Measurement Matters: Comparing Old and New Definitions of Rape in Federal Statistical Reporting.

521470 research-article2014

SAXXXX10.1177/1079063214521470Sexual Abuse: A Journal of Research and TreatmentBierie and Davis-Siegel

Article

Measurement Matters: Comparing Old and New Definitions of Rape in Federal Statistical Reporting

Sexual Abuse: A Journal of Research and Treatment 2015, Vol. 27(5) 443–459 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1079063214521470 sax.sagepub.com

David M. Bierie1 and James C. Davis-Siegel2

Abstract National statistics on the incidence of rape play an important role in the work of policymakers and academics. The Uniform Crime Reports (UCR) have provided some of the most widely used and influential statistics on the incidence of rape across the United States over the past 80 years. The definition of rape used by UCR changed in 2012 to include substantially more types of sexual assault. This article draws on 20 years of data from the National Incident-Based Reporting System to describe the impact this definitional change will have on estimates of the incidence of rape and trends over time. Drawing on time series as well as panel random effects methodologies, we show that 40% of sexual assaults have been excluded by the prior definition and that the magnitude of this error has grown over time. However, the overall trend in rape over time (year-to-year change) was not substantially different when comparing events meeting the prior definition and the subgroups of sexual assault that will now be counted. Keywords female sexual offenders, sex offender policy, rape, incest, child molestation, child pornography

1United

States Marshals Service, Alexandria, VA, USA of California, Seal Beach, CA, USA

2University

Corresponding Author: David M. Bierie, United States Marshals Service, 2604 Jefferson Davis Highway, Alexandria, VA 22301, USA. Email: [email protected]

Downloaded from sax.sagepub.com by guest on November 16, 2015

444

Sexual Abuse 27(5)

National Statistics Measuring the Incidence of Rape National crime statistics play a critical role in policy making, development of academic theory, and law enforcement activity in the field. One of the primary data sources for national statistics has been the Uniform Crime Reports (UCR), a system developed by the International Association of Chiefs of Police (IACP) in 1928 and adopted by the Federal Bureau of Investigation (FBI) in the 1930s (FBI, 2004). The data are used in myriad academic and policy applications by providing, law enforcement with data for use in budget formulation, planning, resource allocation, assessment of police operations, etc., to help address the crime problem at various levels. Chambers of commerce and tourism agencies examine these data to see how they impact the particular geographic jurisdictions they represent. Criminal justice researchers study the nature, cause, and movement of crime over time. Legislators draft anti-crime measures using the research findings and recommendations of law enforcement administrators, planners, and public and private entities concerned with the problem of crime. The news media use the crime statistics provided by the UCR Program to inform the public about the state of crime. (FBI, 2011b, p. 1)

The UCR Program records the total number of rapes reported to law enforcement across more than 95% of all police departments in the United States. It has remained an authoritative source for national statistics on sexual assault for nearly a century. As such, the UCR has guided policy at the federal, state, and local levels—impacting the allocation of funds and design of programs to address sexual assault. This impact includes planning related to shelters, hospitals, specialized training for law enforcement, evidence collection infrastructure, allocating funds for research, providing resources for prosecution, and prison facilities and treatment capacity. Academic research and theory development draws on rape rates, frequencies, and trends to inform ideas about sexual offending, test between competing theories, and evaluate the impact of policy on sexual assault. The counting of rape cases by the UCR came under fire in recent years as political action groups became aware of the way rape was defined by the FBI. This movement culminated in the Feminist Majority’s “Rape Is Rape” campaign in which more than 160,000 emails were sent to the FBI demanding the definition be changed (Terkel, 2012). As a result, researchers and policymakers became more aware of, and troubled by, the highly select criteria required for a sexual assault to “count” in the national reporting system.1 That definition held that a sexual assault incident would only be included if it involved the “carnal knowledge of a female, forcibly and against her will,” where carnal knowledge was defined as “the slightest penetration of the sexual organ of a female (vagina) by the sexual organ of the male (penis)” (FBI, 2004, p. 19). The definition was troubling because it inherently discounted sexual assault of any male victim, sexual assault by any female offender, and other acts of sexual assault (such as oral, anal, digital, or other forms of illegal sexual acts). In particular, this definition may have (a) produced an underestimation of the incidence of sexual assault, and (b) masked important differences in trends between types of sexual assault. For


