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Sexual Abuse: A Journal of
Research and Treatment
1 –24
© The Author(s) 2015
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DOI: 10.1177/1079063215594376
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Article
Deconstructing Incidents
of Female Perpetrated Sex
Crimes: Comparing Female
Sexual Offender Groupings
Kristen M. Budd1, David M. Bierie2,
and Katria Williams3
Abstract
Very little is known about co-offending by female sexual offenders (FSOs),
especially in terms of diverse forms of offender groupings. To address this gap
in the literature, this study uses 21 years (1992-2012) of National Incident-Based
Reporting System data to analyze incidents of sexual offending committed by four
female groupings: solo FSOs (n = 29,238), coed pairs consisting of one male and
one FSO (n = 11,112), all-female groups (n = 2,669), and multiple perpetrator
groups that consist of a combination of three or more FSOs and male sexual
offenders (MSOs; n = 4,268). Using a multinomial logistic regression model, the
data show significant differences in offender, victim, and crime context incident
characteristics. The data also indicate that incidents with solo FSOs and all-female
groups have similar characteristics, coed pairs and multiple perpetrator incidents
have similar characteristics, and these two categorizations are fairly distinct from
one another. Implications of this research are discussed in addition to directions for
future research on female sexual offending.
Keywords
female sexual offending, co-offending, National Incident-Based Reporting System, sex
crimes, multiple perpetrator groups
1Miami University, Oxford, OH, USA
2U.S. Department of Justice, Washington, DC, USA
3U.S. Department of Justice, Alexandria, VA, USA
Corresponding Author:
Kristen M. Budd, Department of Sociology and Gerontology, Miami University, 367D Upham Hall, 100
Bishop Circle, Oxford, OH 45056, USA.
Email: buddkm@MiamiOH.edu
594376SAXXXX10.1177/1079063215594376Budd et al.Sexual Abuse
research-article2015
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2 Sexual Abuse
In the United States, female offenders perpetrate thousands of sexual assaults each
year (Bierie & Davis-Siegel, 2014; Black et al., 2011; Williams & Bierie, 2015). Little
is known, however, about offender, victim, and offense similarities and differences
between females offending alone versus with others in terms of sexual assault inci-
dents. Because there is the need to develop a gender-specific approach toward female
sexual offenders (FSO; Cortoni, 2010; Cortoni & Gannon, 2013; Turchik, Hebenstreit,
& Judson, 2015), research that brings about a better understanding of similarities and
differences between types of female sexual offending can aid in working toward this
goal. A better understanding of females who sexually offend alone versus with others
can also assist law enforcement, policy makers, and, in particular, treatment providers
as they respond to this social problem.
Evidence suggests the majority of FSOs and male sexual offenders (MSOs) offend
alone; however, FSOs are far more likely than MSOs to commit sex crimes with a co-
offender, primarily males (Denov, 2003; Gillespie et al., 2015; Grayston & De Luca,
1999; Lewis & Stanley, 2000; Nathan & Ward, 2002; Vandiver, 2006; Williams & Bierie,
2015). Although several studies have documented this fact, few to date have compared
females who offend alone with those offending with a male. For the few studies that
have specifically compared these two groupings, solo to co-offending FSOs, the sample
sizes have been limited, ranging from 20 to 104 identified co-offenders, which were then
compared with 12 to 123 identified solo FSOs (Gillespie et al., 2015; Muskens, Bogaerts,
van Casteren, & Labrijn, 2011; Vandiver, 2006; Wijkman, Bijleveld, & Hendriks, 2010).
In addition, no empirical research has gone beyond these two FSO groupings, solo
versus co-offending, to compare other groupings such as females who sexually offend
with other females or females who sexually offend in larger groups. As a result, it is
unclear how these more nuanced groupings of FSO incidents vary with respect to
offender features (e.g., demographics), victim characteristics (e.g., age, race, gender,
and relationship to offender), and crime details (e.g., injury and sexual assault behav-
iors). The field has recently recognized this dearth of research as particularly important
to address because females sexually offending in other groupings are more common
than initially realized (Horvath & Woodhams, 2013; Morgan, Brittain, & Welch, 2012;
Williams & Bierie, 2015). Williams and Bierie (2015) found that a sizable minority of
female co-offending involved groups of three or more offenders, and that almost 6% of
the FSO incidents reported to police were committed by all-female groups. This is simi-
lar to Vandiver (2006) who reported all-female groups in approximately 3% of sex
crime incidents resulting in arrest. Because a number of FSO incidents each year derive
from group offending, a better understanding is needed across these various groupings
to continue to advance knowledge about female sexual offending dynamics and to con-
tinue the development of a gender-specific approach toward FSOs.
Group Composition and Offender Behavior
Four studies to date have compared solo and co-offending FSOs and when taken
together showed significant similarities and differences in sexual offending patterns,
but there were also areas where evidence was mixed. One area of mixed evidence was
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Budd et al. 3
in regard to the number of victims involved in the sexual assault. Vandiver (2006) and
Wijkman et al. (2010) found that co-offenders were significantly more likely to have
multiple victims, whereas Muskens et al. (2011) found no significant difference in the
number of victims.
