Evaluating the Availability of Uniform Crime Reporting Data, and Using Data Management to Make It More Powerful and Accessible

The current study introduces a method to assess hate crime classification error in a state Incident-Based Reporting System. The study identifies and quantifies the “statistical accuracy” of aggregate hate crime data and provides insight from frontline officers about thought processes involved with classifying bias offenses. Random samples of records from two city and two county agencies provided data for the study. A systematic review of official case narratives determined hate crime classification error using state and federal definitions. A focus group sought to inquire about officers’ handling of hate crimes. Undercounting of hate crimes in official data was evident. When error rates were extrapolated, National Incident-Based Reporting System Group A hate crimes were undercounted by 67%. Officers’ responses validated complications involved with classifying hate crimes, particularly, incidents motivated “in part” by bias. Classification errors in reporting hate crimes have an impact on the statistical accuracy of official hate crime statistics. Officers’ offense descriptions provided greater awareness to issues with accurately interpreting and classifying hate crimes. The results yield useful information for officer training, understanding the true magnitude of these crimes, and a precursor for adjusting crime statistics to better estimate the “true” number of hate crimes in the population.

This article examines the coverage of the National Incident-Based Reporting System (NIBRS) as of 2013. We use NIBRS, Uniform Crime Reports (UCR), and Supplementary Homicide Reports to assess the population coverage and index crime coverage of NIBRS. We also examine the correspondence of crime rates between the UCR and agencies that do and do not participate in NIBRS. We found that NIBRS covers 29.3% of the U.S. population and 28% of UCR index crimes. We also found that the crime rates in NIBRS jurisdictions are appreciably lower than jurisdictions that do not participate in NIBRS. As of 2013, therefore, NIBRS data are not representative of the U.S. population, crime counts, or crime rates.

The United States relies on the National Incident-Based Reporting System (NIBRS) as the primary source of crime occurrence. In 2021, crime reporting in the United States transitioned from the Uniform Crime Reports (UCR) Program to NIBRS. In similar fashion, NIBRS data consist of crime known to law enforcement. Law enforcement agencies then report these data to the Federal Bureau of Investigation (FBI). Key to this reporting, and a relevant concern, is that, law enforcement agencies voluntarily report their crime data to the FBI. The transition from UCR to NIBRS data reporting has created monetary and technical hurdles for agencies to overcome to be certified in reporting their NIBRS data. NIBRS is a significant improvement from UCR reporting, benefiting not only law enforcement agencies but the public at large since these data are openly available. NIBRS provides much greater detail about crimes that are committed, the characteristics of offenders and victims, and the general circumstances of the crime. This level of detail is accomplished within NIBRS using 13 different segments that can be adjoined to each other using common incident identifiers. Given the rich data within NIBRS, practitioners and researchers have numerous opportunities to use these data to their advantage. Additionally, based on the structure of NIBRS, the data could be merged with other existing datasets, furthering the depth and understanding of crime occurrence.

National-Incident Based Reporting System (NIBRS) information for 1993 iscompared with two other sets of homicide data to assess the acuracy ofprocedures for estimating age-, sex-, and race-specific arrest counts fromtraditional Uniform Crime Report (UCR) data. The simultaneous age, race, andsex characteristics of offenders provided in the NIBRS arrest and offender segmentsare compared with estimates of the same characteristics derived from summaryUCR arrest data. The results suggest that using UCR marginal totals toestimate arrest counts by race and sex produces reasonably accurateestimates of the number of black and nonblack male arrests for murder andother offenses. Estimates of arrests of females by race and sex are lessaccurate, probably because of the relatively small number of arrests ofwomen and girls. Estimating male arrest counts for specific age groups alsoproduces reasonably accurate estimates. The results suggest that previousresearch employing such estimates may have been misleading in the estimatesof female arrests by race but accurate in the estimates of male arrests byrace. Although the use of summary UCR-based estimates in futurecross-sectional research will become increasingly unnecessary as the NIBRSreplaces the current UCR program, time series research designs on arrest andcrime trends will continue to require estimates.

