A Comparison of Logistic Regression and Classification Tree Analysis for Behavioural Case Linkage

M Tonkin, J Woodhams, R Bull, JW Bond, Pekka Santtila

Research output: Contribution to journalArticleScientificpeer-review

24 Citations (Scopus)


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 (c) 2012 John Wiley & Sons, Ltd.
Original languageUndefined/Unknown
Pages (from-to)235–258
Number of pages24
JournalJournal of Investigative Psychology and Offender Profiling
Issue number3
Publication statusPublished - 2012
MoE publication typeA1 Journal article-refereed


  • case linkage
  • classification trees
  • comparative case analysis
  • regression

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