Citation

Tonkin M, Pakkanen T, Sirén J, Bennell C, Woodhams J, Burrell A, Imre H, Winter JM, Lam E, ten Brinke G, Webb M, Labuschagne GN, Ashmore-Hills L, van der Kemp JJ, Lipponen S, Rainbow L, Salfati CG, Santtila P. J. Crim. Justice 2017; 50: 19-28.

Copyright

(Copyright © 2017, Elsevier Publishing)

DOI

10.1016/j.jcrimjus.2017.04.002

PMID

unavailable

Abstract

This study compared the utility of different statistical methods in differentiating sexual crimes committed by the same person from sexual crimes committed by different persons. Logistic regression, iterative classification tree (ICT), and Bayesian analysis were applied to a dataset of 3,364 solved, unsolved, serial, and apparent one-off sexual assaults committed in five countries. Receiver Operating Characteristic analysis was used to compare the statistical approaches. All approaches achieved statistically significant levels of discrimination accuracy. Two out of three Bayesian methods achieved a statistically higher level of accuracy (Areas Under the Curve [AUC]=0.89 [Bayesian coding method 1]; AUC=0.91 [Bayesian coding method 3]) than ICT analysis (AUC=0.88), logistic regression (AUC=0.87), and Bayesian coding method 2 (AUC=0.86). The ability to capture/utilize between-offender differences in behavioral consistency appear to be of benefit when linking sexual offenses. Statistical approaches that utilize individual offender behaviors when generating crime linkage predictions may be preferable to approaches that rely on a single summary score of behavioral similarity. Crime linkage decision-support tools should incorporate a range of statistical methods and future research must compare these methods in terms of accuracy, usability, and suitability for practice.

Language: en

Keywords

Logistic regression; Bayesian analysis; Classification tree analysis; Comparative case analysis; Crime linkage; Stranger sexual assault