The Uniform Crime Report (UCR), the nation's primary mechanism of measuring crime levels, has remained relatively unchanged since its inception in the 1920s. While there was little change in the summary collection procedures of the UCR, many believed that fundamental changes were necessary in order to cover more offenses, collect more details on criminal events, and overcome some of its deficiencies and limitations. In the 1980s the UCR Study Task Force completed a report that suggested nothing short of a complete overhaul to the UCR program, which was to focus from then on on incident‐based reporting. The result of this review was the National Incident‐Based Reporting System (NIBRS). The NIBRS was designed to collect more information than the preexisting UCR summary reporting system and to overcome the inherent problems associated with summary reporting. While data efforts under the NIBRS began in the early 1990s, to date the nation's law enforcement agencies have yet to universally adopt the incident‐based data collection program. This entry examines the implementation of the NIBRS in the United States and the problems that have plagued the program.

My chapter is slightly different than the others in this volume. Rather than focusing on a particular topic to frame my discussion of developing research questions and addressing methodological challenges, I use a dataset as the basis for exploring these issues. Specifically, my focus is on the Uniform Crime Reporting (UCR) Program’s National Incident-Based Reporting System (NIBRS). Mike and Steve asked the contributors to be informal and conversational in our entries, which proved to be much more challenging than it sounded. My conversations about NIBRS tend to be highly animated ones that quickly get into the weeds. One reason is that I am thrilled to meet anyone expressing even a passing interest in NIBRS as the NIBRS crowd tends to be rather limited (although our numbers are growing). Another is that I truly love my research (overall and with NIBRS in particular). It continues to amaze me that I am paid essentially to play with data, explore topics of my choosing, and derive the excitement of discovering something new and interesting. In an effort to bring some organization to the present conversation, I start with a brief description of NIBRS for the uninitiated before discussing my experiences using these data in my research.

Data on the incidence and prevalence of hate crimes in the United States come primarily from the Uniform Crime Reports (UCR). Although UCR data undercount most crimes, hate crimes are particularly underreported, especially for some groups. We compare 2000-2011 UCR data in Pennsylvania to data from a state agency that came from police, media, and citizen reports. First, we find that the state-agency database is generally consistent with the UCR data, in terms of absolute counts, correlations, and predictors. Second, we find that UCR data underestimate hate crime rates by a factor of about 1.6 overall and by a factor of 2.5 for rural areas. Moreover, although UCR data on hate crimes show a decrease in the most recent 5-year period, the state agency data show that hate crime incident counts have not dropped. We suggest that using a broader index that includes both the UCR and a database like that in Pennsylvania will give a more complete picture of hate crime.

What are "Official" RecordsThe term "official records" is broad and would include data taken from any set of administrative records in order to measure crime or features of criminal justice. In practice, it is usually taken to refer to police or court records. For present purposes, we will limit discussion to the Uniform Crime Reporting (UCR) Program. This includes the older "Summary Reporting System" with seven different data collections (Offenses Known to the Police also called Return A; Age, Sex and Race of Persons Arrested; Supplementary Homicide Report; Law Enforcement Officers Killed and Assaulted; Full-time Law Enforcement Employees; Arson Offenses Known to Law Enforcement; and Hate Crime), as well as the National Incident-Based Reporting System which is currently under development. The term "UCR" is ambiguous. When we refer to the whole organizational enterprise coordinated by the FBI, we use the term "UCR Program," otherwise we will refer to specific data collections. ... Language: en

This article examines the coverage of the National Incident-Based Reporting System (NIBRS) arrest data as of 2016. We use NIBRS and Uniform Crime Report (UCR), Summary Reporting System on persons arrested. We examine the correspondence of arrests measures between the UCR and NIBRS for overall arrests, and arrests by sex, race, and age. We found that NIBRS arrest data are a close match to UCR data overall and the differences across demographic measures are very slight.