Pertaining to victim gender, solo FSOs were more likely to have male victims
(Muskens et al., 2011; Vandiver, 2006; Wijkman et al., 2010), whereas co-offenders
were more likely to have female victims (Vandiver, 2006; Wijkman et al., 2010).
Vandiver (2006) and Wijkman et al. (2010) also found that co-offenders were more
likely than solo FSOs to victimize both males and females during a sexual assault,
although Muskens et al. (2011) found no statistical difference relating to this victim-
ization pattern. When looking at the victim–offender relationship, co-offenders were
more likely to be related to their victim compared with solo FSOs (Gillespie et al.,
2015; Muskens et al., 2011; Vandiver, 2006; Wijkman et al., 2010).
Vandiver (2006) investigated additional characteristics of FSO sexual assault inci-
dents. Her study is the largest and broadest study to date that investigated and com-
pared 123 solo FSOs incidents with 104 co-offending incidents in the United States. In
her research, incidents that involved solo FSOs and co-offending pairs had similar
propensities to commit rape, but forcible sodomy was significantly more like to be
committed in incidents with co-offending groups (Vandiver, 2006). Pertaining to crime
characteristics, there were no significant differences in terms of the use of a weapon
during the sexual assault incident or in terms of the location of the sexual assault inci-
dent (Vandiver, 2006).
In addition to these studies, typology and theoretical work on female sexual offend-
ing have further elaborated on the co-offending dynamic. Two types of female co-
offenders have been identified in prior research: male-coerced FSOs who participate
due to some type of force exhibited by the MSO and male-accompanied FSOs who
participate willingly with the MSO (Gannon, Rose, & Ward, 2008; Mathews,
Matthews, & Speltz, 1989, 1991; Nathan & Ward, 2002). It is theorized that FSOs who
offend with men do so for various reasons, such as fear of their partner leaving them,
fear of abuse, revenge, anger, or jealously toward the victim, or sexual motivations
such as establishing and maintaining intimacy with their partner (Gannon et al., 2008;
Gannon et al., 2014; Mathews et al., 1991). If females are part of the sexual assault
strategy of male partners, victims and other characteristics of these incidents may
more strongly reflect patterns generally observed among MSOs compared with solo
FSOs (Faller, 1995; Gannon et al., 2014; Mathews, Hunter, & Vuz, 1997; Nathan &
Ward, 2002; Vandiver, 2006). Research so far suggests this is the case—females who
offend with a male partner are more likely to abuse girls, a victim characteristic con-
sistent with the preferences displayed by MSOs (Freeman & Sandler, 2008; Williams
& Bierie, 2015).
Furthermore, female co-offenders may permit access to their children or other
intrafamilial relatives; for example, Gannon et al. (2008) found that about half of the
victims were related to the offender through marriage or blood. In recent work, Gannon
et al. (2014) found that roughly 20% of their participants were biologically related to
their victims. As a result, co-offending pairs may be more likely to have a dependent
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4 Sexual Abuse
child or an intrafamilial victim. This has been suggested by prior work on solo FSOs
and coed pairs (see, for example, Muskens et al., 2011; Vandiver, 2006; Wijkman
et al., 2010), but needs further investigation.
Moving beyond the solo and co-offending comparisons, there is reason to believe
other FSO groupings may demonstrate unique incident characteristics. For instance,
although studies comparing solo and co-offending show similar levels of severity of
abuse and suggest victims and offenders are likely to be intrafamilial (Muskens et al.,
2011; Rudin, Zalewski, & Bodmer-Turner, 1995; Vandiver, 2006; Wijkman et al.,
2010), other research suggests multiple perpetrator sexual assaults, such as those that
involve male offender groups or male-accompanied groups, are characterized by more
injuries, are more likely to involve stranger victimization, and have a greater diversity
of offending (Lambine, 2013; Morgan et al., 2012). Scholars theorize this derives from
the motivation of this type of assault—assault that is often about public displays of
masculinity and aggression among gangs or cliques of offenders; it is about face work
(Collins, 2005; Goffman, 1967). That is, “where women are implicated in the perpe-
trating group, they are invariably acting within the current constructs of masculinity as
a bid for acceptance and power accorded to men and boys” (Kelly, 2013, p. xv). Taken
as a whole, there is evidence that suggests incidents involving multiple perpetrator
groups (MPGs) will victimize strangers at greater rates, have more victim injury, and
commit their sexual assaults in conjunction with other crimes, but there have been few
empirical tests of these assertions to date.
There is also a lack of empirical work on all-female groups that sexually offend
together. Some literature discusses sexual assault by groups of females in jails due to
the special nature of the segregated setting. Much of this discussion has centered on
the use of group sexual assaults, which act as tools of harassment or retaliation for
insulting members of a gang or clique (Alarid, 2000). If sexual assaults are used in
jails as expressions of dominance, there may also be more instances of additional vio-
lence, such as physical beatings. Therefore, all-female sexual assaults compared with
other sexual assault groupings may be more likely to occur in a jail setting and result
in additional non-sex crimes. These also remain untested assertions.
As with other research, there are challenges in building the body of FSO literature.
