data quality in a large population-based sample: an analysis of baseline data from the Hamburg City Health Study
For analysing population-based data, dealing with missingness is equally important at all levels of analysis. During the design and conduct of the study, the identified groups may be targeted to reach higher levels of data completeness.
Authors
- Holger Schulz (ORCID: https://orcid.org/0000-0001-5375-0030)
- Christiane Bleich (ORCID: https://orcid.org/0000-0002-4589-623X)
- Christian Wiessner (ORCID: https://orcid.org/0000-0001-7943-9881)
- Olaf von dem Knesebeck (ORCID: https://orcid.org/0000-0001-6310-5879)
- Heiko Becher (ORCID: https://orcid.org/0000-0002-8808-6667)
- Peer Briken (ORCID: https://orcid.org/0000-0002-1360-014X)
- Elina Petersen (ORCID: https://orcid.org/0000-0002-1235-5066)
- Martin Härter (ORCID: https://orcid.org/0000-0001-7443-9890)
- Katharina Stahlmann (ORCID: https://orcid.org/0000-0002-4704-310X)
- Janina Freitag
Institutions
- Universität Hamburg (DE)
- Heidelberg University (DE)
- University Medical Center Hamburg-Eppendorf (DE)
Publication Details
- Journal
- BMJ Open
- Published
- 2025-11-01
- DOI
- https://doi.org/10.1136/bmjopen-2025-103154
- Primary Topic
- Survey Methodology and Nonresponse
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Universitätsklinikum Hamburg-Eppendorf