445

Bierie and Davis-Siegel

example, the FBI reports that rape has declined steadily for most of the previous decade (FBI, 2011a). We do not know whether this pattern holds true for other forms of sexual assault. Starting in the 2012 reporting period, the UCR definition of rape changed to “the penetration, no matter how slight, of the vagina or anus, with any body part or object, or oral penetration by a sex organ of another person, without the consent of the victim” (U.S. Department of Justice, 2012, p. 1). The change will, inherently, increase the reported incidence of sexual assault. It may also alter the national picture of the way in which the incidence of reported sexual assault has changed over time. That is, it is possible that new-definition cases may have grown or declined differently than the old-definition rape incidents. We might expect, for example, that targeted interventions and social movements of the past two decades that have primarily focused on female victims (e.g., Take Back the Night) have led to some unique changes in those forms of sexual aggression over time. These movements may have had a larger effect on rape that was previously counted relative to the types not counted in the prior definition (e.g., male victim sexual assaults, incest, statutory rape). To the degree there have been fewer or less intensive social movements focused on these other subtypes of sexual assault, the old-definition and new-definition incidence may have changed differently over time. We might also expect divergent patterns of sexual assault for old-definition and some new-definition subtypes as a function of changes in the availability of victims over time (e.g., male vs. female sexual assault victimizations). Research indicates that 25% of male victims of sexual assault are victimized prior to age 10, whereas this is only true for 12% of female victims. In contrast, females enter their highest risk time period between the ages of 20 and 24—a time period when risk to male victims is extremely low (Black et al., 2011). Census data show that the male population under age 10 has decreased by 2.4% between 2000 and 2010, and the female population aged 20 to 24 years has increased by nearly 14% over the same time period (Howden & Meyer, 2011). Given this, we might expect female sexual victimization counts to change over time differently than male sexual victimization counts. This expectation would derive from differences in the number of potential victims available in each gender group and the age-graded targeting of predators who assault male versus female victims. The change in the UCR definition of rape likely represents a strong and substantial improvement in the nation’s capacity to measure sexual assault rates over time and across geography. However, it also represents a critical problem for those who rely on the accuracy of the statistics—there is no clear understanding of what the quality of error has been in the past, what impact the change is likely to have when annual rates are announced, and whether a better understanding of past rape incidence can better inform current and future research on sexual assault. Are there lessons to be learned by applying the new definition of rape to national data recording incidents that occurred during prior decades?


446

Sexual Abuse 27(5)

Current Study There is no body of literature evaluating and estimating the impact of the new definition of rape on current and past statistics. All else constant, broadening what is meant by rape will result in an increase in the reported frequency of rape in the coming years. However, no one knows by how much (FBI, 2011a; Greenfield, 1997). In part, this problem will be self-correcting—new statistics will eventually be released that detail the incidence of sexual assault using the new definition, and these data can be compared with the prior year’s incidence.2 However, doing so will only partially address the problem at hand. Academic researchers will need to know whether the difference in incidence observed in 2012 is the same as in previous years. Likewise, researchers will need to know whether prior work relying on UCR should be reinterpreted in light of potential changes in what is known about the likely rate of error in those prior estimates. Thus, two research questions are addressed in this study: Research Question 1: What is the difference in the incidence of sexual assault estimated under the old and new definitions of rape? Research Question 2: Are there differences in trends among the subgroups of sexual assault as compared with the traditional UCR definition (e.g., do trends in “male victim rape” vary from rape trends in the traditional definition)?

Method Data This study draws on the National Incident-Based Reporting System (NIBRS). NIBRS was designed in the late 1980s and launched in the early 1990s as a tool to supplement and eventually replace the Uniform Crime Reports Summary Statistics program (Poggio, Kennedy, Chaiken, & Carlson, 1985). As of the 2010 data file, NIBRS included data on criminal incidents reported to police within 37 states and among 6,159 (34%) of the nation’s approximately 18,000 police departments. The data system is housed by the FBI and operates as a centralized data collection center for police departments in the program. Participating agencies upload data directly to the FBI data center on reported crimes, regardless of whether the case was closed, for 59 of the most serious crime types. The data system captures information about offenders, victims, arrestees, and criminal incidents. The information is maintained in a series of relational tables such that each table is organized at the unit of analysis applicable to the topic of that data set. For example, the victim-file table contains a row of data for each victim in a crime and approximately 55 columns of data about each victim. The offender table contains one row for each offender in the crime and approximately 10 columns of data about each offender. Each data set contains an identifier for the police agency housing the case and a unique case-identifier that


447


allows the linking of relational tables or collapsing of data within any individual file to the incident level (see Bierie, Detar, & Craun, 2013; Dunn & Zelnock, 1999).3 Although cleaning and coding of specific items described below may have occurred at the table level, all data sets (e.g., the victim table, offender table) were cleaned and then collapsed (aggregated) to the incident level. These incident-level data sets were then merged into a single incident-level data set for analysis (Dunn & Zelnock, 1999; Maxfield, 1999). A special feature of NIBRS is that agencies reporting to the system have included information about each incident in sufficient detail to identify acts of rape under both the prior and the revised definitions.4

