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Look-alike modelling in violence-related research: A missing data approach
Estela Capelas Barbosa1, Niels Blom2, Annie Bunce3
1Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.
Plos One
|January 14, 2025
Summary
This study demonstrates a novel method to combine survey and administrative data for violence research. Creating a synthetic dataset enhances understanding of sexual violence impacts by overcoming data access barriers.
Area of Science:
- Social Sciences
- Criminology
- Public Health
Background:
- Violence research is fragmented due to data access limitations and safety concerns.
- Existing studies often analyze violence using single datasets, limiting comprehensive understanding.
- Combining datasets could enable longitudinal analysis of violence experiences and health consequences.
Purpose of the Study:
- To provide proof of concept for creating a synthetic dataset by combining survey and administrative data.
- To explore patterns and associations in violence research across multiple sectors.
- To overcome data linkage barriers in violence research.
Main Methods:
- Data integration approached as a missing data problem using multiple imputation with chained equations.
- Combined data from the Crime Survey for England and Wales (CSEW) and Rape Crisis England and Wales (RCEW) administrative data.
- Utilized look-alike modeling principles to impute missing data from CSEW into the RCEW dataset, creating a synthetic RCEW-CSEW dataset.
Main Results:
- Effect sizes in the combined synthetic dataset mirrored those from the source dataset used for imputation.
- Increased variance in the combined dataset led to fewer statistically significant estimates.
- The method successfully created a synthetic combined dataset for analyzing violence-related patterns.
Conclusions:
- Combining administrative and survey datasets using look-alike methods is feasible for violence research.
- This approach offers an innovative and cost-effective way to address data access barriers.
- The synthetic dataset approach facilitates multi-sectorial exploration of violence experiences and impacts.
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