After a thorough search, we located only four studies that compared FSO groups. One
study was based on data from the United States, whereas the others focused on the
United Kingdom and the Netherlands (see Gillespie et al., 2015; Muskens et al., 2011;
Vandiver, 2006; Wijkman et al., 2010). Not only is there a scarcity of research in this
area but also a vast gap of knowledge on this phenomenon in the United States. The
FSOs in these studies were drawn from arrest, conviction, or court-ordered psychiatric
treatment groups. Samples drawn from these settings are limiting in that only a minor-
ity of FSOs are arrested (Williams & Bierie, 2015). Because of this filtering effect of
the criminal justice system (Langevin et al., 2004; Patrick & Marsh, 2011), these sam-
ples offer a narrow view of this offending population. This limits the statistical tools
available to analyze data or the power to detect differences. The field benefits by
pursuing samples that are larger, more recent, from early points in the criminal justice
system, and that operationalize more nuanced types of FSO groupings.
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Budd et al. 5
The Current Study
The current study compares sexual assault incidents perpetrated by four distinct FSO
groupings: solo FSOs, coed pairs, all-female groups, and MPGs. By focusing on
victim, offender, and crime characteristics, we address the following research
questions:
Research Question 1: Are FSO sexual assault incidents involving a male co-
offender more likely to have female victims in comparison with solo FSO sexual
assault incidents?
Hypothesis 1: Compared with solo FSO sexual assault incidents, sexual assault
incidents involving a female and male co-offending pair will increase the odds that
the victim is a female.
Research Question 2: Are FSO sexual assault incidents involving a male co-
offender more likely to involve relative victims, such as a dependent child or intra-
familial family member, in comparison with solo FSO sexual assault incidents?
Hypothesis 2: Compared with solo FSO sexual assault incidents, sexual assault
incidents involving a female and male co-offending pair will increase the odds that
the victim is a relative.
Research Question 3: Are FSO sexual assault incidents involving MPGs more
likely to result in stranger victimization, result in victim injury, and involve other
non-sex crimes in comparison with other FSO sexual assault incidents?
Hypothesis 3: Compared with other FSO sexual assault incidents, sexual assault
incidents involving MPGs will increase the odds that the victim is a stranger and
the victim is injured, and the sexual assault incident involves other non-sex crimes.
Research Question 4: Are FSO sexual assault incidents involving all-female
groups more likely to occur in jails and involve other non-sex crimes in comparison
with other FSO sexual assault incidents?
Hypothesis 4: Compared with other FSO sexual assault incidents, sexual assault
incidents involving an all-female group will increase the odds that the sexual assault
incident occurs in a jail setting and involves other non-sex crimes.
Method
This research analyzed data from the National Incident-Based Reporting System
(NIBRS) over a 21-year period, from the beginning of 1992 through the end of 2012.1
The NIBRS, managed by the FBI, collects incident-level crime data for 52 crime types
reported to police in participating agencies. As of 2012, a total of 6,115 law enforce-
ment agencies across 37 states submitted data to the NIBRS program, approximately
33% of all agencies that submit data to the Uniform Crime Reports (FBI, 2012). The
NIBRS is the largest publicly available incident-driven data set in the United States
that captures information on sex crimes, including the type of offenses committed in
the incident, characteristics of the offenders and their victims in the incident, and inci-
dent context characteristics such as where the crime took place.
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6 Sexual Abuse
The NIBRS collects incident-level data on six sexual offenses: forcible rape, forc-
ible sodomy, sexual assault with an object, forcible fondling, non-forcible incest, and
non-forcible statutory rape. In addition, it includes two types of sex-associated crimes:
pimping and prostitution. The analysis began with a total of 1,008,483 sex crime inci-
dents reported to police between 1992 and 2012. To narrow the analysis, pimping that
did not have a direct sex crime component was dropped, approximately 8% of inci-
dents. Because FSOs are the focus of this research, we dropped any incidents where
the sex of the offender was unknown, removing approximately 6% of the incidents.
We also dropped any incidents that involved (a) a solo MSO or (b) all male groups of
sexual offenders, approximately 81% of the incidents. The final sample size consisted
of 47,287 sex crime incidents involving FSOs.
Dependent Variable: Incidents by FSO Grouping
To construct a detailed and comparative picture of incidents of female sexual offend-
ing, we created a categorical variable that separated incidents by type of female sexual
offending dynamic: solo FSOs, coed pairs, all-female groups, and MPGs.
A solo FSO referred to incidents with a single female perpetrator. Solo FSOs
accounted for approximately 62% of the sample (n = 29,238). Coed pairs consisted of
one MSO and one FSO who both participated in the sex crime incident, approximately
24% of the sample (n = 11,112). All-female groups consisted of two or more FSOs who
participated in the sex crime incident. All-female groups accounted for approximately
6% of the sample (n = 2,669), and the vast majority of these consisted of a female pair
(93%). MPGs were a mixed gender group consisting of three or more perpetrators that
included at least one female who participated in the sex crime incident. MPGs accounted
for approximately 9% of the incidents in the sample (n = 4,268).