Measures A number of offender, victim, and incident characteristics are described in this section. The majority of these items were only used to provide a rich description of the overall data set as well as the differences in characteristics of old- and new-definition cases. A minority of items were also used to define cases as either old- or new-definition incidents, as described below. Offender characteristics included the average age of all offenders in the incident. A dummy variable (1/0) was created to indicate whether any offender in the incident was Male. Also, a dummy was coded to indicate whether any offender was believed to be under the influence of drugs or alcohol during the incident. The Count of arrests referred to the number of people arrested (not charges or cases cleared). The vast majority of offenses had only one offender involved (88.8%). Victim characteristics included Age, which was measured in a year metric and referred to the average age of all victims in the incident (most incidents had only one victim; 89.3%). A dummy was also created to indicate whether any victim was a Juvenile (1/0). An item was created to indicate the proportion of victims who were Non-residents of the jurisdiction in which the sexual assault took place. Two dummies were created to reflect gender of victim(s) in the incident: Male (1/0) and Female (1/0). Note that these are not mutually exclusive—an incident could have both male and female victims. Race of each victim was coded into binary variables of Black, White, Hispanic, or Other (Native American or Asian/Pacific Islander). Once again, these were not mutually exclusive in events with multiple victims. Situational characteristics included a dummy variable to indicate whether the incident involved any attempted or completed Homicide, a dummy indicating whether a Weapon was used, and a dummy identifying a Firearm specifically. A dummy was coded as 1 if the incident had No physical injury (e.g., cuts, bruises, lacerations, broken bones, or broken teeth), 0 otherwise. NIBRS records more than 20 possible Locations for each incident. These were recoded into five categories of Outdoors (e.g., woods, waterways, and construction sites), Buildings (e.g., school, office building, bar), Home, Roadway, and Parking Lot. The Relationship between victims and offenders was reported by victims and aggregated to the category of Family, Acquaintance, Partner, and Stranger. Finally, the item Year referred to the year the crime took place


448

Sexual Abuse 27(5)

(rather than the year reported or cleared). Finally, a measure of the Number of arrests made in each incident was created (ranging from 0 to 20 arrestees). Rape. In the victim-level table, NIBRS contains 10 categorical items describing each potential criminal act carried out against each victim in an incident. The types of sexual assault included5 •• •• •• •• ••

forcible rape, forcible sodomy, forcible sexual assault with an object, non-forcible incest, and non-forcible statutory rape.

First, a dummy variable indicating that the incident would be counted under the old definition of rape was created by using information from these sexual assault fields, items indicating the gender of victim(s), and items indicating the gender of perpetrator(s). Specifically, the item Old rape was coded as 1 (yes) if any crime within an incident involved the forcible rape of a female by a male offender. All other incidents were coded as 0 (no).6 The category “No” is referred to as Missed rape in Table 1—it signifies all cases that would have been counted had the revised definition of rape been in effect during prior years of NIBRS data collection. Second, a dummy variable was created for each sexual assault subtype (listed above). Subtypes were coded as 1 (yes) if any victim suffered from that specific crime. For example, if the incident included a female victim forcibly sodomized, then the forcible sodomy item was coded as 1. If that incident included a female victim forcibly sodomized and a male victim of forcible sexual assault with an object, the dummies for both of these sexual crimes were set to 1. However, each dummy variable was then recoded to 0 (no) if that incident also included a rape event that would have led to the incident being counted under the old definition. Therefore, if the same incident also involved a male forcibly penetrating a female victim’s vagina with his penis, then the dummies for sexual assault subtypes were coded as 0. Thus, the dummy variables reflect the presence of a specific subtype of sexual assault in an incident and are mutually exclusive with the prior-definition rape. Third, a single dummy was created that represented the most serious type of sexual assault perpetrated during an incident. Although assigning a hierarchy of seriousness is inherently subjective, doing so was necessary to describe the growth in sexual assault attributable to each new subtype of sexual assault without double counting incidents that had multiple forms of assault. The hierarchical order tracked the following list: 1. 2. 3. 4. 5. 6.

Forcible rape (old definition) Forcible rape (male victim) Forcible sodomy Forcible object penetration Non-forcible incest Non-forcible statutory rape


449


Table 1. Comparison of Incidents Counted Under the Old Rape Definition Versus Those Incidents That the New Definition Would Have Also Included (N = 455,234). Old rape

Overall

Minimum Maximum

Victim Count 1 10 Male 0 1 Female 0 1 Age (average) 0 99 Juvenile 0 1 Nonresident 0 1 Race Other 0 1 Black 0 1 White 0 1 Hispanic 0 1 Relationship with the offender Family 0 1 Acquaintance 0 1 Stranger 0 1 Partner 0 1 Incident Locations Buildings 0 1 Outside 0 1 Home 0 1 Road 0 1 Parking lot 0 1 Crime feature No injury 0 1 Gun 0 1 Offender Male 0 1 Age 10 99 Drug/alcohol 0 1 No. of arrests 0 20

M

SD

M

Missed rape SD

M

Difference SD

Old − Missed

1.12 0.43 1.10 0.39 1.14 0.48 −0.05*** 0.11 0.31 0.01 0.10 0.26 0.44 −0.25*** 0.90 0.30 1.00 0.00 0.76 0.43 n/a 19.01 11.68 21.30 11.66 15.64 10.86 5.65*** 0.60 0.49 0.49 0.50 0.77 0.42 −0.29*** 0.18 0.38 0.18 0.39 0.17 0.38 0.01*** 0.01 0.21 0.73 0.06

