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Look-alike modelling in violence-related research: A missing data approach

Author

Listed:
  • Estela Capelas Barbosa
  • Niels Blom
  • Annie Bunce

Abstract

Violence has been analysed in silo due to difficulties in accessing data and concerns for the safety of those exposed. While there is some literature on violence and its associations using individual datasets, analyses using combined sources of data are very limited. Ideally data from the same individuals would enable linkage and a longitudinal understanding of experiences of violence and their (health) impacts and consequences. This paper aims to provide proof of concept to create a synthetic dataset by combining data from the Crime Survey for England and Wales (CSEW) and administrative data from Rape Crisis England and Wales (RCEW), pertaining to victim-survivors of sexual violence in adulthood. Intuitively, the idea was to impute missing information from one dataset by borrowing the distribution from the other. In our analyses, we borrowed information from CSEW to impute missing data in the RCEW administrative dataset, creating a combined synthetic RCEW-CSEW dataset. Using look-alike modelling principles, we provide an innovative and cost-effective approach to exploring patterns and associations in violence-related research in a multi-sectorial setting. Methodologically, we approached data integration as a missing data problem to create a synthetic combined dataset. Multiple imputation with chained equations were employed to collate/impute data from the two different sources. To test whether this procedure was effective, we compared regressions analyses for the individual and combined synthetic datasets on binary, continuous and categorical variables. We extended our testing to an outcome measure and, finally, applied the technique to a variable fully missing in one data source. Our results show that the effect sizes for the combined dataset reflect those from the dataset used for imputation. The variance is higher, resulting in fewer statistically significant estimates. Our approach reinforces the possibility of combining administrative with survey datasets using look-alike methods to overcome existing barriers to data linkage.

Suggested Citation

  • Estela Capelas Barbosa & Niels Blom & Annie Bunce, 2025. "Look-alike modelling in violence-related research: A missing data approach," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-19, January.
    • Handle: RePEc:plo:pone00:0301155
    DOI: 10.1371/journal.pone.0301155
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    References listed on IDEAS

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    1. Waters, Hugh Richard & Hyder, Adnan Ali & Rajkotia, Yogesh & Basu, Suprotik & Butchart, Alexander, 2005. "The costs of interpersonal violence--an international review," Health Policy, Elsevier, vol. 73(3), pages 303-315, September.
    2. Chakraborty, Tanika & Mukherjee, Anirban & Rachapalli, Swapnika Reddy & Saha, Sarani, 2018. "Stigma of sexual violence and women’s decision to work," World Development, Elsevier, vol. 103(C), pages 226-238.
    3. Lall, Ranjit, 2016. "How Multiple Imputation Makes a Difference," Political Analysis, Cambridge University Press, vol. 24(4), pages 414-433.
    4. Niels Blom & Vanessa Gash, 2023. "Measures of Violence within the United Kingdom Household Longitudinal Survey and the Crime Survey for England and Wales: An Empirical Assessment," Social Sciences, MDPI, vol. 12(12), pages 1-17, November.
    5. Annie Bunce & Sophie Carlisle & Estela Capelas Barbosa, 2023. "The Concept and Measurement of Interpersonal Violence in Specialist Services Data: Inconsistencies, Outcomes and the Challenges of Synthesising Evidence," Social Sciences, MDPI, vol. 12(7), pages 1-15, June.
    6. Niels Blom & Anastasia Fadeeva & Estela Capelas Barbosa, 2023. "The Concept and Measurement of Violence and Abuse in Health and Justice Fields: Toward a Framework Aligned with the UN Sustainable Development Goals," Social Sciences, MDPI, vol. 12(6), pages 1-15, May.
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