DOI: 10.3886/icpsr20060.v1
title: New Approach to Evaluating Supplementary Homicide Report (SHR) Data Imputation, 1990-1995
title: Version 1

description: The purpose of the project was to learn more about patterns of homicide in the United States by strengthening the ability to make imputations for Supplementary Homicide Report (SHR) data with missing values. Supplementary Homicide Reports (SHR) and local police data from Chicago, Illinois, St. Louis, Missouri, Philadelphia, Pennsylvania, and Phoenix, Arizona, for 1990 to 1995 were merged to create a master file by linking on overlapping information on victim and incident characteristics. Through this process, 96 percent of the cases in the SHR were matched with cases in the police files. The data contain variables for three types of cases: complete in SHR, missing offender and incident information in SHR but known in police report, and missing offender and incident information in both. The merged file allows estimation of similarities and differences between the cases with known offender characteristics in the SHR and those in the other two categories. The accuracy of existing data imputation methods can be assessed by comparing imputed values in an "incomplete" dataset (the SHR), generated by the three imputation strategies discussed in the literature, with the actual values in a known "complete" dataset (combined SHR and police data). Variables from both the Supplemental Homicide Reports and the additional police report offense data include incident date, victim characteristics, offender characteristics, incident details, geographic information, as well as variables regarding the matching procedure.
description: The purpose of the project was to learn more about patterns of homicide in the United States by strengthening the ability to make imputations for Supplementary Homicide Report (SHR) data with missing values. Several strategies for imputing missing homicide data have been suggested in recent years. However, aside from assessing their logic and the degree to which findings based on imputed data are in accordance with what is known broadly about homicide, such strategies are difficult to evaluate for the reason that the "true" values for cases with missing data cannot be observed. By combining different datasets available through the National Archive of Criminal Justice Data, this project offered a strategy for both testing imputation approaches that have been suggested in the literature and for using these results to advance the development of new methods for imputing SHR data.
description: In order to better understand why data are missing and how missing and nonmissing data differ, Supplementary Homicide Reports (SHR) and local police data from Chicago, Illinois, St. Louis, Missouri, Philadelphia, Pennsylvania, and Phoenix, Arizona, for 1990 to 1995 were merged to create a master file. The researchers used overlapping information on victim and incident characteristics from the SHR and police data from fields for which the data are very rarely missing even if the offender is unknown (age, sex, and race of victims, month of incident, and weapon) to link the two types of data sources. While this process became challenging when the characteristics of victims and incidents were identical across multiple incidents in a given month, this type of situation was extremely rare as usually at least one characteristic varied across incidents. Through this process, 96 percent of the cases in the SHR were matched with cases in the police files. The data contain variables for three types of cases: complete in SHR, missing offender and incident information in SHR but known in police report, and missing offender and incident information in both. The merged file allows estimation of similarities and differences between the cases with known offender characteristics in the SHR and those in the other two categories. The accuracy of existing data imputation methods can be assessed by comparing imputed values in an "incomplete" dataset (the SHR), generated by the three imputation strategies discussed in the literature, with the actual values in a known "complete" dataset (combined SHR and police data), for both case-by-case imputation as well as distributions at the aggregate level.
description: Variables from both the Supplemental Homicide Reports and the police report offense data include incident date (year and month), victim characteristics (age, sex, and race), offender characteristics (age, sex, race, and relationship to victim), and incident details (weapon used, circumstance, and situation of offense). Data also contain geographic variables (UCR state code, agency, population group, region, population, county, and SMSA), as well as matching information, such as from which source the offender data were missing, the number of victims in the SHR data, the number of offenders in the SHR data, the researchers' confidence in the case match, their comments, and several aggregated and derived variables.
description: Data contain three weight variables associated with the SHR data: WTNONE, WTUS, and WTST.
description: ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes..
description: Presence of Common Scales: None
description: Response Rates: Not applicable.
description: Datasets: 
 
 DS1: Dataset
description: record abstracts 
Users are encouraged to refer to the project final report for more information about imputation methods used and the processes the researchers used to assess them.
description: Homicides known to the police in Chicago, Illinois, Philadelphia, Pennsylvania, Phoenix, Arizona, St. Louis, Missouri, from 1990 to 1995. Smallest Geographic Unit: jurisdiction
description: Not applicable.