0.11 0.41 0.45 0.23

0.01 0.22 0.72 0.05

0.11 0.41 0.45 0.22

0.01 0.19 0.74 0.06

0.10 0.00*** 0.39 0.03*** 0.44 −0.02*** 0.25 −0.01***

0.19 0.49 0.22 0.13

0.39 0.50 0.42 0.33

0.14 0.51 0.25 0.13

0.34 0.50 0.43 0.34

0.26 0.47 0.17 0.13

0.44 −0.13*** 0.50 0.04*** 0.38 0.08*** 0.33 0.00**

0.09 0.03 0.73 0.05 0.02

0.28 0.17 0.45 0.23 0.15

0.09 0.03 0.72 0.06 0.02

0.28 0.18 0.45 0.24 0.15

0.09 0.03 0.73 0.04 0.02

0.28 0.00 0.16 0.01*** 0.44 −0.01*** 0.20 0.02*** 0.14 0.01***

0.63 0.02

0.48 0.72 0.45 0.13 0.02 0.15

0.50 0.50 0.01 0.10

0.22*** 0.01***

0.96 0.20 1.00 0.00 0.91 0.29 0.00*** 28.12 12.57 28.88 12.14 26.86 13.16 2.02*** 0.12 0.32 0.15 0.36 0.07 0.25 0.08*** 0.28 0.55 0.27 0.54 0.29 0.56 −0.02***

Note. n/a = not applicable. *p < .05. **p < .01. ***p < .001.

We also explored alternate hierarchies such as prioritizing forcible object penetration over forcible sodomy. Results remained consistent regardless as there were relatively few cases that contained large amounts of overlap between sexual assault subtypes.7


450

Sexual Abuse 27(5)

Analytic Strategy This study proceeds by describing the old- and new-definition cases across offender, victim, and crime characteristics. The analysis then turns to Research Question 1 specifically: a comparison of the incidence of rape under the old and new definitions. In addition, each subcategory of sexual assault was examined over time to assess whether the relative proportions of sexual assault subtypes have varied over the prior two decades. Two methodologies were used to assess whether trends of sexual assault subtypes varied over time (Research Question 2). These analyses were tailored to two issues in NIBRS: (a) there is significant change in the amount of data in the system over time and (b) significant clustering of cases within jurisdictions. The NIBRS data grow each year as more police departments join the system. Thus, the total count of sexual assaults each year increases as a function of changes in reported sexual assaults and other factors such as the population at risk (the number of jurisdictions participating and growth in the U.S. population). In addition, jurisdictions may vary in important ways related to the reporting, recording, and investigation of sexual assault incidents. For both of these reasons, tests were not merely conducted by aggregating counts of sexual assault to the nation and examining change over time (a basic time series approach); instead, tests of trends over time were constructed within a panel time series structure (within-jurisdiction analysis of change over time). The data were collapsed to the jurisdiction-by-year level such that there was a single row of data for each jurisdiction for each year (approximately 44,000 rows). The count of each form of sexual assault was summed such that the data then reflected the count of each sexual assault type per jurisdiction. The data were then analyzed by setting the jurisdiction as a panel and the year variable as a temporal lag. This methodology provides several advantages. It allows the computation and analysis of within-jurisdiction change, and it handles problems associated with the clustering of observations within jurisdictions (e.g., contextual contamination). It also has the benefit of handling left censoring—the fact that numerous jurisdictions varied in the year they joined NIBRS and, thus, the number of comparisons of change for which they could offer data. In short, the analysis tests whether the change in old rape within any one jurisdiction is the same as the amount of change in a given subtype of rape in that same jurisdiction (for each potential measurement of change between years). These within-jurisdiction changes are then averaged across all potential jurisdictions for each year to create an overall estimate of the relationship and significance of the link between the prior and revised rape definition rates. All statistical tests reported below were computed via a negative binomial framework. However, several sensitivity analyses were also computed. The analysis was repeated using random effects Poisson as well as random effects ordinary least squares (OLS) frameworks (results were consistent regardless of functional form). The analysis was also re-estimated within a fixed effects structure rather than random effects— results were again consistent. Finally, tests were computed within a time series ARIMA structure. Here, the data were collapsed to the national level for each year,


451


and the subtypes of rape were each regressed on the old definition. Conclusions were the same as those obtained in the panel time series random effects models.