Independent Variables
All the independent variables included in the analysis are analyzed at the incident
level. As described below, this means that although many items were recorded at the
individual level (e.g., gender of each victim; age of each offender), the items used in
the analyses are incident-level summarizations of these characteristics. If there was
only one offender, for example, then these variables were merely the offender-level
attribute. If there were multiple offenders, then these measures represented aggrega-
tions of individual-level attributes.
Offender characteristics. We included the offender demographic variables of age, race,
and substance use. Age was a continuous variable measured in years, ranging from 10
years old to 99 years old.2 It recorded the offender’s age at the time of the incident.
Approximately 10% of the offender age data were missing. If more than one offender
was present, we used the average age of offenders; although, it is important to note that
offender’s ages were within 2 years of one another in 90% of cases with multiple
offenders. Incident-level offender race was coded into three mutually exclusive
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Budd et al. 7
dummy variables: White, Black, and Other (Other including Asian and Native Ameri-
cans). Due to the design of the NIBRS, race categories White and Hispanic were both
pre-populated into the Caucasian category; therefore, we could not disentangle His-
panic from Caucasian to create a separate ethnicity measure. We refer to this combined
Hispanic-or-Caucasian grouping as White and use it as the comparison category. In
addition, due to collinearity, we had to exclude the measure that captured multiple
races of offenders if more than one offender was present during the incident. This
occurred in less than 4% of the incidents. Approximately 5% of the offender race data
were missing. Finally, we also included two dummy variables to signal whether any
offender was under the influence of drugs or alcohol during the incident.
Victim characteristics. Victim characteristics included a count of the number of victims,
victim age, victim race, victim gender, and the relationship of the victim to the offender.
We computed the number of victims in each incident. This item was a continuous
variable that ranged from 1 to 10 victims. Seventy-eight percent of the incidents had
one sexual assault victim. If there was more than one victim in the incident, at least one
of those victims was sexually assaulted. Approximately 18% of the incidents had 2
victims, and less than 5% had 3 or more victims. Victim age was a continuous variable
measured in years and referred to the victim(s) age at the time of the incident. If more
than one victim was present, then the item referred to the average age of victims.
Approximately 1% of victim age data were missing. Incident race was coded into five
mutually exclusive dummy variables at the incident level: White, Black, Other,
Hispanic, and multiple races present. White was the omitted comparison category.
Approximately 4.5% of victim race and ethnicity data were missing. Female victim
(1 = female, 0 = male) was included in the model, using male victims as the omitted
comparison category. In addition, we included mutually exclusive binary items of male,
female, or both genders of victim(s) were present during the sex crime incident.
The NIBRS includes more than 20 victim–offender relationship categories. To pro-
vide breadth and depth about victim–offender relationships within these incidents, we
created six distinct victim–offender relationship categories that were coded as separate
variables: dependent children, intrafamilial or within the family (excluding dependent
children), extrafamilial or outside the family but known to the victim, significant other,
homosexual relationship, and stranger or not known to the victim. Significant other
was created using the categories spouse, common-law spouse, boyfriend/girlfriend,
and ex-spouse. Homosexual relationship is its own distinct NIBRS category and was
not collapsed with “significant other” because some literature suggests homosexual
relationships are an understudied but important feature of some sex crimes (see, for
example, Rothman, Exner, & Baughman, 2011; Vandiver & Kercher, 2004).
Crime characteristics. Features of the criminal incident included the type of sexual
assault behavior, measures of force used against the victim, the severity of the injury
sustained by the victim, the sex crime incident location, and summary information
regarding non-sex crime offenses. The NIBRS contains information on each sexual act
committed against each victim. We included all the NIBRS sexual assault categories
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8 Sexual Abuse
in this research: forcible rape, forcible sodomy, forcible sexual assault with an object,
forcible molestation, non-forcible incest, and non-forcible statutory rape. These were
coded as dummy variables and were not mutually exclusive; effects were allowed to
be additive in models.
The NIBRS contains 15 categories for types of force used against victims. We col-
lapsed the 15 categories into six primary types: guns, knives, blunt objects, personal
weapons, drugs, and other. Guns included firearms, handguns, shotguns, and any other
firearms. Knives included other cutting instruments such as ice picks and screwdrivers.
Blunt objects were items such as clubs and hammers. The offender was considered to use
personal weapons as a source of force if the offender used his or her hands, feet, or teeth,
including using body parts to asphyxiate the victim. Drugs included things such as nar-
cotics, sleeping pills, and poison. The category called other contained types of force that
were atypical weapons in sexual assaults such as motor vehicles, explosives, and fire.
The NIBRS has eight classifications for victim injury. Due to the overall low fre-
quency of injury in the sample, we created three dummy variables to measure victim
injury: none, minor, and major. Minor injuries were defined as minor by the NIBRS.
We created a major injury dummy by combining broken bones, internal injury, loss of
teeth, severe laceration, unconsciousness, and other major injury.
The NIBRS tracks where the offense occurred using 25 specific location categories.
