Measuring Violent Crime

This review summarizes developments over the past decade in national data sources that can further our understanding of intimate partner and family violence. Particular attention is given to recent improvements in the National Crime Victimization Survey and Uniform Crime Reporting Program’s National Incident-Based Reporting System as well as to features of the National Intimate Partner and Sexual Violence Survey. Over the past 10 years, these data sources have broadened their collection in areas such as victim demographics, disclosure and access to services. This information can support the research necessary to better respond to victims of intimate partner and family violence.

Hate crime is defined by the FBI as a criminal offense motivated by bias against the following protected categories: race, ethnicity, ancestry, religion, sexual orientation, disability, gender, and gender identity. There are three official sources of hate crime data in the United States: the Uniform Crime Report (UCR), the National Incident‐Based Reporting System (NIBRS, part of the UCR reporting program), and the NCVS (National Crime Victimization Survey). According to the FBI in 2018, of the 16,039 agencies that participated in the UCR reporting program, 2,026 agencies reported a total of 7,120 hate crime incidents. Both the UCR and NCVS reveal that victims were most commonly targeted due to their perceived race/ethnicity. Recommendations to reduce hate crime include condemnation of the hate incident by community leaders and proper identification and prosecution of criminal acts of hate.

The authors apply age standardization methods to the Uniform Crime Reports (UCR) and the National Crime Victimization Survey (NCVS) to assess the effects of age composition changes on long-term (1980 to 1996) and short-term trends (1992 to 1996) in index crimes. Key findings include large age composition effects on crime rates during the 1980s but diminishing effects by the mid-1990s; UCR and NCVS disagree somewhat about long-term trends but agree about short-term trends: UCR shows age-adjusted crime rates rising in the 1980s, with index crime at about the same level today as it was in 1980, whereas the NCVS shows steadily declining rates throughout the 1980 to 1996/1998 period. Both sources show across-the-board declines in crime rates during the Clinton years. The authors interpret the crime trends as due to varied changes in reporting programs as well as basic changes in society and extend the rate adjustment procedures to forecast crime trends into the next century.

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.

The Bureau of Justice Statistics’ Deaths in Custody Reporting Program is the primary source for jail suicide research, though the data is restricted from general dissemination. This study is the first to examine whether jail suicide data obtained from publicly available sources can help inform our understanding of this serious public health problem. Of the 304 suicides that were reported through the DCRP in 2009, roughly 56 percent (N = 170) of those suicides were identified through the open-source search protocol. Each of the sources was assessed based on how much information was collected on the incident and the types of variables available. A descriptive analysis was then conducted on the variables that were present in both data sources. The four variables present in each data source were: (1) demographic characteristics of the victim, (2) the location of occurrence within the facility, (3) the location of occurrence by state, and (4) the size of the facility. Findings demonstrate that the prevalence and correlates of jail suicides are extremely similar in both open-source and official data. However, for almost every variable measured, open-source data captured as much information as official data did, if not more. Further, variables not found in official data were identified in the open-source database, thus allowing researchers to have a more nuanced understanding of the situational characteristics of the event. This research provides support for the argument in favor of including open-source data in jail suicide research as it illustrates how open-source data can be used to provide additional information not originally found in official data. In sum, this research is vital in terms of possible suicide prevention, which may be directly linked to being able to manipulate environmental factors.

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.

