Abstract
This study took advantage of the new open-source Extremist Crime Database (ECDB) to overcome obstacles to studying domestic far-right terrorism from a criminological perspective. In the past, exclusive definitions and inclusion criteria have limited available data on violent crimes committed by domestic far-right terrorists, and official data on violent crimes fail to capture offenders’ links to domestic far-right terrorism and ideological motivation (e.g., anti-government, anti-abortion, anti-religion). Therefore, little is known about the nature of far-right terrorist violence and how such violence is similar to and different from routine or more common forms of violence. Focusing on homicides, this study addressed why and how open-source terrorism data and official crime data can be comparatively analyzed. In doing so, we also demonstrate the utility of synthesizing terrorism and official crime data sources. Data on 108 far-right terrorist homicides were taken from the ECDB. Data on 540 common homicides (five comparison homicides for each far-right terrorist homicide) were randomly sampled from the 2000 Supplementary Homicide Reports. Using multiple imputation by chained equations and logistic regression, we imputed missing values and estimated models to compare the two homicide types on 12 different victim, offender, and event characteristics. Relative to common homicides, we found that far-right terrorist homicides were significantly more likely to have white offenders, multiple victims, multiple offenders, and to occur between strangers, and they were significantly less likely to have white victims, to be carried out with a firearm, and to occur in cities with more than 100,000 residents.
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Notes
This research was supported by the United States Department of Homeland Security through the National Consortium for the Study of Terrorism and Responses to Terrorism (START), grant number N00140510629. Any opinions, findings, and conclusions or recommendations in this document are those of the authors and do not necessarily reflect views of the US Department of Homeland Security.
Although not a focus of the current study, the ECDB Project is collecting open-source data for other domestic extremist crimes including bombings and arsons committed by animal and environmental extremists, as well as homicides, attempted homicides, and bombing plots by homegrown jihadists.
The RAND-MIPT Terrorism Incident Database Project has been subsumed by the RAND Database of Worldwide Terrorism Incidents (http://www.rand.org/nsrd/projects/terrorism-incidents/).
Though having multiple research assistants involved in data coding process is advantageous in regards to efficiency, there may be legitimate concerns over the inter-rater reliability. There were a number of ways in which this study ensured successful inter-rater reliability among open-source data coders for the ECDB project. First, all went through extensive training on the coding process for each variable. Each new coder entered into a probationary status in which he or she coded a number of duplicate cases as seasoned coders for the purposes of comparison. In this way, any inconsistencies were addressed early in the coding process. Second, each codebook includes a variable to capture specific coders’ names. Therefore, this study was able to explore over time the data for abnormalities across data coders. Third, open-source coding occurred in stages, which increased the chances that all available information from open sources was captured. After open-source data collectors compiled search reports on each case, data collectors conducted target searches based on information uncovered during the initial search. This presented the continued opportunity for coders to recheck their past work, as well as the work of fellow open-source coders.
Often filling in values for variables included in the current study required little interpretation by coders because the variables captured the basic facts of each fatal crime. Nonetheless, it remains important to constantly track the level of coding reliability over time. The ECDB presents a good opportunity to comparatively examine the quality of coding for the 108 ideologically motivated far-right extremist homicides. Data for these homicides were originally coded in early 2008 primarily by three research assistants. However, in 2010 all of the homicide variables included in this study were reviewed and, if necessary, recoded by a new coder. In this way, a new coder was able to update the homicide cases based on newly available information and catch any coding errors by the original coder. The coding of variables was compared across coders and the rate of coder agreement was calculated across the initial and secondary coding stages for each variable, including suspect age (93%), suspect race (100%), suspect gender (100%), weapon (100%), number of suspects (97%), number of deaths (98%), region (state) (99%), and population (city size) (93%). In effect, inter-rater reliability across coders was not deemed a threat to the quality of far-right extremist homicide data.
The total number of far-right ideological (and non-ideological) homicides used in the current study differ slightly from those used by Chermak et al. (this issue) because in our analysis, for the purposes of comparison with SHR data, we selected only the first victim and offender dyad in those homicides involving multiple victims. In addition, late in the process of our analysis it was discovered that four far-right terrorist homicide events had inadvertently failed to be transferred from the ECDB to our list of homicides. Though we did not add a further five common comparison homicides from the 2000 SHR for each of these new homicides, we did add the four new ideological homicides to our database and reestimated the model. The addition of these cases had no tangible effect on the size of the odds ratios and no impact on the inferences drawn from the results shown in Table 4.
While the number of homicides in the SHR would allow us to provide a much larger comparison group than we have selected, any incremental benefits in statistical power gained from adding additional controls diminishes rapidly beyond about three cases per control (Breslow 1982), and going beyond five controls per case provides no useful increase in precision.
The Census West includes Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.
Several of the 28 missing observations on the white variable from the ECDB were Hispanic, but it was unknown whether they were white Hispanic or black Hispanic. In order to remain consistent with comparison cases from the SHR—which classifies victims as white, black, Native American, or Asian, with a separate field for Hispanic ethnicity for whites and blacks—we classified as missing any cases in which the person was Hispanic but it was unknown whether they were white or black.
Whereas the EM approach is based on maximum-likelihood estimates that describe a likelihood function that is averaged over a predictive distribution for the missing values, multiple imputation employs the same type of averaging but uses Monte Carlo methods.
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Acknowledgments
We thank Joshua D. Freilich and Steven M. Chermak for allowing us to use data from the Extremist Crime Database (ECDB). We thank Colin Loftin, Scott Long, and Jim Lynch for advice on the analyses performed here, and Gary LaFree for his helpful comments on an earlier draft.
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Gruenewald, J., Pridemore, W.A. A Comparison of Ideologically-Motivated Homicides from the New Extremist Crime Database and Homicides from the Supplementary Homicide Reports Using Multiple Imputation by Chained Equations to Handle Missing Values. J Quant Criminol 28, 141–162 (2012). https://doi.org/10.1007/s10940-011-9155-5
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DOI: https://doi.org/10.1007/s10940-011-9155-5