Results In all, there were 269,656 incidents that would have been counted under the old definition of rape, and 185,578 (40.8%) that would have been missed—for a total of 455,234 analyzable incidents in the data set between 1993 and 2010. An incident may include more than one victim, as was the case in just over 41,000 incidents in these data (approximately 10% of the incidents). Table 1 illustrates differences between the two pools of incidents—those that would have been counted in the prior definition and those that would have been missed. Although case characteristics were not the focus of this study, some information on the victims, offenders, and situations are presented to paint a context around the sample used in the study. The data show that 26% of “missed” sexual assault cases included at least one male victim. In contrast, only 1% of forcible rape cases under the prior definition included a male victim (these were incidents with more than one victim in which one was a female who was forcibly raped by a male). It is noteworthy that the age of victims was substantially lower among cases excluded using the prior definition (21.3 vs. 15.6 years old, p < .001). Likewise, the excluded rape cases were substantially more likely to include at least one juvenile victim in the incident (77% vs. 44%, p < .001). This is consistent with expectations regarding the impact of age on victimization in some subgroups. For example, victims of incest were expected to be younger than old-definition cases. Likewise, the literature shows male victims tend to be significantly younger than female victims (Black et al., 2011). Far more of the missed cases involved victimization within the family (26% vs. 14%, p < .001), as might be expected from the inclusion of non-forcible incest in the new definition. No difference was seen between the two definitions of rape in the proportion of events that included intimate partner violence (13% under both definitions). Likewise, no changes were observed in the location of sexual assaults. The majority of incidents under both definitions occurred in homes (73%) or other buildings (9%). The new definition, however, included substantially more cases with injury. While 72% of old-definition cases included no additional injury, only 50% of the newly counted incidents lacked injury (p < .001). Finally, the data suggest some differences in offenders between the two definitions. The new offender group was slightly younger (2 years) and half as likely to involve substance use by an offender (7% vs. 15%, p < .001). Arrest rates were similar (less than 30%). The first research question centered on differences in the incidence of rape under the competing definitions. These data showed that 269,578 (60%) of the incidents of sexual assault would have been counted under the prior definition. Just over 40% (185,578) were deleted from statistical reporting under the prior definition. Table 2 shows the breakdown of subtypes of sexual assault that were excluded. The data in Table 2 show two important trends with respect to the controlling offense in new-definition incidents. First, the data indicate that the most commonly


452

Sexual Abuse 27(5)

Table 2. Subcategories of Rape Missed by the Old Definition: Comparison of Proportions Falling Into Each Category by Year.

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Total

Rape (prior definition)

Rape (male victim)

Sodomy

Object

Incest

Statutory

0.66 0.69 0.59 0.60 0.60 0.61 0.59 0.60 0.60 0.60 0.59 0.59 0.59 0.59 0.59 0.58 0.58 0.59 0.59

0.05 0.02 0.03 0.03 0.04 0.04 0.05 0.04 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.06 0.05 0.05 0.05

0.09 0.10 0.15 0.15 0.15 0.15 0.16 0.14 0.14 0.13 0.14 0.13 0.13 0.14 0.13 0.13 0.13 0.14 0.13

0.07 0.06 0.08 0.08 0.09 0.09 0.09 0.09 0.08 0.08 0.07 0.07 0.08 0.07 0.07 0.07 0.06 0.06 0.07

0.04 0.02 0.02 0.02 0.03 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03

0.10 0.10 0.12 0.12 0.10 0.09 0.10 0.11 0.11 0.11 0.12 0.13 0.13 0.13 0.13 0.13 0.14 0.13 0.12

Note. The table reduces each incident to the single most serious act of sexual assault occurring in that incident prior to the computation of annual counts. Thus, although 26% of rapes under the new definition include an act of sodomy, sodomy was the controlling offense in half of these incidents (for a total contribution of 13% of cases under the new definition).

missed act of sexual assault under the prior definition has been sodomy (i.e., 13% of all sexual assaults that will now be counted included a controlling offense of sodomy). A small increase (5%) occurred due to the inclusion of missed forcible rape (incidents with male victims of female or male offenders). This was a similar increase as associated with object-based sexual assault (7%) and incest (3%). Again, each category of sexual assault here reflects the most serious form of sexual assault occurring if the incident had multiple forms of sexual offending. The second trend apparent in these data speaks to change over time in the magnitude of error. The data show that excluded incidents grew from 34% to 41% between 1993 and 1995, and then remained at that level through 2010. The second research question focused on changes over time in the subcategories of sexual assault—Were there trends which were missed because agencies were only reporting the old definition of rape? To address this question, data were transformed into a pseudo-rate by computing the count of each sexual assault category for each police jurisdiction (Originating Agency Identifier [ORI]) in each year. It should be noted that these are imperfect forms of rates as the underlying features of a police


453

0

Count of rapes per jurisdiction 2 4 6

8


1995

2000

Year

Rape (prior definition) Rape by object Incest

2005

2010

Forcible rape of male Sodomy Statutory rape

Figure 1. Trends in rape over time: Average within-jurisdiction counts of sexual assault.