Due to the field’s interest on sexual abuse in the home, jails, and schools, we left these
as distinct location categories in the model. We also left hotel as its own distinct loca-
tion because hotels may signal interesting and unique pathways to offending (e.g.,
offending among college students on spring break, or pimping and prostitution), and
the sample size was large enough to support these analyses. We then collapsed the
remaining 21 locations into the following categories: service locations (e.g., hospital,
church, government buildings such as libraries), business locations (e.g., bar, store,
gas station), and outdoor locations (such as construction areas, woods, alleys, parking
lots). We also included a measure to capture whether the sex crime moved from one
location to another. Finally, we included measures to capture whether the sex crime
incident occurred in conjunction with another crime: computer (e.g., pornography),
pimping, drugs, robbery, and assault.
Analytic Strategy
Using the statistical software package Stata, the incidents comprising the four FSO
grouping were first described across crime, offender, and victim characteristics.
Bivariate comparisons on each item were provided to paint a rich descriptive picture
of the data. Research questions were then addressed using a multinomial logistic
regression model (MNLM) in which group membership in the incident was the nomi-
nal outcome category (see Long & Freese, 2006). With this type of regression model,
the probability of membership in each of the other female sexual offending categories
in the incident was compared with the probability of membership in the reference, or
base, category in the incident. For this analysis, incidents with solo FSOs were the
reference category.
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Budd et al. 9
The MNLM reported coefficients for the effect of each independent variable on
incidents containing each female sexual offending category relative to solo FSO inci-
dents (Long & Freese, 2006). Because multinomial log-odds coefficients lack a metric
in which substantive results can be explained, significant findings were reported as
odds ratios (Pampel, 2000). Odds ratios were interpreted as follows: For each addi-
tional unit change in xk (p ≤ .05), the odds of
m
versus
n
are expected to change by
a factor of exp (,
|)
,βkmn holding all other variables constant.
Due to the complexity of the model and the corresponding statistical output, results
in the tables are shown only for the reference category, incidents involving solo FSOs,
relative to incidents with other groupings, although we do discuss statistically signifi-
cant comparisons between incidents involving all groupings; for example, comparing
incidents involving coed pairs versus incidents involving MPGs. Estimates for com-
paring incidents by each grouping were obtained by running the MNLM and then
running an additional user command written by Long and Freese (2006), which pro-
duces coefficient estimates and odds ratios for all combinations of outcome categories.
These results are available on request.
Because this study involves a large number of analyses, we applied a post hoc
adjustment to account for Type I error. We used the Benjamini and Hochberg (1995)
method to adjust the alpha level to control the false discovery rate (FDR), or the pro-
portion of false positives among all significant results. Because power is seriously
affected by the Bonferroni method, this FDR method is advantageous in that the loss
of power is relatively small, and it balances the need to control Type I error from mul-
tiple testing while maintaining sensitivity to detect effects (Benjamini & Hochberg,
1995; Verhoeven, Simonsen, & McIntyre, 2005). The alpha for this research is based
on the FDR adjustment: p ≤ .03.
All multivariate models used a listwise deletion strategy for item non-response.
After missing data were dropped, the final MNLM analyzed 34,468 (73%) incidents
of sex crimes committed by FSOs (see Table 1 for descriptive statistics and bivariate
comparisons between incidents by FSO grouping).
Results
Research Question 1: FSO incidents involving co-offending pairs and victim
gender
Bivariate analyses indicated that incidents that involved coed pairs (64%) more often
had female victims compared with solo FSOs ( p ≤ .001). Based on the MNLM, com-
pared with solo FSO sexual assault incidents, sexual assault incidents involving a
female and male co-offending pair increased the odds that the victim was a female
compared with male. If the victim was a female, the odds the sexual assault incident
involved a coed pair relative to a solo FSO were 9.08 times greater, holding all vari-
ables constant ( p ≤ .001). Said differently, if the victim was a female, the probability
the sexual assault incident involved a coed pair is .34 greater than if the victim was
male, holding all other variables at their mean. In contrast, if the victim was a female,
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10
Table 1. Descriptive Statistics (N = 47,287) and Bivariate Comparisons of Incidents by FSO Grouping (Reference Category: Solo Female
Offender Incidents).
