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Detecting Specialization in Offending: Comparing Analytic Approaches

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Abstract

Offending specialization continues to be a subject of empirical inquiry for scholars interested in criminal careers. Early research consistently spoke to the generality of offending profiles, but more recent work has revealed somewhat mixed findings. These results have emerged alongside newly developed and applied methods that detect and describe offending specialization. To what extent these methods shape divergent conclusions and/or provide overlapping insight remains unclear, however. Therefore, the degree to which more recent inquiries are actually studying the same operational definition of specialization is unknown. In order to consider this issue further, this study utilizes four frequently applied approaches with a single data set. The study indicates when and where findings converge and also describes any unique insights provided by each method. The work concludes with a discussion surrounding the utility of applying multiple strategies in assessing specialization in criminal offending.

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Notes

  1. The fact that previous work has not explicitly compared a number of these methods might partly be due to data constraints. As forthcoming sections will detail, each of the methods requires data with certain attributes, such as knowledge of how offenses are sequenced, ample indicators of crime types (e.g., a number of items for violence and non-violence), and a healthy amount of criminal activity from which to derive patterns. Very few data sets meet these requirements.

  2. Farrington (1986) and Farrington et al. (1988) also utilized a “backward specialization coefficient”, which essentially investigates the likelihood that the k offense was preceded by an offense of the same type.

  3. Britt (1996) and Deane et al. (2005) offer useful approaches that draw on a somewhat similar template for understanding specialization, but provide potential strengths beyond the FSC. We apply the FSC here because it is the most frequently utilized of the three.

  4. Osgood and Schreck (2007) point out that the flexible coding available in the Multilevel IRT method would allow for categorization of multiple different types of offenses (e.g., violent, nonviolent, drug). Still, this would necessitate knowledge of anticipated categorizations ahead of time.

  5. Such a circumstance, in which the relative and objective views of specialization are inconsistent, is at this point speculative. Thus far, analyses using this approach have not encountered this problem. Even so, this method is still emerging (thus, it has not been used across many data sets) and this divergence is a possible outcome under the modeling strategy.

  6. Some may assert that if certain cases are excluded for particular analyses, then they should be excluded across all analyses for comparative purposes. The purpose of this study is to evaluate the relative benefits and drawbacks of these four methods, however. If the fact that one method does not exclude certain cases is one of its purported benefits, then it is appropriate to study the potential impact on the results. For instance, a forthcoming section describes how a hierarchy rule is often invoked with the FSC; in this way, it also excludes data. This is inherent in using these methods and researchers should consider this when assessing their utility.

  7. It is possible that this choice of time window could impact the level of specialization/diversity uncovered in this study as specialization has been found to vary with age (e.g., Piquero 2000). The major emphasis of the current work is on comparing techniques, however, so this difficulty is attenuated somewhat in its impact on our objectives.

  8. The category for “other offenses” was excluded because it largely represented administrative offenses.

  9. Like others (e.g., Osgood and Schreck 2007) we believe that the selection of the most serious offense for a given event may remove interesting information from the analysis, but apply these methods in a way that is consistent with the manner in which they have been used previously.

  10. Although the structure of the data make it somewhat difficult to fully and unambiguously assess the effects of utilizing only the most serious offense in calculating the FSC, we looked at a 10% random subsample of the 1,206 cases in these analyses to assess the extent to which these individuals were charged with more than one offense at a time. Across the time window of focus, individuals had roughly two offenses (mean = 2.16, SD = 2.74), on average, that were not captured in the construction of the transition matrices upon which the FSC is based. This indicates that utilizing only the most serious arrest/charge in a given incident may lead to some information loss in assessing specialization and diversity in offending.

  11. The negative value for arson (−0.003) suggests that the overwhelming tendency is that arson will be followed by a dissimilar offense. This is not surprising—given the overall prevalence of that particular offense type in the data. Britt (1996), Farrington et al. (1988) and Paternoster et al. (1998) have previously discussed the nature of such findings in detail.

  12. We use dichotomous indicators as that has been the most frequent approach in applying this method to date (e.g., Francis et al. 2004; Soothill et al. 2008).

  13. For reference, descriptive probabilities for various offense types based on the model estimates are also shown.

  14. Osgood and Schreck (2007) mention that their method might be expanded to allow for more than one type of specialization (i.e., categorical rather than binary). This would still require that the list of possible offense items be reduced to broader categories, however, therefore losing some information.

  15. As noted above, one limitation of the FSC is the fact that it is an aggregate measure, which may preclude its use in many theoretical tests for technical reasons. Additionally, this emphasis on theory requires that greater consideration be given to those methods that can seamlessly incorporate covariates and provide unbiased and efficient estimates of their impacts on specialization. Depending on the specific question, this suggests the utility of the diversity index, LCA, or IRT or those models developed by Britt (1996) and Deane et al. (2005).

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Acknowledgments

The authors thank Dr. Matt DeLisi. Dr. Wayne Osgood, and Dr. Christopher Schreck for their helpful comments on earlier drafts of this manuscript. They also appreciate the constructive suggestions offered by the anonymous reviewers and editors.

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Authors and Affiliations

  1. School of Criminal Justice, University of Cincinnati, 600k Dyer Hall, P.O. Box 210389, Cincinnati, OH, 45221-0389, USA

    Christopher J. Sullivan

  2. Department of Criminology and Criminal Justice, University of Maryland, 2220 L LeFrak Hall, College Park, MD, 20742, USA

    Jean Marie McGloin

  3. Department of Criminology, University of South Florida, 4202 E Fowler Ave, SOC 107, Tampa, FL, 33620, USA

    James V. Ray & Michael S. Caudy

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  1. Christopher J. Sullivan
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Correspondence to Christopher J. Sullivan.

Appendix

Appendix

Table 7 LCA model fit indices
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Sullivan, C.J., McGloin, J.M., Ray, J.V. et al. Detecting Specialization in Offending: Comparing Analytic Approaches. J Quant Criminol 25, 419–441 (2009). https://doi.org/10.1007/s10940-009-9074-x

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