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Analyzing the NIBRS Data: the Impact of the Number of Records Used per Segment

American Journal of Criminal Justice volume 45, pages 379–409 (2020)Cite this article

Abstract

In an effort to upgrade and improve criminal justice statistics, the Uniform Crime Reporting (UCR) program is currently in the process of transitioning from the Summary Reporting System (SRS) to the National Incident-Based Reporting System (NIBRS). While this transition will increase the capacity for law enforcement agencies and analysts to make informed decisions regarding crime and policing policy, the detail of NIBRS increases analytic complexity. More specifically, NIBRS includes variables in six data segments, five of which can include multiple records per incident. As a result, analysts must decide how many records to use. However, there is currently no guidance for best practices in making this decision. This research addresses this gap by examining the impact of this decision on descriptive analyses and regression estimates. Results indicate some estimates are measured accurately using only one record, using three records reduces inaccuracy, and with some exceptions, using more than three records is methodologically unnecessary. As the NIBRS data become increasingly representative and useful in the coming years, it will be important that they are used both efficiently and effectively. Taken together, this research suggests that for most analyses there is substantial consistency when using at least three records per data segment but that there are some cases for which the number of records is consequential and researchers should consider the methodological and theoretical implications of each strategy when choosing between them.

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Notes

  1. It is important to note that, while we call this approach the complete-information approach, we do not mean to imply that every single record is included. Instead, this approach is to use as many records as is reasonable for the research question at hand. It is not practical, for example, for a researcher to include information on all possible 999 victim records in their analysis. It may be practical, however, to include information on up to 10 victims in the analysis; doing so using the 2015 NIBRS data as we do here accounts for 99.99% of incidents.

  2. As previously described, the extract files released by NIBRS include complete information for the segment they are named for, but only the first three records for the remaining segments. For the purposes of this study, we limit all segments to the first three records because ours is an incident-level analysis.

  3. Other race includes offenders who identified as either American Indian/Alaskan Native or Asian/Pacific Islander. Other race offenders compose less than 2% of the total sample. Further, while NIBRS has begun collecting information on offender ethnicity in recent years, we elected to exclude this measure from the analyses because of a high degree of missing data.

  4. The National Incident-Based Reporting System data include the use of “body parts” (e.g., hands, fists, feet) as a possible personal weapon. These offenses were coded as not involving a weapon because it can be assumed that all offenders have these potential weapons, and coding them as weapons would skew the data positively regarding the prevalence of weapon use (see Cunningham & Vandiver, 2016).

  5. No incidents in the 2015 NIBRS data included more than eight offenses per incident. As such, the complete-information approach includes up to eight records for the offense segment, instead of the ten records used in the other segments.

  6. It should be noted that while individual police departments might employ different strategies, there is no rule in NIBRS regarding the order in which offense types should be reported. For example, for incidents involving a robbery and a sexual assault, there is no formal rule regarding whether the robbery or the sexual assault is the first reported offense.

  7. As some of our incident characteristics are coded as dummy variables, we present point-biserial correlations for the bivariate correlations between these variables and the number of records to allow for comparability with the other correlations. Point-biserial correlations can be interpreted as the effect size of a difference-of-means test for the difference between the means of the two groups of the dummy variable (Kornbrot, 2005).

  8. As all homicide offenses involve victim death, we do not include victim injury as a predictor variable for analyses that include homicide offenses.

  9. Although substantive interpretation is not a focus of the current manuscript, we feel it necessary to comment on this coefficient. While the expectation may be that weapon use should increase the likelihood of arrest, we suspect that offenders using weapons are more able to evade capture. Such a suspicion is purely speculative, but seems reasonable.

  10. This does not, of course, mean that all records need to be included; to do so would be infeasible. But the analyses in this research, for example, were able to include and account for more than 99.9% of offender observations by limiting the number of offender records to ten.

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Funding

This research was completed in part with funding from the Bureau of Justice Statistics (2015-R2-CX-K032). The views presented represent those of the authors, and do not necessarily represent those of the Bureau of Justice Statistics.

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  1. College of Criminology and Criminal Justice, Florida State University, 112 S. Copeland St, Eppes Hall, Tallahassee, FL, 32306, USA

    Brendan Lantz & Marin R. Wenger

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  1. Brendan Lantz
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  2. Marin R. Wenger
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Correspondence to Brendan Lantz.

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Lantz, B., Wenger, M.R. Analyzing the NIBRS Data: the Impact of the Number of Records Used per Segment. Am J Crim Just 45, 379–409 (2020). https://doi.org/10.1007/s12103-019-09513-4

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Keywords

  • NIBRS
  • UCR
  • Administrative data
  • Summary reporting system
  • Crime analysis