Solo female
incidents
Coed pair
incidents
All-female
group
incidents
MPG (3+)
incidents
Minimum Maximum M SD M SD M SD M SD
Offender
Age (years) 10 99 25.697 13.205 28.922*** 11.766 24.385*** 12.235 26.750*** 10.093
Race
White-Hispanic 0 1 0.807 0.395 0.758*** 0.428 0.743*** 0.437 0.687*** 0.464
Black 0 1 0.181 0.385 0.141*** 0.348 0.211*** 0.408 0.159*** 0.366
Other 0 1 0.012 0.107 0.007*** 0.081 0.014 0.119 0.005*** 0.072
Substance use
Alcohol 0 1 0.026 0.160 0.066*** 0.249 0.030 0.172 0.088*** 0.283
Drugs 0 1 0.012 0.110 0.042*** 0.200 0.015 0.122 0.067*** 0.249
Victim
Count of victims 1 10 1.141 0.482 1.421*** 0.666 1.776*** 0.951 1.818*** 1.261
Age (years) 0 94 12.289 10.697 14.835*** 10.377 12.758* 10.444 15.729*** 10.041
Race
White 0 1 0.757 0.429 0.787*** 0.410 0.729*** 0.445 0.786*** 0.410
Black 0 1 0.187 0.390 0.165*** 0.371 0.232*** 0.422 0.169** 0.375
Other 0 1 0.010 0.101 0.016*** 0.124 0.011 0.102 0.017*** 0.128
Hispanic 0 1 0.053 0.224 0.072*** 0.259 0.053 0.224 0.066*** 0.248
Multiple 0 1 0.007 0.086 0.039*** 0.194 0.023*** 0.149 0.037*** 0.189
Gender
Female 0 1 0.425 0.494 0.638*** 0.481 0.469*** 0.499 0.706*** 0.456
Male 0 1 0.542 0.498 0.141*** 0.348 0.481*** 0.500 0.120*** 0.325
Multiple 0 1 0.033 0.179 0.221*** 0.415 0.050*** 0.219 0.174*** 0.379
(continued)
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11
Solo female
incidents
Coed pair
incidents
All-female
group
incidents
MPG (3+)
incidents
Minimum Maximum M SD M SD M SD M SD
Relation to offender
Dependent children 0 1 0.133 0.340 0.321*** 0.0467 .0130 0.336 0.260*** 0.438
Intrafamilial 0 1 0.187 0.390 0.157*** 0.366 0.201 0.401 0.197 0.398
Extrafamilial 0 1 0.613 0.487 0.655*** 0.476 0.698*** 0.459 0.781*** 0.414
Significant other 0 1 0.043 0.202 0.128*** 0.334 0.034 0.182 0.091*** 0.288
Homosexual relation 0 1 0.009 0.093 0.002*** 0.039 0.015** 0.120 0.002*** 0.048
Stranger 0 1 0.023 0.150 0.048*** 0.214 0.033** 0.178 0.116*** 0.321
Crime
Sexual assault behavior
Rape 0 1 0.130 0.336 0.334*** 0.472 0.123 0.329 0.399*** 0.490
Sodomy 0 1 0.095 0.293 0.090 0.286 0.114** 0.317 0.107* 0.309
Object penetration 0 1 0.071 0.256 0.059*** 0.236 0.090*** 0.286 0.059* 0.235
Forced molestation 0 1 0.591 0.492 0.406*** 0.491 0.575 0.494 0.349*** 0.477
Incest 0 1 0.024 0.155 0.033*** 0.178 0.020 0.140 0.019 0.138
Statutory 0 1 0.096 0.294 0.107*** 0.309 0.092 0.289 0.104 0.306
Use of force
Blunt 0 1 0.004 0.066 0.005 0.068 0.007* 0.086 0.007* 0.084
Drug 0 1 0.001 0.031 0.003*** 0.058 0.001 0.039 0.006*** 0.079
Gun 0 1 0.001 0.027 0.007*** 0.083 0.003*** 0.055 0.026*** 0.158
Personal weapons 0 1 0.501 0.500 0.524*** 0.499 0.518 0.500 0.530*** 0.499
Knife 0 1 0.003 0.054 0.007*** 0.082 0.006* 0.075 0.015*** 0.120
Other 0 1 0.034 0.181 0.036 0.185 0.039 0.193 0.036 0.187
(continued)
Table 1. (continued)
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12
Solo female
incidents
Coed pair
incidents
All-female
group
incidents
MPG (3+)
incidents
Minimum Maximum M SD M SD M SD M SD
Injury
Minor 0 1 0.088 0.283 0.171*** 0.376 0.101* 0.302 0.201*** 0.401
Major 0 1 0.019 0.137 0.052*** 0.222 0.021 0.142 0.069*** 0.254
Location
Home 0 1 0.775 0.418 0.806*** 0.395 0.749** 0.433 0.770 0.421
Jail 0 1 0.021 0.145 0.002*** 0.040 0.029** 0.168 0.002*** 0.048
School 0 1 0.039 0.193 0.022*** 0.146 0.050** 0.218 0.019*** 0.138
Hotel 0 1 0.010 0.099 0.028*** 0.165 0.011 0.104 0.038*** 0.191
Service 0 1 0.026 0.158 0.011*** 0.105 0.025 0.158 0.015*** 0.122
Business 0 1 0.016 0.127 0.018 0.134 0.018 0.132 0.023** 0.150
Outdoors 0 1 0.038 0.190 0.061*** 0.239 0.044 0.206 0.092*** 0.289
Moves 0 1 0.001 0.036 0.018*** 0.134 0.008*** 0.090 0.036*** 0.187
Additional crimes
Computer involved 0 1 0.002 0.047 0.011*** 0.106 0.006*** 0.077 0.017*** 0.130
Pimping/prostitution 0 1 0.001 0.036 0.006*** 0.076 0.003 0.055 0.012*** 0.111
Drugs 0 1 0.002 0.046 0.021*** 0.142 0.003 0.058 0.040*** 0.195
Robbery 0 1 0.001 0.030 0.005*** 0.073 0.003* 0.051 0.025*** 0.155
Assault 0 1 0.006 0.080 0.076*** 0.265 0.022*** 0.147 0.071*** 0.257
Note. Statistical tests use solo FSO as the reference, or base, category. Intrafamilial victim excludes dependent children. Personal weapons are defined as hands,
feet, teeth, and so on. FSO = female sexual offender; MPG = Multiple perpetrators groups.