Note. The data points in the figure refer to the count of incidents per jurisdiction (averaged across all jurisdictions for that year) that qualify for a given category of rape. Rape categorizations utilized the controlling offense in the incident (i.e., the most serious act of sexual assault).

jurisdiction could vary; for example, the population level of an area may change over time. However, as described above, these rates are compared within jurisdiction and at the same time. This allowed error caused by within-jurisdiction changes in population levels to cancel out when comparing counts of assault produced by the two definitions of rape within any one jurisdiction.8 The counts of sexual assault per jurisdiction over time are presented in Figure 1 below. Table 3 presents tests of differences between each trend line (i.e., the latent trend representation of data points in Figure 1 within a random effects framework). Again, the data were organized such that each police jurisdiction was designated a panel, and the year was set as the time function. The prior definition of rape was regressed on each subcategory, meaning that the coefficients referred to the relationship between change in a subcategory of sexual assault within a given jurisdiction and change in prior-definition rape in that same jurisdiction. These within-jurisdiction comparisons confirm what visual inspection of Figure 1 implies—there is no significant difference in rape trends over time. The old definition of rape did not mask unique underlying trends in the excluded sexual assaults over all or any specific form. Importantly, the z scores in this analysis are exceptionally large. This indicates the consistency of the


454

Sexual Abuse 27(5)

Table 3. Trend Tests Between Rape Subgroups: Random Effects Negative Binomial Regression Over Time.

Any missed sex offense Rape of male Sodomy Statutory rape log Object log Incest

Coefficient

SE

Z

p

.005 .007 .007 .008 .089 .125

0.000 0.000 0.000 0.000 0.008 0.013

43.4 84.21 32.17 20.91 10.58 9.24

.000 .000 .000 .000 .000 .000

Note. Each comparison is bivariate, with coefficients omitted.

link between change in the prior-definition rape incidence and change in each subtype of sexual assault. The model in Table 3 assesses change over time within each police department’s jurisdiction. Although the average police department in NIBRS indicated slightly more than four sexual assaults reported to police under the prior definition of rape in 1993 (see Figure 1), the count may have varied greatly between jurisdictions. Regardless, we computed the change in the count of sexual assaults within each police department over time for old-definition rape, and then computed change in the withinagency count of each subtype. The random effects model then compared that change (within police departments) to generate the test of the relationship between old and new definitions of rape. The random effects tests are confirmed if the models are instead estimated within a time series ARIMA framework, fixed effects regression, and alternate expressions of the functional form of the equation (e.g., Poisson). Again, the data here showed that change in any one subtype of sexual assault was strongly and significantly associated with change in the old-definition cases—there was no significant difference between any of the trend lines (other than the intercept, which reflects the incidence of the sexual assault types).

Discussion National statistics regarding the incidence of sexual assault, as well as changes or trajectories over time, play an important role in academia and policy. One of the most important national reporting sources, the UCR, recently changed the definition used to count a sexual assault in national statistics. The prior definition required the victim to be female and the offender male, limited the acts to those where explicit force was used, and stipulated that the victim’s vagina had to be penetrated by an offender’s penis. The new definition expands force to include any lack of consent, includes both male and female victims, adds female offenders, and records additional forms of sexual assault (i.e., oral or object penetration).


455


Two critical questions emerge from the change in the definition of sexual assault. First, how much sexual assault was missed over the decades as a result of the prior definition? Knowing this will inform academic and legal research that relies on historic data or analyzes change over time. It also may speak to prior research and policy—by helping consumers of information about sexual assault re-interpret prior studies in light of the magnitude and form of errors that were present. This study showed that the incidence of sexual assault reported in UCR was underestimated by 40%. Importantly, this error rate had grown over time. The second research question focused on rape trends. Did the failure to count, track, and report on various forms of sexual assault bias the picture of sexual assault? The data here show that this was not the case. Within any particular police jurisdiction, the trend in each subcategory of rape that had been excluded changed over time in statistically similar ways to the rape that was counted. The general picture of increases and decreases over time were essentially the same. This finding held regardless of a number of sensitivity analyses. It is important to interpret the conclusions of this study with several limitations in mind. First, the data may not be representative of patterns or trends in jurisdictions that do not report to NIBRS. Although a large portion of the United States (34%) is currently involved in the NIBRS program, the majority of police departments do not participate. Furthermore, NIBRS underrepresented urban areas to some degree, particularly in the early 1990s (Maxfield, 1999). There is no way to know for certain whether the difference in incidence or trends observed in this study would apply to other jurisdictions. It is likely that the broad geographic representation of these data, and the sheer size of the sample (hundreds of thousands of cases over a large amount of time), would offer resiliency in these findings to all but the most dramatic selection bias scenarios. For bias to emerge, for example, we would have to assume that the portion of sexual assault with male victims is dramatically different in urban areas relative to NIBRS reporting jurisdictions that have fewer urban areas than the nation as a whole. This may be the case, but there is little empirical or theoretical reason for assuming so. Regardless, our data did not allow us to identify whether there would be any differences in generalizing to other jurisdictions, or how large those differences could be. Second, and related, it is important to recognize that a majority of rape cases are unreported to police and, therefore, are not captured in NIBRS. This means that the information in this study should not be interpreted as an indicator of actual incidence of sexual assault—it is only reflective of officially reported sexual assault. This also implies a caveat when interpreting these trends over time. If the reporting rate differs across types of sexual assault, or the reporting rates for one type of sexual assault have changed over time differently than the reporting rate for another form of sexual assault, the analysis here may be biased. For example, if male victims of sexual assault have become more likely to report their abuse over time, but female victim reporting has remained constant, then this gendered reporting process may cause bias in the analysis comparing sexual assault subtypes over time (see Table 3). There is


