Premium
A Comparison of Logistic Regression and Classification Tree Analysis for Behavioural Case Linkage
Author(s) -
Tonkin Matthew,
Woodhams Jessica,
Bull Ray,
Bond John W.,
Santtila Pekka
Publication year - 2012
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.1367
Subject(s) - logistic regression , logistic model tree , linkage (software) , sample (material) , statistics , computer science , regression analysis , tree (set theory) , decision tree , usability , artificial intelligence , econometrics , psychology , machine learning , mathematics , mathematical analysis , biochemistry , chemistry , chromatography , human–computer interaction , gene
Much previous research on behavioural case linkage has used binary logistic regression to build predictive models that can discriminate between linked and unlinked offences. However, classification tree analysis has recently been proposed as a potential alternative owing to its ability to build user‐friendly and transparent predictive models. Building on previous research, the current study compares the relative ability of logistic regression analysis and classification tree analysis to construct predictive models for the purposes of case linkage. Two samples are utilised in this study: a sample of 376 serial car thefts committed in the UK and a sample of 160 serial residential burglaries committed in Finland. In both datasets, logistic regression and classification tree models achieve comparable levels of discrimination accuracy, but the classification tree models demonstrate problems in terms of reliability or usability that the logistic regression models do not. These findings suggest that future research is needed before classification tree analysis can be considered a viable alternative to logistic regression in behavioural case linkage. Copyright © 2012 John Wiley & Sons, Ltd.