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Advancement of Criminal Profiling Methods in Faceted Multidimensional Analysis
Author(s) -
Goodwill Alasdair M.,
Stephens Skye,
Oziel Sandra,
Sharma Shankari,
Allen Jared C.,
Bowes Nicola,
Lehmann Robert
Publication year - 2013
Publication title -
journal of investigative psychology and offender profiling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.479
H-Index - 22
eISSN - 1544-4767
pISSN - 1544-4759
DOI - 10.1002/jip.1388
Subject(s) - offender profiling , facet (psychology) , psychology , profiling (computer programming) , goodwill , crime scene , centroid , logistic regression , social psychology , criminology , artificial intelligence , computer science , machine learning , personality , finance , bayesian network , economics , big five personality traits , operating system
The current study seeks to advance the faceted multidimensional scaling (termed FMDS) procedure used in much of the investigative psychology research. To this end, recent research on street robbery by Goodwill and colleagues will be utilised to illustrate the effectiveness of a facet scale method for offender profiling. Four FMDS themes of street robbery (Con, Blitz, Confrontation and Snatch) were revealed by the crossing of two underlying axial facets: the offenders' level of violence and interaction with the victim. The facet scale method, utilising offenders' axial facet scores, was compared to previous count, proportional and centroid classification methods in the prediction of offender criminal histories. Utilising logistic regression and receiver operating characteristic analyses, the axial facet scale method was found to significantly outperform the qualitatively based dominant theme classification methods that typically employ angular and radial facets for FMDS interpretation. Implications for the use of axial facet scales within FMDS analysis for offender profiling research are discussed. Copyright © 2012 John Wiley & Sons, Ltd.