456

Sexual Abuse 27(5)

little evidence to date suggesting this is the case. Regardless, bias remains an important potential limitation. Third, the analysis assumes data were entered into NIBRS accurately. The FBI reports that they audit the system at each jurisdiction on a 3-year cycle and require an error rate below 3% from members of NIBRS. Although this is encouraging, the complexity of NIBRS and differences between jurisdictions or over time may imply important variance in how sexual assault is processed or recorded into NIBRS. Bias generated by differences in quality of data across areas or over time would be minimized by the within-jurisdiction panel analysis conducted in this study. Still, it remains a potential limitation to consider. Notwithstanding these limitations, the study contains important strengths. The study draws on a data system that shares important parallels with UCR. Most important, NIBRS provides the raw data to create the UCR within jurisdictions that report to NIBRS—making it the ideal platform for comparing the old and new definitions. NIBRS provides nearly two decades of data and a substantively large number of incidents to draw on. NIBRS also contains the most geographically diverse data set in the nation for use in assessing the definitional change—an important strength for cross-jurisdictional analyses. Finally, it provides a platform for assessing rates within jurisdictions—to partial out myriad forms of potential bias in computing statistics and comparing estimates between the old and new definitions of rape. These data show that the national picture of sexual assault, in terms of incidence, is likely to be greatly altered when the new UCR data are released. Researchers and policymakers will have to think carefully about the meaning of the new data. The new figures will refer to a combination of new forms of sexual assault, the most common of which involves sodomy and statutory rape. Also, the new cases will reflect a different composition of victims—more than 10% now recognized will likely be male. Academics and policymakers will have to consider what UCR statistics mean in light of these changes and how to use these data in pursuit of effective public policy. The new UCR figures will be summarized across a greater variety of criminal acts, offenders, and victims but will not include variables that allow the statistics to be disaggregated by those characteristics. This may prove challenging to those who intend to use the UCR to evaluate policy or programs that only target a specific type of sexual assault, victim, or offender. The NIBRS data also offer an additional insight into the definitional change of rape/sexual assault. That is, the new definition is still not all inclusive. Indeed, almost half of all sex crimes listed in NIBRS were not recognized by the revised definition. NIBRS shows that 30,000 cases of computer-facilitated sex crime (e.g., child pornography production) did not include any of the new inclusion criteria of sexual assault. Also, slightly more than 31,000 cases of assisting with prostitution (e.g., sexual trafficking) did not include qualifying criteria under the revised definition. Finally, and most significant, there were more than 319,000 cases of child molestation in NIBRS during this period. These forcible fondling cases are actually the single most common form of sexual assault in NIBRS. This figure only refers to cases where the child was


457


fondled but there were no other acts that would allow the incident to be counted in the new definition (e.g., forcing a child to manually masturbate a male offender; an offender manually masturbating a male child). National reporting will continue to exclude these forms of sexual assault under the revised definition. (Although not a focus of this article, the data showed that these forms of sexual assault also tracked the “old definition” of rape over time—they did not exhibit a unique trend in change over time within jurisdictions.) Although the revised definition of rape represents a giant leap forward in national reporting, it is not the final step toward creating a complete picture of sexual assault in the nation. Authors’ Notes The views and opinions in this study do not necessarily reflect those of the United States Marshals Service or the U.S. Department of Justice.

Acknowledgment We would like to thank Paul Detar for comments on analytic strategy.

Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The authors received no financial support for the research, authorship, and/or publication of this article.

Notes 1.

The definition was clearly described in Uniform Crime Reports (UCR) handbooks (see, for example, Federal Bureau of Investigation [FBI], 2004). However, few researchers using the data have noted the definition or implications explicitly in their academic or policy work (for an exception, see Greenfield, 1997). 2. Future iterations of the UCR will contain two fields, one reporting the count of rape under the old definition and one reporting the count under the new definition. 3. National Incident-Based Reporting System (NIBRS) is a publicly available data set. The data as well as codebook can be accessed at http://www.icpsr.umich.edu/icpsrweb/ NACJD/NIBRS/ 4. It is important to note that NIBRS is the source of data used to generate the UCR figures for jurisdictions that participate in NIBRS. (Alternative methods are used to generate these data if a jurisdiction does not participate in NIBRS.) For this reason, the comparison here is particularly strong: We replicate the actual UCR incidence data and compare it with an alternate definition within the same data set as used in practice. 5. There were several additional sexual crimes that could be recorded but which would not be counted either in the old or new rape definition absent one of the forms of sexual assault listed above also being present in the incident. These include computer-facilitated sex


458

Sexual Abuse 27(5)

crime (e.g., child pornography), child molestation (forcible fondling), and assisting with prostitution (e.g., trafficking). 6. The victim-level file does not link each specific offender from the offender-level file to each specific criminal act carried out against each victim in the victim-level file. This may cause error in the event there are multiple offenders of different gender. There were a relatively small number of cases in which the victim-level data indicated a female was forcibly raped, but the offender file indicated both male and female offenders were present. In these scenarios, we assumed that a prior rape event likely occurred. 7. Syntax used to create these categories is available on request. 8. It is important to note that the values that result from this computation are counts per jurisdiction. Rural jurisdictions comprised a relatively greater portion of NIBRS participants during the initial launch of the data system. Over time, a greater number of urban jurisdictions have joined NIBRS. These urban police departments have higher crime counts than rural jurisdictions. Thus, Figure 1 shows a slightly increasing count of rapes per jurisdiction over time (on average across all NIBRS jurisdictions) despite the fact that rape as a whole was declining in the United States over time. The within-jurisdiction analysis described in Table 3 wipes out this jurisdictional contamination of comparisons between rape subtypes.

