Document Type

Article

Publication Date

6-2003

Publication Title

Journal of Quantitative Criminology

Abstract

Homicide cases suffer from substantial levels of missing data, a problem largely ignored by criminological researchers. The present research seeks to address this problem by imputing values for unknown victim/offender relationships using the EM algorithm. The analysis is carried out first using homicide data from the Los Angeles Police Department (1994-1998), and then compared with imputations using homicide data for Chicago (1991-1995), using a variety of predictor variables to assess the extent to which they influence the assignment of cases to the various relationship categories. The findings indicate that, contrary to popular belief, many of the unknown cases likely involve intimate partners, other family, and friends/acquaintances. However, they disproportionately involve strangers. Yet even after imputations, stranger homicides do not increase more than approximately 5%. The paper addresses the issue of whether data on victim/offender relationships can be considered missing at random (MAR), and the im-plications of the current findings for both existing and future research on homicide.

Original Citation

Regoeczi, W. C., & Riedet, M. (2003). The Application of Missing Data Estimation Models to the Problem of Unknown Victim/Offender Relationships in Homicide Cases. Journal Of Quantitative Criminology, 19(2), 155-183.

DOI

10.1023/A:1023002220545

Version

Postprint

Volume

19

Issue

2

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