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Examining What Makes Violent Crime Victims Unique: Extending Statistical Methods for Studying Specialization to the Analysis of Crime Victims

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Abstract

Objectives

Much victimization research focuses on specific types of crime victims, which implies that the factors responsible for some victimization outcomes are distinct from others. Recent developments in victimization theory, however, take a more general approach, postulating that victimization regardless of type will share a similar basic etiology. This research examines how and whether the risk factors that are associated with violent victimization significantly differ from those that predict nonviolent victimization.

Methods

Using data from 3,682 Kentucky youth, we employ Osgood and Schreck’s (2007) Item Response Theory-based statistical approach for detecting specialization to determine the properties and predictors of tendencies for individuals to fall victim to specific types of crime.

Results

Findings show that victims typically experience varied outcomes, but some victims have a clear tendency toward violent victimization and that it is possible to predict this tendency.

Conclusions

The findings indicate that a more nuanced general approach, one that accounts for tendencies toward specific victimization outcomes, might add insight about the causes of victimization. This research also shows how statistical methods designed to examine offense specialization can add value for research on victimization.

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Notes

  1. One difficulty for our particular line of inquiry is that the term “specialization” might be invidious with respect to describing victimization, implying as it does the possibility that victims somehow willingly choose what types of crimes they fall victim to. This to us is a value question rather than a scientific one; however, we only use “specialization” in order to draw appropriate parallels in the cognate literature on offending. Otherwise, in this paper, we refer to the phenomenon as “victim type differentiation” or “differential victimization.”

  2. This issue also exists in research on criminal offending, as detailed by McGloin et al. (2011).

  3. For additional details on sample characteristics and attrition, see, for example, Ousey and Wilcox (2007) and Wilcox et al. (2009). For additional details on the application of the Dillman method in the RSVP study, see Wilcox et al. (2006).

  4. The Kentucky data also include items measuring difficulty in controlling temper, which is a characteristic of those with low self-control, but we elected not to use these insofar as they could arguably be too closely linked with violent outcomes.

  5. “Other” in this sample, is almost all white, non-Hispanic. Treating non-Hispanic whites as the reference category does not change the results.

  6. In the notation of hierarchical linear modeling (Raudenbusch and Bryk 2002), our Level-1 regression equation is:

    \( \ln (\lambda_{ij}) = \beta_{0j} + \beta_{1j} {\text{Diff}} + \sum\limits_{i = 2}^{I - 1} {\beta_{ij} D_{ij} } \quad (1) \)

    The Level-2 regression equations are:

    \( \beta_{0j} = \gamma_{00} + \gamma_{01} {\text{X}}1j + \gamma_{02} {\text{X}}2j + \cdots + {\text{u}}_{0j} \quad (2) \)

    \( \beta_{1j} = \gamma_{11} {\text{X}}1j + \gamma_{12} {\text{X}}1j + \cdots + {\text{u}}_{1j} \quad (3) \)

    \( \beta_{ij} = \gamma_{i0} \quad (4) \)

    In Eq. 1, the intercept, β0j , refers to the average score for all victimization items, β1j is the differential victimization coefficient, and the remainder incorporates the base rates for the individual victimization items through dummy variables indicative of each item.

  7. To further verify the results, we estimated identical models with lagged predictors (i.e., Wave 1 predictors with Wave 2 outcomes). The pattern of results in these models is identical to those reported in the narrative and in Table 5.

  8. Assault, for instance, very plausibly is an expressive crime. But several ethnographers (e.g., Anderson 1999; Jacobs and Wright 2006) have reported how assaults serve very clear instrumental purposes; sometimes both motives come into play.

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Acknowledgments

The authors are grateful to the editors and anonymous referees of the Journal of Quantitative Criminology for their thoughtful and detailed comments.

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Authors and Affiliations

  1. Department of Criminal Justice, Rochester Institute of Technology, 1 Lomb Memorial Drive, Rochester, NY, 14623, USA

    Christopher J. Schreck

  2. Department of Sociology, College of William and Mary, P.O. Box 8795, Williamsburg, VA, 23187, USA

    Graham C. Ousey

  3. School of Criminal Justice, University of Cincinnati, 600J Dyer Hall, PO Box 210389, Cincinnati, OH, 45221, USA

    Bonnie S. Fisher

  4. School of Criminal Justice, University of Cincinnati, 600P Dyer Hall, PO Box 210389, Cincinnati, OH, 45221, USA

    Pamela Wilcox

Authors
  1. Christopher J. Schreck
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  2. Graham C. Ousey
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  3. Bonnie S. Fisher
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  4. Pamela Wilcox
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Correspondence to Christopher J. Schreck.

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Schreck, C.J., Ousey, G.C., Fisher, B.S. et al. Examining What Makes Violent Crime Victims Unique: Extending Statistical Methods for Studying Specialization to the Analysis of Crime Victims. J Quant Criminol 28, 651–671 (2012). https://doi.org/10.1007/s10940-012-9165-y

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  • DOI: https://doi.org/10.1007/s10940-012-9165-y

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