References Bierie, D., Detar, P., & Craun, S. (2013). Firearm violence directed at police. Crime & Delinquency. Advance online publication. doi:10.1177/0011128713498330 Black, M. C., Basile, K. C., Breiding, M. J., Smith, S. G., Walters, M. L., Merrick, M. T., . . .Stevens, M. R. (2011). The National Intimate Partner and Sexual Violence Survey (NISVS): 2010 Summary report. Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention. Dunn, C., & Zelnock, T. (1999). NIBRS data available for secondary analysis. Journal of Quantitative Criminology, 15, 239-248. Federal Bureau of Investigation. (2004). Uniform Crime Reporting handbook. Retrieved from http://www2.fbi.gov/ucr/handbook/ucrhandbook04.pdf Federal Bureau of Investigation. (2011a). Crime in the United States: By volume and rate per 100,000 residents. Retrieved from http://www.fbi.gov/about-us/cjis/ucr/crime-in-theu.s/2011/crime-in-the-u.s.-2011/tables/table-1 Federal Bureau of Investigation. (2011b). Uniform crime reporting statistics: Their proper use. Retrieved from http://www.fbi.gov/about-us/cjis/ucr/ucr-statistics-their-proper-use Greenfield, L. (1997). Sexual offenses and offenders: An analysis of data on rape and sexual assault (NCJ-163392). Washington, DC: Bureau of Justice Statistics, Office of Justice Programs, U.S. Department of Justice. Howden, L., & Meyer, J. (2011). Age and sex composition: 2010 Census briefs. Washington, DC: U.S. Census Bureau. Retrieved from http://www.census.gov/prod/cen2010/briefs/ c2010br-03.pdf Maxfield, M. (1999). The National Incident-Based Reporting System: Research and policy applications. Journal of Quantitative Criminology, 15, 119-141. Poggio, E., Kennedy, S., Chaiken, J., & Carlson, K. (1985). Blueprint for the future of the Uniform Crime Reporting program (NCJ 98348). Washington, DC: Bureau of Justice Statistics, Office of Justice Programs, U.S. Department of Justice. Retrieved from https:// www.ncjrs.gov/pdffiles1/bjs/98348.pdf


459


Terkel, A. (2012, January 6). Eric Holder expands FBI’s narrow, outdated definition of rape. Huffington Post. Retrieved from http://www.huffingtonpost.com/2012/01/06/eric-holderfbi-rape_n_1189145.html U.S. Department of Justice. (2012, January 6). Attorney General Eric Holder announces revisions to the Uniform Crime Report’s definition of rape. Justice News. Retrieved from http:// www.justice.gov/opa/pr/2012/January/12-ag-018.html


Outcomes Definitions and Statistical Tests in Oncology Studies: A Systematic Review of the Reporting Consistency.

Rape myth acceptance impacts the reporting of rape to the police: a study of incarcerated women.

Motivation Matters: Lessons for REDD+ Participatory Measurement, Reporting and Verification from Three Decades of Child Health Participatory Monitoring in Indonesia.

Statistical reporting in the "Clujul Medical" journal.

Cost-effectiveness analysis: a proposal of new reporting standards in statistical analysis.

Improving the Conduct and Reporting of Statistical Analysis in Psychology.

Definitions of death: brain death and what matters in a person.

Sarcopenia: the new definitions.

MEASUREMENT AND REPORTING OF RADON EXPOSURES.

The definitions of the beginning of old age in history.

Statistical and methodological considerations for reporting RCTs in medical literature.

Comparing Prices for Food and Diet Research: The Metric Matters.

Statistical Reporting Errors and Collaboration on Statistical Analyses in Psychological Science.

Corneal pachymetry: new ways to look at an old measurement.

Variations in measurement of sexual activity based on EHR definitions.

Epidemiology: measurement matters--making musculoskeletal disease count.

Healthcare reform: quality outcomes measurement and reporting.

The reporting of statistical inferences in selected prosthodontic journals.

Guidelines for statistical reporting in the British Journal of Dermatology.

Choice impulsivity: Definitions, measurement issues, and clinical implications.

The prevalence of statistical reporting errors in psychology (1985-2013).

Informed consent and the implications for statutory rape reporting in research with adolescents.

Rapid-response impulsivity: definitions, measurement issues, and clinical implications.

Statistical correction for measurement imprecision.