This means that accelerated longitudinal design is only suitable for situations that are very homogeneous over time, which is not the case for the study of healthcare professionals' career trajectories. Another source of longitudinal data could be long-term longitudinal surveys, such as the Swiss Household Panel (11) or the German Socio-Economic Panel (12), which provide immediate access to long series of longitudinal data. However, these surveys pursue general objectives of understanding the population as a whole and therefore do not include sufficiently detailed data to study specific topics such as the HCWF.The limitations of prospective longitudinal data motivated us to consider methods allowing data to be collected retrospectively. There are distinct advantages to such a retrospective approach, as all data up to the present moment can be collected at once, which significantly reduces collection costs and results in immediately available and complete data. However, retrospective data are often considered less reliable than prospective data, as they suffer from the cognitive limitations of human beings and their difficulty in accurately and comprehensively recalling events that may have happened decades ago (13). This is particularly true when data are collected using a traditional questionnaire consisting of multiple questions. To overcome this, a special tool called the Life History Calendar (LHC) has been developed to improve the quality of retrospective data by maximizing the use of different cognitive processes related to memory and recollection ( 14).An LHC, as shown in Error! Reference source not found., is a graphical tool in the form of a grid with rows representing periods of time (a month, a trimester, etc.) and columns each representing an area of life (education, health, family life, work, etc.) (15). The various events that have occurred in each area of life, whether they were one-off events (e.g., the birth of a child) or of varying duration (e.g., training or employment), can be indicated in the corresponding columns, with additional details provided if required (exact qualification obtained at the end of training, employment rate, etc.). The decisive advantage of the LHC over other retrospective methods is that it allows the various events that have occurred in different areas to be visually linked, which helps to stimulate respondents' memories by exploiting the cognitive processes associated with the distance in time from past events, their succession, and their positioning within larger periods (16,17). The quality of the data obtained can be further improved by inserting a number of cues into the calendar, such as the respondent's age or a list of events known to many (presidential election, Olympic Games, etc.) (18,19). All of this improves both the number of events reported and their temporal accuracy. LHCs can be used in a wide range of different fields, either to supplement prospective longitudinal surveys or as the sole data collection tool. LHCs have often been filled out on paper with the help of an interviewer, but it is also possible to use online versions of this tool that can be completed independently (20,21). Research shows that the different versions of LHCs provide reliable and comprehensive retrospective data (22).Sequence analysis (23,24) is the most common statistical method for studying life trajectories such as those collected using an LHC (Error! Reference source not found.). Beyond simply listing and representing the trajectories observed for an individual in each area of life, this method makes it possible to identify the most typical sequences, relate the sequences observed in each area, divide individuals into a finite number of groups, interpret these groups using other variables, or even use the groups as explanatory variables in other models. Specific approaches allow data to be weighted in cases of non-representativeness and missing data to be handled appropriately (25).<Insert Figure 2 The LHC is currently one of the most effective methods for collecting retrospective data, such as that required for analyzing career trajectories within the HCWF. This method is now well-established in literature and enables the relatively rapid collection of detailed longitudinal information suitable for highly complex analyses. The use of an LHC can provide healthcare system stakeholders with valuable insights to support more effective management and future planning of the HCWF. This study adhered to ethical guidelines outlined in the Declaration of Helsinki. Ethical approval was obtained from the local Ethics Committee and informed consent was obtained from participants.Confidentiality was maintained throughout the study.

The National Incident-Based Reporting System (NIBRS) is a part of the Uniform Crime Reporting Program (UCR), administered by the Federal Bureau of Investigation (FBI). In the late 1970s, the law enforcement community called for a thorough evaluative study of the UCR with the objective of recommending an expanded and enhanced UCR program to meet law enforcement needs into the 21st century. The FBI fully concurred with the need for an updated program to meet contemporary needs and provided its support, formulating a comprehensive redesign effort. Following a multiyear study, a "Blueprint for the Future of the Uniform Crime Reporting Program" was developed. Using the "Blueprint," and in consultation with local and state law enforcement executives, the FBI formulated new guidelines for the Uniform Crime Reports. The National Incident-Based Reporting System (NIBRS) was implemented to meet these guidelines. NIBRS data are archived at ICPSR as 13 separate data files per year, which may be merged by using linkage variables. The data focus on a variety of aspects of a crime incident. Part 4, Administrative Segment, offers data on the incident itself (date and time). Each crime incident is delineated by one administrative segment record. Also provided are Part 5, Offense Segment (offense type, location, weapon use, and bias motivation), Part 6, Property Segment (type of property loss, property description, property value, drug type and quantity), Part 7, Victim Segment (age, sex, race, ethnicity, and injuries), Part 8, Offender Segment (age, sex, and race), and Part 9, Arrestee Segment (arrest date, age, sex, race, and weapon use). The Batch Header Segment (Parts 1-3) separates and identifies individual police agencies by Originating Agency Identifier (ORI). Batch Header information, which is contained on three records for each ORI, includes agency name, geographic location, and population of the area. Part 10, Group B Arrest Report Segment, includes arrestee data for Group B crimes. Window Segments files (Parts 11-13) pertain to incidents for which the complete Group A Incident Report was not submitted to the FBI. In general, a Window Segment record will be generated if the incident occurred prior to January 1 of the previous year or if the incident occurred prior to when the agency started NIBRS reporting. As with the UCR, participation in NIBRS is voluntary on the part of law enforcement agencies. The data are not a representative sample of crime in the United States.

