This paper outlines a brief history of the evolutionary trajectory of offender profiling and illustrates the three broad strands (investigative, clinical, and statistical ) that emerged in the 1970s-1990s. We then indicate how a more pragmatic, interdisciplinary practitioner-academic model has emerged in recent years and go on to describe the range of contributions that are now made across the criminal justice field. More recently termed 'behavioural investigative advice' in the UK, the paper then argues that whilst a range of potential contributions exist (from linking crimes, risk assessment, provision of bad character evidence, investigative interviewing advice, to geoprofiling), the nature of the process by which that contribution occurs is not yet well understood. The review of these potential contributions concludes with several suggestions and recommendations for further research and relevant methodologies by which to conduct that research. This includes the requirement to combine conceptual and theory-driven models alongside empirically driven statistical approaches, as well as the requirement to more precisely delineate and describe how contributions are made by behavioural experts through cognitive task analyses and associated methods.Several countries' police services regularly employ the assistance of psychologists in relation to the prevention, management, and investigation of crime (Alison, 2005).Although some of what they are engaged in might be described as offender profiling, the support from psychologists over the last 10 years, in the UK at least, might be more accurately described as behavioural investigative advice (BIA; ACPO, 2006). The older term offender profiling has developed an almost mythic status in popular literature and drama (Herndon, 2007), although, as this paper will demonstrate, in its best understood but narrow definition, it has failed to make much operational impact.

The empirical support for linkage analysis is steadily increasing, but the question remains as to what method of linking is the most effective. We compared a more theory‐based, dimensional behavioural approach with a rather pragmatic, multivariate behavioural approach with regard to their accuracy in linking serial sexual assaults in a UK sample of serial sexual assaults (n = 90) and one‐off sexual assaults (n = 129). Their respective linkage accuracy was assessed by (1) using seven dimensions derived by non‐parametric Mokken scale analysis (MSA) as predictors in discriminant function analysis (DFA) and (2) 46 crime scene characteristics simultaneously in a naive Bayesian classifier (NBC). The dimensional scales predicted 28.9% of the series correctly, whereas the NBC correctly identified 34.5% of the series. However, a subsequent inclusion of non‐serial offences in the target group decreased the amount of correct links in the dimensional approach (MSA–DFA: 8.9%; NBC: 32.2%). Receiver operating characteristic analysis was used as a more objective comparison of the two methods under both conditions, confirming that each achieved good accuracies (AUCs = .74–.89), but the NBC performed significantly better than the dimensional approach. The consequences for the practical implementation in behavioural case linkage are discussed. Copyright © 2012 John Wiley & Sons, Ltd.

Purpose To conduct a test of the principles underpinning crime linkage (behavioural consistency and distinctiveness) with a sample more closely reflecting the volume and nature of sexual crimes with which practitioners work, and to assess whether solved series are characterized by greater behavioural similarity than unsolved series. Method A sample of 3,364 sexual crimes (including 668 series) was collated from five countries. For the first time, the sample included solved and unsolved but linked‐by‐DNA sexual offence series, as well as solved one‐off offences. All possible crime pairings in the data set were created, and the degree of similarity in crime scene behaviour shared by the crimes in each pair was quantified using Jaccard's coefficient. The ability to distinguish same‐offender and different‐offender pairs using similarity in crime scene behaviour was assessed using Receiver Operating Characteristic analysis. The relative amount of behavioural similarity and distinctiveness seen in solved and unsolved crime pairs was assessed. Results An Area Under the Curve of .86 was found, which represents an excellent level of discrimination accuracy. This decreased to .85 when using a data set that contained one‐off offences, and both one‐off offences and unsolved crime series. Discrimination accuracy also decreased when using a sample composed solely of unsolved but linked‐by‐DNA series (AUC = .79). Conclusions Crime linkage is practised by police forces globally, and its use in legal proceedings requires demonstration that its underlying principles are reliable. Support was found for its two underpinning principles with a more ecologically valid sample.

Purpose: 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.Methods: 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.Results: 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). Conclusions: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 2 research must compare these methods in terms of accuracy, usability, and suitability for practice.

Research on offenders with intellectual disabilities (IDs) in the criminal justice arena is on the rise, reflected by a growing number of relevant publications each year. However, there is a long recognized methodological problem that hampers the comparability of empirical studies and that raises doubts about the accuracy of prevalence rates, comorbidities, and various correlates and characteristics. In this paper we will argue that the crux of the problem can, on the one hand, be found in the plurality of assessment methods for intelligence and adaptive functioning, which are not all sufficiently reliable and valid. On the other hand, assessment of IQ in criminal justice and mental health-related areas appears to be informed more by practical aspects and needs rather than grounded in a solid theoretical model. Hence, we suggest that the Cattell-Horn-Carroll (CHC) model of intelligence has potential value in this regard, and deserves a closer look. Finally, we will discuss its incorporation into, and possible implications for, criminal justice practice and future study designs.

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This study compared the ability of seven statistical models to distinguish between linked and unlinked crimes. The seven models utilised geographical, temporal, and modus operandi information relating to residential burglaries (n = 180), commercial robberies, (n = 118), and car thefts (n = 376). Model performance was assessed using receiver operating characteristic analysis and by examining the success with which the seven models could successfully prioritise linked over unlinked crimes. The regression‐based and probabilistic models achieved comparable accuracy and were generally more accurate than the tree‐based models tested in this study. The Logistic algorithm achieved the highest area under the curve (AUC) for residential burglary (AUC = 0.903) and commercial robbery (AUC = 0.830) and the SimpleLogistic algorithm achieving the highest for car theft (AUC = 0.820). The findings also indicated that discrimination accuracy is maximised (in some situations) if behavioural domains are utilised rather than individual crime scene behaviours and that the AUC should not be used as the sole measure of accuracy in behavioural crime linkage research.