*p ≤ .03. **p ≤ .01. ***p ≤ .001.
Table 1. (continued)
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Budd et al. 13
the probability the sexual assault incident involved a solo FSO decreases by .43, hold-
ing all other variables at their mean. Thus, this finding supports the first hypothesis
that incidents involving coed pairs compared with solo FSOs are more likely to have
female victims.
Research Question 2: FSO incidents involving co-offending pairs and victim–
offender relationship
Bivariate analyses indicated that dependent children were more likely to be victimized
in incidents that involved coed pairs (32%) compared with solo FSO incidents
( p ≤ .001). and that incidents involving coed pairs (16%) were more likely to have an
intrafamilial family member victim compared with solo FSO incidents ( p ≤ .001).
Based on the MNLM, compared with solo FSO sexual assault incidents, sexual assault
incidents involving a female and male co-offending pair increased the odds that the
victim is a relative. The odds an incident involved a coed pair relative to a solo FSO
were 101.83 times greater if the victim was a dependent child, holding all other vari-
ables constant ( p ≤ .001). Said differently, if the victim was a dependent child, the
probability the sexual assault incident involved a coed pair is .55 greater than if the
victim was not a dependent child, holding all other variables at their mean. In contrast,
if the victim was a dependent child, the probability the sexual assault incident involved
a solo FSO decreases by .75, holding all other variables at their mean.
Pertaining to intrafamilial victims, the odds an incident involved a coed pair relative
to a solo FSO were 34.98 times greater if the victim was an intrafamilial family mem-
ber, holding all other variables constant ( p ≤ .001). Said differently, if the victim was an
intrafamilial family member, the probability the sexual assault incident involved a coed
pair is .39 greater than if the victim was not an intrafamilial family member, holding all
other variables at their mean. In contrast, if the victim was an intrafamilial family mem-
ber, the probability the sexual assault incident involved a solo FSO decreases by .67,
holding all other variables at their mean. These findings support the second hypothesis
that incidents involving coed pairs compared with solo FSOs are more likely to have
relative victims measured as dependent children and intrafamilial family members.
Research Question 3: MPG incidents, victim–offender relationship, victim injury,
and non-sexual assault criminal behaviors
Based on the bivariate analyses, incidents with MPGs (12%) were more likely to have
stranger victims compared with solo FSO incidents ( p ≤ .001). Sexual assault inci-
dents involving MPGs were more likely to involve injury, minor (20%) and major
(7%), compared with solo FSO incidents ( p ≤ .001). MPG incidents were also more
likely to be involved in computer crimes (2%), pimping/prostitution (1%), drugs (4%),
robbery (3%), and assault (7%) compared with solo FSO incidents ( p ≤ .001).
Based on the MNLM, compared with other FSO sexual assault incidents, sexual
assault incidents involving MPGs increased the odds that the victim is a stranger, the
victim is injured, and the sexual assault incident involved other non-sex crimes. Stranger
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14 Sexual Abuse
victimizations were most likely in incidents that involved MPGs relative to all other
groupings—the odds increased 187.61 versus solo FSO incidents, 8.37 versus all-
female group incidents, and 2.89 versus coed pair incidents, holding all other variables
constant ( p ≤ .001). Said differently, if the victim was a stranger, the probability the
sexual assault incident involved an MPG is .35 greater than if the victim was not a
stranger, holding all other variables at their mean. In contrast, if the victim was a
stranger, the probability the sexual assault incident involved a solo FSO decreases by
.61, holding all other variables at their mean.
As hypothesized, injury was more likely to result from an MPG sexual assault inci-
dent. The odds the incident involved an MPG relative to a solo FSO, a coed pair, and
an all-female group were 2.28, 1.34, and 2.27 times greater if the sexual assault
resulted in a major injury, holding all other variables constant ( p ≤ .001). Said differ-
ently, if the victim sustained a major injury, the probability the sexual assault incident
involved an MPG is .06 greater than if the victim did not sustain a major injury, hold-
ing all other variables at their mean. In contrast, if the victim sustained a major injury,
the probability the sexual assault incident involved a solo FSO decreases by .13, hold-
ing all other variables at their mean. Minor injury was also more likely to result from
an MPG sexual assault incident. The odds the incident involved an MPG relative to a
solo FSO, a coed pair, and an all-female group were 1.83, 1.20, and 1.60 times greater
if the sexual assault resulted in a minor injury, holding all other variables constant
( p ≤ .001).