This study compared data from death certificates (DC), medical examiner (ME) reports, and Uniform Crime Reporting (UCR) Program for homicides among children age 5 years or younger in Jefferson County, Alabama between 1988 and 1998. Records from each source were matched independently to records from the other two sources. Kappa coefficients measured the chance-corrected agreement between sources. The strongest correlations were for victim's gender and race. Correlations were weakest for victim's relationship to the offender and offender's gender. DC and ME data demonstrated excellent agreement and provided a comprehensive count of homicides in Jefferson County, whereas UCR comparisons had lower correlations. However, the UCR is a valuable source of child homicide data in the United States that is underutilized in public health research.

Summary Review of Data Sources for School to Work Transitions by Youth with Disabilities - Policy Brief

This paper analyses a previously unused source of data – the All Media and Product Survey (AMPS) – to arrive at alternative estimates of the post‐transition poverty path. The motivations for using this non‐official data source are twofold: concern over the comparability of the existing official post‐transition datasets – the Income and Expenditure Survey (IES) and Population Census – and a desire to extend analysis of poverty trends beyond 2001. While official data sources are generally preferred for purposes of poverty analysis, the IES and Census collect data at long (5 or 10 year) intervals, and additional years pass before these datasets become available to the public. In some cases there is also concern about data comparability between surveys. The expenditure data contained in the General Household Survey are available annually, although data are captured in a small number of categories that are not very conducive to analysis at the lower end of the income distribution. Analysis on AMPS data confirms the large decline in poverty implied by an increase of R18 billion (in 2000 Rand) in social grant payments between 2000 and 2004. The direction of this trend is consistent with recent research findings based on more frequently analysed data sources, including the work done by Agüero, Carter and May (2005), Seekings (2006) and Meth (2006).

Applying Dickey-Fuller time series techniques in tandem with intuitive plot-displays, we examine recent trends in girls' violence and the gender gap as reported in four major sources of longitudinal data on youth violence. These sources are arrest statistics of the Uniform Crime Reports, victimization data of the National Crime Victimization Survey (where the victim identifies sex of offender) and self-reported violent behavior of Monitoring the Future and National Youth Risk Behavior Survey. We find that the rise in girls' violence over the past one to two decades as counted in police arrest data from the Uniform Crime Reports is not borne out in unofficial longitudinal sources. Several net-widening policy shifts have apparently escalated girls' arrest-proneness: first, stretching definitions of violence to include more minor incidents that girls in relative terms are more likely to commit; second, increased policing of violence between intimates and in private settings (for example, home, school) where girls' violence is more widespread; and, third, less tolerant family and societal attitudes toward juvenile females. These developments reflect both a growing intolerance of violence in the law and among the citizenry and an expanded application of preventive punishment and risk management strategies that emphasize early identification and enhanced formal control of problem individuals or groups, particularly problem youth.

