Violence against children: an application of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to the Crime Survey for England and Wales.

Abstract

Background: Violence victimization in childhood is a significant public health and social justice concern. Yet there is limited evidence on how multiple, overlapping identities relate to children’s experiences of non-familial violence. Objectives: This study examines differences in violence victimization rates among children in different social groups. In doing so, we seek to demonstrate the application of Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) as a method for intersectional analysis. Participants and Setting: The study uses nine years of pooled data from the 10-15 Crime Survey for England and Wales (2011-2019), including a total sample of 26,106 children aged 10-15 years old. Methods: Logistic MAIHDA models were employed to analyse the likelihood of experiencing violence victimization across intersectional social groups defined by combinations of four social identities (sex, age, ethnicity, disability status). Results: Most of the differences in violence victimisation across intersectional social groups are explained by individual characteristics like disability, sex, ethnicity, and age. Interaction effects between these characteristics add little beyond their separate (additive) impacts. Predicted probabilities show that disabled boys are among the most likely to experience violence victimisation. Conclusion: The study underscores the need for targeted policies and interventions to reduce violence against children, particularly those who are disabled. It also serves as a case study for researchers interested in using MAIHDA to explore intersectionality in crime against children (or any other outcomes) and inform harm prevention strategies.