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Limited research attention has focused on homicides involving foreign-born victims. Using data from the National Violent Death Reporting System, we examined 9428 homicides that occurred in 2017 in the United States across 32 states and D.C. Approximately 8% of homicide victims were foreign-born. Homicide victimization rates were substantially lower for foreign-born persons, compared to U.S.-born persons. However, foreign-born persons from Honduras, El Salvador, and Jamaica had a substantially higher risk of homicide victimization. Notably, few homicides involving foreign-born victims were gang- or drug-trade-related. With the growing number of immigrants in the United States, policy and prevention efforts should be guided by research.

This study explores advanced techniques in machine learning to develop a short tree-based adaptive classification test based on an existing lengthy instrument. A case study was carried out for an assessment of risk for juvenile delinquency. Two unique facts of this case are (a) the items in the original instrument measure a large number of distinctive constructs; (b) the target outcomes are of low prevalence, which renders imbalanced training data. Due to the high dimensionality of the items, traditional item response theory (IRT)-based adaptive testing approaches may not work well, whereas decision trees, which are developed in the machine learning discipline, present as a promising alternative solution for adaptive tests. A cross-validation study was carried out to compare eight tree-based adaptive test constructions with five benchmark methods using data from a sample of 3,975 subjects. The findings reveal that the best-performing tree-based adaptive tests yielded better classification accuracy than the benchmark method IRT scoring with optimal cutpoints, and yielded comparable or better classification accuracy than the best benchmark method, random forest with balanced sampling. The competitive classification accuracy of the tree-based adaptive tests also come with an over 30-fold reduction in the length of the instrument, only administering between 3 to 6 items to any individual. This study suggests that tree-based adaptive tests have an enormous potential when used to shorten instruments that measure a large variety of constructs.

Although an increasing number of studies have examined offending and victimization in China, little is known about the extent of the victim–offender overlap and what factors may explain differential tendencies toward offending versus victimization in the Chinese context. To fill this gap and to broaden the empirical base of the current literature on the victim–offender overlap, we examine the association of a number of prominent theoretical constructs with overall violent encounters and role differentiation. Using data collected from over 2,000 students from six schools in Changzhi, a city in Northern China, and multilevel item response theory modeling, we find a nontrivial overlap between violent offending and victimization and differential tendencies toward offending versus victimization. We also find that most of our theoretical constructs are associated with overall violent encounters, but only moral beliefs, peer delinquency, drinking, and gender are related to role differentiation. Findings for theory and research are discussed.