More than two decades ago, the U.S. Department of Justice released its recommendations for a new data collection method for the Uniform Crime Reporting Program that would become the National Incident-Based Reporting System (NIBRS). The full potential of NIBRS data has not been realized based on assumptions that these data are inherently biased due to a lack of national coverage and overrepresentation by law enforcement agencies serving smaller populations. To date, no research has assessed the amount of nonresponse or the degree to which NIBRS data are biased for generating crime estimates. Although this study confirms an overall low response rate for NIBRS reporting, higher rates are found for particular subnational population groups. An examination of violent crime rates and change estimates suggests that the amount of bias in NIBRS is not so small as to be ignorable but is not so considerable as to warrant abandoning these data altogether.

This paper compares and attempts to integrate the results of the two largest international empirical sources of crime and criminal justice data (the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems and the International Crime Victim Survey) with respect to world crime levels. These two sources provide different pictures of conventional crime from an international perspective. One of the main factors contributing to differences in crime levels based on these two international data sources relates to police reporting patterns. These are much higher in the industrialized world compared with other developmental regions. Differences are due to a number of factors related to the propensity to report to the police, including the degree of insurance coverage and the public confidence in police. Comparative analysis stimulates discussion on the relationships between development and crime, particularly in relation to the modernization theory. The results seriously challenge modernization theory, whose empirical base is composed exclusively of official criminal justice statistics. The International Crime Victim Survey empirical base, composed of citizens' direct experience with crime and reporting to the police, serously undermines the very foundations of the prevalent explanatory paradigm regarding development and crime relationships.

The main goal of emergency preparedness is the creation of processes and procedures focused on the preservation of human life. One of the leading contributors to loss of life during multi-casualty incidents (MCIs) is the lack of adequate planning, preparation, and simulation. According to a 2017 study, approximately 15 percent of human-caused mass casualty events, with over 10 fatalities, are mass shootings. These events can occur under various circumstances and take place in a wide range of venues, such as schools, offices, and outdoor events, presenting a wide range of unique challenges. To address these more effectively through procedures and policies, more research is need, including simulation and creation of digital twins, all of which have proven beneficial in gathering insights. This is especially true when conducting drills that are not practical or possible, as they do not allow for the multitude of responses to active shooter events. Current research models in use today treat the victims of these simulations either as "killed" or "unaffected." This binary approach is suitable for many simulations when the timeliness of interventions is of no concern, but it does not allow for higher-fidelity simulation, which may be beneficial when developing response and safety protocols for a specific event or specific facility. Simulating physiological decline is beneficial to improving realism and will lead to response protocol improvement. Increased fidelity can help assess the effects of active bystanders voluntarily and opportunistically providing medical first response. Furthermore, this allows us to assess the response of others who have different primary functions during those events, such as the School Resource Officer, or a tactical medic attached to Special Weapons And Tactics during the process of building clearing. Last but not least, this type of simulation can inform and improve how a lockdown is conducted. In mass shooting events, uncontrolled bleeding is often the proximate cause of death for victims. Several data sources were consulted to simulate exsanguination, which helped quantify and describe blood loss based on different types of injuries. This work summarizes our findings and provides a practical guide for the implementation of these findings. In additional papers, the authors cover the process of blood loss mitigation and provide a reference library for implementation in AnyLogic. This research focuses on the simulation of the initial injury and bleeding control mitigation, efforts which by historical context are limited to the first hour of treatment, the so-called golden hour of trauma management. The circulatory system is complex and has several compensatory mechanisms. The efficiency and timing of each are predictable but have some variability to every individual. This work mainly focuses on the primary effectors and simulates the overall process defined by statistical data from peer-reviewed studies. Ultimately, this work presents a quantitative model of blood loss as a function of time, injury placement, and individual victim variability such as age, weight, and gender, which are suitable for computer simulation. Data were validated using empirical datasets. While no model is perfect, the authors propose a common framework for future simulation work that different researchers can use on a known standard deterministic model. This allows for better control for the blood loss process variables while testing other hypotheses related to emergency response for similar events.