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Modeling the Referral Decision in Sexual Assault Cases: An Application of Random Forests

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Abstract

This paper examines the decision to refer a sexual assault case for prosecution using a sample of 730 reported sexual assaults in which the victim received a medical/forensic examination. The decision to refer a case for prosecution was modeled using an algorithmic modeling technique, Random Forests. The key advantages of this modeling approach include its superiority in predicting case outcomes and its ability to easily uncover nonlinear relationships. Key results indicate that the likelihood of referral increased when sperm was found and documented, when the victim could identify the suspect, and as the severity of nongenital injury increased. Neither the presence nor the severity of genital injury impacted the decision to refer a case for prosecution. On the whole, suspect and report characteristics had the largest impact on referring cases for prosecution, with victim characteristics having little influence.

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Notes

  1. Additional factors examined in the case processing literature include strength of the evidence (Albonetti, 1987), use of a weapon (Alderden & Ullman, 2012; Beichner & Spohn, 2005; Kerstetter, 1990; Spohn et al., 2001), multiple offenders (McGregor et al. 1999, 2002), victim resistance (Alderden & Ullman, 2012; Du Mont & Myhr, 2000; Kerstetter, 1990; Spohn & Horney, 1993; Spohn & Spears, 1996) timing of reporting (Beichner & Spohn, 2005; Kingsnorth et al., 1999; Spears & Spohn, 1997), and the availability of witnesses (Beichner & Spohn, 2005; Kingsnorth et al., 1999).

  2. For brevity’s sake, other results are not presented but are available from the authors upon request. This cost ratio was chosen arbitrarily but it does represent the middle ground among the six estimated cost ratios and provides a representative picture of the effects seen at other cost ratios (although rank order varied slightly, the same six variables provided the largest change in average prediction success for all cases across all cost ratios).

  3. As with variable importance measures, analyses were conducted at all six cost ratios. Across all cost ratios, the shape and direction of the effects were very similar. Partial dependence plots for all independent variables, at all cost ratios, are available from the authors upon request.

  4. Rather than relying solely on the linear interpolation used in R, we also overlay a smoothed version of the partial dependence represented by the dotted curve. Smoothing is done using Friedman’s (1984) Super Smoother, with optimal smoothing chosen by cross-validation.

  5. The rug plot is simply the observed covariate value for each case plus some random disturbance, where the disturbance is a drawn from a Uniform (−0.4,0.4) distribution.

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Acknowledgments

This project is supported by Grants No. 2004-WB-GX-0003 and 2005-WB-GX-0011 awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. Points of view in this document are those of the authors and do not represent the views, beliefs, official position, or policies of the U.S. Department of Justice. Additionally, the viewpoints expressed here do not reflect the views or beliefs of the State of Alaska. We thank Tara Henry, MSN, RN, SANEA, SANE-P, for collecting all medical/forensic data.

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Authors and Affiliations

  1. Department of Natural Resources, State of Alaska, 550 W 7th Ave, Suite 1100, Anchorage, AK, 99501, USA

    G. Matthew Snodgrass

  2. Justice Center, University of Alaska Anchorage, 3211 Providence Drive, Anchorage, AK, 99508, USA

    André B. Rosay

  3. School of Public Affairs, University of Colorado Denver, P.O. Box 173364, Campus Box 142, Denver, CO, 80217, USA

    Angela R. Gover

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  1. G. Matthew Snodgrass
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Correspondence to Angela R. Gover.

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Snodgrass, G.M., Rosay, A.B. & Gover, A.R. Modeling the Referral Decision in Sexual Assault Cases: An Application of Random Forests. Am J Crim Just 39, 267–291 (2014). https://doi.org/10.1007/s12103-013-9210-x

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