Comparative judgement modelling to map forced marriage at local levels
Forcing someone into marriage against their will is a violation of their human rights. In 2021, the county of Nottinghamshire, U.K., launched a strategy to tackle forced marriage and violence against women and girls. We set out to map the risk of forced marriage across the county to support the strategy and enable the development of local interventions. However, there was no centralised database for forced marriage in the county and carrying out a survey using standard survey methods was unlikely to produce robust results due to the hidden nature of this crime. Comparative judgement provides a survey design that can map the risk of forced marriage through pairwise comparisons. Current comparative judgement models require studies to have a large number of participants, so we developed a more flexible spatial modelling structure and a mechanism to schedule comparisons more effectively. The proposed modelling structure reduced the data collection burden and made a comparative judgement study feasible with a small number of participants. Underpinning this structure is a latent variable representation that improves on the scalability of inferential procedures of previous comparative judgement models. We used these methods to map the risk of forced marriage across Nottinghamshire, thereby supporting the county’s strategy for tackling violence against women and girls.
Authors
- Helen McCabe (ORCID: https://orcid.org/0000-0002-7625-7916)
- Albert Nyarko-Agyei (ORCID: https://orcid.org/0000-0003-2222-7567)
- Rowland G. Seymour (ORCID: https://orcid.org/0000-0002-8739-3921)
- David Sirl (ORCID: https://orcid.org/0000-0002-2639-1666)
- Katie E. Severn (ORCID: https://orcid.org/0000-0003-2406-727X)
- Adam J. Taylor (ORCID: https://orcid.org/0000-0001-7736-795X)
- Theodore Kypraios (ORCID: https://orcid.org/0000-0002-6190-4762)
Institutions
- University of Nottingham (GB)
- University of Birmingham (GB)
Publication Details
- Journal
- The Annals of Applied Statistics
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1214/24-aoas1966
- Citations
- 1
- Primary Topic
- Demographic Trends and Gender Preferences
- Type
- article
- Field-Weighted Citation Impact
- 3.87
Funders
- UK Research and Innovation
- Engineering and Physical Sciences Research Council
- Research England