Linking Serial Sex Offences Using Standard, Iterative, and Multiple Classification Trees View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2021-11-23

AUTHORS

Craig Bennell, Rebecca Mugford, Jessica Woodhams, Eric Beauregard, Brittany Blaskovits

ABSTRACT

Studies have shown that it is possible to link serial crimes in an accurate fashion based on the statistical analysis of crime scene information. Logistic regression (LR) is one of the most common statistical methods in use and yields relatively accurate linking decisions. However, some research suggests there may be added value in using classification tree (CT) analysis to discriminate between offences committed by the same vs. different offenders. This study explored how three variations of CT analysis can be applied to the crime linkage task. Drawing on a sample of serial sexual assaults from Quebec, Canada, we examine the predictive accuracy of standard, iterative, and multiple CTs, and we contrast the results with LR analysis. Our results revealed that all statistical approaches achieved relatively high (and similar) levels of predictive accuracy, but CTs produce idiographic linking strategies that may be more appealing to practitioners. Future research will need to examine if and how these CTs can be useful as decision aides in operational settings. More... »

PAGES

691-705

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11896-021-09483-6

DOI

http://dx.doi.org/10.1007/s11896-021-09483-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1142733429


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