4.1 Importance of Categorizing Data

For many outcome measures, the findings will be most useful if the data is reported not only in the aggregate but also categorized by specific victim characteristics or demographics. This additional information can provide staff valuable insights for identifying training needs — as well as a factual understanding of the community they are serving.

This added information may provide insight relevant to interactions with victims, particular victim vulnerabilities, and case outcomes specific to particular subgroups. Information pertaining to different groups also can help indicate the extent to which equal access to justice issues are present.

Exhibit 4-1 below provides a set of suggested victim characteristics for consideration. Sites may wish to add other victim characteristics or may decide to report routinely on only a small subset of victim characteristics. Tabulations by other characteristics would be made on an as needed basis.

It would be helpful if all stakeholders within a sexual assault response system jointly identify the victim subgroup categories that should be a priority to track within their system because, e.g., they identify particular victim populations being targeted by offenders, or particular victim populations unable or unwilling to participate in the prosecution of their offenders.

Tabulations of victim characteristics will be easiest to process if the prosecutor’s office has a computerized case management system. A checklist of victim characteristics can be incorporated into the case management system, enabling a prosecutor or other staff to readily record relevant victim’s characteristics in each case. For jurisdictions without a computerized case management system, data can be tabulated using an Excel spreadsheet.

As mentioned above, examining case outcomes based on victim characteristics can be useful for understanding the community that is being served. It can also help with workload planning; e.g., if there are a large number of victims who do not speak English, more time and resources may be dedicated to translation services. The totals for each subgroup can provide officials with important information on case volume and how it is changing over time for each subgroup, as well as in total.

The more difficult step is to link the information on each victim’s characteristics to the outcomes of their cases, so that the relationship between case resolution and victim characteristics can be examined. For example, the data might show that during year 2019, 29% of tourist-victim resolutions were satisfactory. This statistic can be compared to those for other victim subgroups and to the overall percentage of cases resolved successfully. See Chapter 8 for more information.

Suppose the tabulations revealed that during year 2019 the overall rate of successful resolution was 58%. This would suggest problems in handling tourist cases, because a smaller proportion of these cases are resolved satisfactorily.

The ability to categorize case outcomes by victim characteristics provides much more actionable information than simply knowing the overall resolution rate. Such information encourages offices to explore why such differences exist. It might, for example, indicate the need for training or technical assistance. Using only the aggregate or overall value hides the fact that certain groups of victims have unequal access to justice and/or considerably fewer successful outcomes than others. (Of course, other considerations could also explain the difference – one such being that the number of sexual violence crimes within a particular victim characteristic was so small that one or two cases greatly influenced the resulting percentages).

A similar set of suspect characteristics might also be chosen to report outcome data by suspect subgroups. For example, data could compare repeat offenders to single case offenders, and outcomes could highlight case resolution disparities by documenting comparable cases and the sentences given to different racial and ethnic groups.

Variances in suspect subgroup information might indicate issues requiring attention, such as areas for specific technical assistance and training. However, it is important to note that such information, as with most performance data, does not tell what actions should be taken.

Prosecutors’ offices can also track case attrition by victim characteristic. For cases in which sexual violence victims seek help from service/advocacy organizations and do not want to report their assault to law enforcement, the organization can be asked to provide both the total number of victims which do not want to report along with subtotals by victim characteristics.

To protect the victim’s privacy, the victim service organization need only provide the aggregate and subgroup totals. These tallies supplement other information for a fuller picture of the total number and characteristics of sexual violence victims. See Chapter 3 for more information on data collection procedures relating to victims who do not report their assaults to law enforcement.

Be sure to take steps to protect victim privacy when disseminating this data. Some jurisdictions may have such small populations of certain demographics that data categorized by these victim subgroups may inadvertently reveal a particular victim’s identity or other confidential information. Ensure compliance with applicable confidentiality and privacy laws. For more guidance on ensuring victim privacy, contact the authors of this volume.85

It should be noted that data for some victim characteristics might not be available for every case. Any substantial data gaps should be identified when reporting the information so users of the information are alerted to possible data accuracy problems.

Candidate Victim Categories for Analyzing Sexual Violence Case Outcomes
  1. Age
  2. Gender identity or presentation
  3. Sexual orientation
  4. Race or ethnicity
  5. Income group
  6. Geographical location of the assault
  7. Educational level
  8. Family size
  9. Disability status
  10. Presence of cognitive disability
  11. Past history of alcohol or drug use/abuse
  12. Past criminal justice involvement
  13. Previous victim of sexual violence
  14. Relationship to the offender
  15. Sexually exploited person
  16. Tourist
  17. Non-resident
  18. Student
  19. Homeless or transient