The passage of the Hate Crime Statistics Act of 1990 resulted in the creation of a new crime category within law enforcement. Linking the collection of national hate crime data to the FBI's Uniform Crime Reporting (UCR) Program required changes in policy and action within thousands of police organizations throughout the United States. The commitment to make these changes and the speed to which one might expect them to occur are a function of the forces, both internal and external, that affect attitudes and behaviors relative to this change. A survey instrument was developed to examine these forces and the extent to which they exist in four police jurisidictions from different regions of the country. Two of these organizations collect hate crime data and two do not. We found that each of the four agencies had its own unique "field of forces" affecting attitudes and behaviors regarding hate crime. The study provides both a method of inquiry and important direction to justice professionals who seek to facilitate organizational change within law enforcement toward more active involvement in hate crime data collection.

In 2021, the FBI sought to retire the Uniform Crime Report (UCR) Summary Reporting System (SRS) and fully transition to the National Incident-Based Reporting System (NIBRS). However, it is unclear how this transition to a “NIBRS-only” system might impact statistics on race/ethnicity and crime in the United States. To date, research offers few comparisons of race/ethnicity and crime patterns in NIBRS versus traditional UCR reporting systems. In addition, studies that provide these comparisons were conducted years ago (1) before the full transition to NIBRS when the program had limited coverage (less than 30% U.S. population coverage) and (2) before the rapid growth in U.S. minority groups and especially Hispanic populations seen throughout the twenty-first century. The current study addresses this gap in research by comparing racial/ethnic portraits of crime (cross-sectionally and over time) across four of the FBI's “public facing” official crime data systems—(1) Crime in the United States (CIUS) reports, (2) NIBRS Offender data, (3) NIBRS Arrestee data, and (4) NIBRS Arrestee National Estimates. Findings suggest that Black offending and arrest patterns are generally consistent across data sources, but depictions of Hispanic crime involvement differ sharply depending on which data source is used.

In 2016, the Federal Bureau of Investigation announced that it would stop accepting aggregate-level crime data for its Uniform Crime Reporting (UCR) Program. As of January 2021, the UCR will rely only on incident crime data collected through its National Incident-Based Reporting System (NIBRS). This change ushers in a new era for the UCR and promises to generate opportunities for researchers and their ability to explore a wide range of crime questions. The present chapter seeks to assist those criminologists interested in a better understanding of NIBRS and how these data can be used. This chapter starts with an introduction to NIBRS before turning to a discussion of current examples of ways the data are used and issues to consider when working with fully incident-based UCR, especially concerns that were not present with aggregate crime data.

PurposeThe paper aims to provide a critical review of how variations in the conceptualization and contextualization of hate crime across US cities might impact how their individual law enforcement agencies collect hate crime data. Media reports and political discourses present hate crime as a prevalent problem in the USA. However, this representation of hate crime in the public sphere is not reflected in the relatively low national numbers of hate crimes published annually by the Federal Bureau of Investigation.Design/methodology/approachDrawing primarily on the national hate crime data for the period 2008–2018, this author conducted a secondary research study of the concept, context, extent and law enforcement collection of hate crime data in five cities in the USA.FindingsThis paper is a product of some of the findings of the study, which include the definition of hate crime at the federal, state and city levels and the contextualization of hate crimes at these levels. The findings show inconsistencies in how the five cities and associated law enforcement agencies conceptualize hate crime and in how they collect and report hate crime data at local and national levels.Originality/valueThrough its analysis of how five US cities and the associated law enforcement agencies interpret and respond to hate crime data collection, with recommendations of best practices for hate crime data collection by law enforcement agencies, the paper contributes to the academic and nonacademic debate on hate crime.

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.

Studying hate crime clearance rates provides an opportunity to uncover the factors that influence police effectiveness for a relatively new legal category—one that was designed ostensibly to protect minorities, and that may pose unique challenges for police reporting, defining, and investigation. Using multiple years (2005-2010) of data from the National Incident-Based Reporting System (NIBRS), we estimate event history models to compare the incident-level predictors and relative probability of arrest for hate and nonbias crimes. As an aggregate category, we find hate crimes are less likely to clear than nonbias crimes. However, the most prototypical hate crimes—White-on-non-White incidents motivated by racial and ethnic bias—are as likely to clear as the most successfully cleared nonbias crimes. Our results suggest that only hate crimes that fit popular constructions of “normal victims and offenders” receive investigative outcomes comparable with otherwise similar nonbias offenses.