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Base rates and Bayes’ Theorem for decision support

Base rates and Bayes’ Theorem for decision support Purpose – The purpose of this paper is to discuss and demonstrate “best practices” for creating quantitative behavioural investigative advice (i.e. statements to assist police with psychological and behavioural aspects of investigations) where complex statistical modelling is not available. Design/methodology/approach – Using a sample of 361 serial stranger sexual offenses and a cross‐validation approach, the paper demonstrates prediction of offender characteristics using base rates and using Bayes’ Theorem. The paper predicts four dichotomous offender characteristic variables, first using simple base rates, then using Bayes’ Theorem with 16 categorical crime scene variable predictors. Findings – Both methods consistently predict better than chance. By incorporating more information, analyses based on Bayes’ Theorem (74.6 per cent accurate) predict with 11.1 per cent more accuracy overall than analyses based on base rates (63.5 per cent accurate), and provide improved advising estimates in line with best practices. Originality/value – The study demonstrates how useful predictions of offender characteristics can be acquired from crime information without large (i.e. >500 cases) data sets or “trained” statistical models. Advising statements are constructed for discussion, and results are discussed in terms of the pragmatic usefulness of the methods for police investigations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Policing: An International Journal of Police Strategies and Management Emerald Publishing

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References (30)

Publisher
Emerald Publishing
Copyright
Copyright © 2014 Emerald Group Publishing Limited. All rights reserved.
ISSN
1363-951X
DOI
10.1108/PIJPSM-03-2013-0025
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to discuss and demonstrate “best practices” for creating quantitative behavioural investigative advice (i.e. statements to assist police with psychological and behavioural aspects of investigations) where complex statistical modelling is not available. Design/methodology/approach – Using a sample of 361 serial stranger sexual offenses and a cross‐validation approach, the paper demonstrates prediction of offender characteristics using base rates and using Bayes’ Theorem. The paper predicts four dichotomous offender characteristic variables, first using simple base rates, then using Bayes’ Theorem with 16 categorical crime scene variable predictors. Findings – Both methods consistently predict better than chance. By incorporating more information, analyses based on Bayes’ Theorem (74.6 per cent accurate) predict with 11.1 per cent more accuracy overall than analyses based on base rates (63.5 per cent accurate), and provide improved advising estimates in line with best practices. Originality/value – The study demonstrates how useful predictions of offender characteristics can be acquired from crime information without large (i.e. >500 cases) data sets or “trained” statistical models. Advising statements are constructed for discussion, and results are discussed in terms of the pragmatic usefulness of the methods for police investigations.

Journal

Policing: An International Journal of Police Strategies and ManagementEmerald Publishing

Published: Mar 11, 2014

Keywords: Decision making; Bayesian methods; Intelligence‐led policing; Investigation; Offender characteristics

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