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The influence of forensic evidence on the case outcomes of homicide incidents

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  • Baskin, Deborah
  • Sommers, Ira

Abstract

Objective In spite of the growth of forensic science services little published research exists related to the impact of forensic evidence on criminal case outcomes. The present study focused on the influence of forensic evidence on the case processing of homicide incidents.Materials and Methods The study utilized a prospective analysis of official record data that followed homicide cases in five jurisdictions from the time of police incident report to final criminal disposition.Results The results showed that most homicides went unsolved (34.5% conviction rate). Only 55.5% of the 400 homicide incidents resulted in arrest of which 77% were referred to the district attorney. On the other hand, 94% of cases referred to the district attorney were charged. Cases were more likely to have arrests, referrals, and charges when witnesses provided information to the police. Suspects who knew their victims were more likely to be arrested and referred to the district attorney. Homicides committed with firearms were less likely to be cleared. The most noteworthy finding was that none of the forensic evidence variables significantly influenced criminal justice outcomes.Conclusions The study results suggest that forensic evidence is auxiliary and non-determinative for homicide cases.

Suggested Citation

  • Baskin, Deborah & Sommers, Ira, 2010. "The influence of forensic evidence on the case outcomes of homicide incidents," Journal of Criminal Justice, Elsevier, vol. 38(6), pages 1141-1149, November.
    • Handle: RePEc:eee:jcjust:v:38:y::i:6:p:1141-1149
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    References listed on IDEAS

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    1. Lee, Lung-Fei, 1983. "Generalized Econometric Models with Selectivity," Econometrica, Econometric Society, vol. 51(2), pages 507-512, March.
    2. Lee, Catherine, 2005. "The value of life in death: Multiple regression and event history analyses of homicide clearance in Los Angeles County," Journal of Criminal Justice, Elsevier, vol. 33(6), pages 527-534.
    3. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
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    Cited by:

    1. Jung, Yeondae & Wheeler, Andrew Palmer, 2019. "The effect of public surveillance cameras on crime clearance rates," SocArXiv eh5bg, Center for Open Science.
    2. Chopin, Julien & Beauregard, Eric & Bitzer, Sonja & Reale, Kylie, 2019. "Rapists' behaviors to avoid police detection," Journal of Criminal Justice, Elsevier, vol. 61(C), pages 81-89.
    3. Gian Maria Campedelli, 2022. "Explainable Machine Learning for Predicting Homicide Clearance in the United States," Papers 2203.04768, arXiv.org.
    4. Campedelli, Gian Maria, 2022. "Explainable machine learning for predicting homicide clearance in the United States," Journal of Criminal Justice, Elsevier, vol. 79(C).
    5. August Daniel Sutmuller & Marielle den Hengst & Ana Isabel Barros & Pieter van Gelder, 2020. "Getting the Perpetrator Incorporated and Prioritized in Homicide Investigations: The Development and Evaluation of a Case-Specific Element Library (C-SEL)," IJERPH, MDPI, vol. 17(17), pages 1-19, September.
    6. Beauregard, Eric & Martineau, Melissa, 2014. "No body, no crime? The role of forensic awareness in avoiding police detection in cases of sexual homicide," Journal of Criminal Justice, Elsevier, vol. 42(2), pages 213-220.
    7. Chopin, Julien & Beauregard, Eric & Bitzer, Sonja, 2020. "Factors influencing the use of forensic awareness strategies in sexual homicide," Journal of Criminal Justice, Elsevier, vol. 71(C).

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