Multiple Imputation of the Supplementary Homicide Reports, 1976–2005
The Supplementary Homicide Reports (SHR), assembled by the Federal Bureau of Investigation (FBI), have for many years represented the most valuable source of information on the patterns and trends in murder and non-negligent manslaughter. Despite their widespread use by researchers and policy makers...
| Autores principales: | ; |
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| Tipo de documento: | Electrónico Artículo |
| Lenguaje: | Inglés |
| Publicado: |
2009
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| En: |
Journal of quantitative criminology
Año: 2009, Volumen: 25, Número: 1, Páginas: 51-77 |
| Acceso en línea: |
Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
| Journals Online & Print: | Gargar...
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| Verificar disponibilidad: | HBZ Gateway |
| Palabras clave: |
| Sumario: | The Supplementary Homicide Reports (SHR), assembled by the Federal Bureau of Investigation (FBI), have for many years represented the most valuable source of information on the patterns and trends in murder and non-negligent manslaughter. Despite their widespread use by researchers and policy makers alike, these data are not completely without their limitations, the most important of which involves missing or incomplete incident reports. In this analysis, we develop methods for addressing missing data in the 1976–2005 SHR cumulative file, related to both non-reports (unit missingness) and incomplete reports (item missingness). For incomplete case data (that is, missing characteristics on victims, offenders or incidents), we implement a multiple imputation (MI) approach based on a log-linear model for incomplete multivariate categorical data. Then, to adjust for unit missingness, we adopt a weighting scheme linked to FBI annual estimates of homicide counts by state and National Center for Health Statistics mortality data on decedent characteristics in coroners’ reports for deaths classified as homicide. The result is a fully-imputed SHR database for 1976–2005. This paper examines the effects of MI and case weighting on victim/offender/incident characteristics, including standard errors of parameter estimates resulting from imputation uncertainty. |
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| ISSN: | 1573-7799 |
| DOI: | 10.1007/s10940-008-9058-2 |