As hypothesized, MPG sexual assault incidents were more likely to involve non-
sex crimes. Compared with solo FSO and all-female group incidents, MPG incidents
were more likely to involve the following crime types: computer-involved crimes
(e.g., pornography), prostitution/pimping, drugs, and assault (e.g., aggravated assault,
simple assault, and intimidation). For example, the odds an incident involved an MPG
relative to a solo FSO or an all-female group were 1.83 and 1.55 times greater if the
sex crime incident also involved an assault, holding all other variables constant
( p ≤ .001, p ≤ .03). In addition, MPG incidents were more likely to involve robbery
compared with incidents involving all other groupings. If the sexual assault incident
also involved a robbery, the odds the incident involved an MPG relative to a solo FSO,
a coed pair, or an all-female group were 5.59, 2.41, and 3.09 times greater, holding all
other variables constant ( p ≤ .001, p ≤ .001, p ≤ .03). These findings support the third
hypothesis that incidents involving MPGs compared with other FSO groupings are
more likely to have stranger victims, greater victim injury, and the co-concurrence of
non-sex crimes in conjunction with the sexual assault incident.
Research Question 4: All-female group incidents, sexual assault location, and
non-sexual assault criminal behaviors
Bivariate analyses indicated that incidents that involved all-female groups (3%) were
more likely to occur in a jail compared with solo FSO incidents (p ≤ .01). When investi-
gating non-sex crimes that occurred in conjunction with sexual assault incidents, inci-
dents that involved all-female group sexual assault were more likely to involve computer
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Budd et al. 15
crimes (e.g., pornography; 1%, p ≤ .001), robbery (0.3%, p ≤ .03), and assault
(2%, p ≤ .001) compared with solo FSO incidents.
The final hypothesis was partially supported: Compared with other FSO sexual
assault incidents, sexual assault incidents involving an all-female group did increase
the odds that the sexual assault incident occurred in a jail setting. If the sexual assault
occurred in a jail, the odds the incident involved an all-female group relative to a coed
pair or MPG were 11.22 and 9.98 times greater, holding all other variables constant
( p ≤ .001). Said differently, if the incident occurred in a jail, the probability the sexual
assault incident involved an all-female group is .05 greater than if the sexual assault
incident did not happen in a jail, holding all other variables at their mean. However,
there was no significant difference when comparing solo FSO and all-female group
sexual assault incidents that occurred in jails. When compared with other sexual
assault incidents involving other groupings, there were also no significant findings
that incidents involving all-female groups were more likely to involve additional non-
sex crimes compared with incidents with other groupings.
Discussion
This study drew on the nation’s largest publicly available incident-level data set on
crimes reported to police, the NIBRS, to compare sexual assault incidents involving
four groupings of FSOs in relation to offender, victim, and crime characteristics.
Overall, a key finding was that incidents that involved solo FSOs or all-female groups
mirrored each other in many offender, victim, and crime characteristics; a similar rela-
tionship was observed in incidents that consisted of females acting in consort with one
or more MSOs. In addition to adding to the body of knowledge on female sexual
offending, this study also contributes to the continuing effort to formulate a gender-
specific approach to FSOs by better understanding female sexual offending behavior
in terms of their offender grouping (Cortoni, 2010; Cortoni & Gannon, 2013).
With regard to victim characteristics, in accordance with prior studies (Vandiver,
2006; Wijkman et al., 2010), incidents involving coed pairs were more likely than
incidents involving solo FSOs to victimize females. This lends support to the theoreti-
cal premise of the co-offending dynamic. If females participate with males, whether
coerced or willingly, it makes sense that coed pair victims are more likely to be female
than male. This victim selection exhibits MSO offending preferences (Freeman &
Sandler, 2008; Williams & Bierie, 2015). What is unknown though is how the coed
pair group dynamic unfolds in regard to victim selection and offender participation
level in terms of sexual assaults (Porter, 2013). Further research is needed to under-
stand these dynamics and better inform a gender-specific approach that can be used by
community actors, such as treatment providers.
With regard to victim–offender relationship, there is evidence that co-offenders are
more likely to sexually assault those who are known to them (Muskens et al., 2011;
Vandiver, 2006; Wijkman et al., 2010). In line with this evidence, this research found
that incidents involving coed pairs were more likely to involve dependent children
compared with incidents that involve solo FSOs. This significance also held for
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16 Sexual Abuse
incidents involving intrafamilial family members. Because of this dynamic, especially
with dependent children, we were interested to see whether non-forcible incest played
a significant role in co-offending incidents versus other type of offending incidents; it
did not once controls were added (Table 2). In addition, non-forcible incest offending
only comprised 3% or fewer of the incidents in any group.
Previous studies have reported that MPG sexual assaults take on distinguishing
characteristics compared with other sexual assault offender groupings: stranger victim-
izations, more victim injury (Lambine, 2013; Morgan et al., 2012), and a greater diver-
sity of offending that includes non-sexual crimes (Lambine, 2013). The data analysis
here offered support to prior research findings. Morgan et al. (2012) found that multiple
perpetrators were more likely to be strangers to their victims than single perpetrators.
This research supports and extends that finding as MPG incidents were more likely to
involve stranger victims compared with solo FSO incidents, but in general, we found
that group size mattered. If the sexual assault incident involved a coed pair, an all-
female group, or an MPG compared with a solo FSO, the odds of stranger victimization
significantly increased. Future research should further investigate the dynamics between
group size and victim selection. The MPG incidents also involved more injury than
other groupings and more diverse offending beyond sexual crimes, especially robbery.
However, these features were rare even if they were significantly greater among MPG
incidents. For example, only approximately 3% of the sexual assault incidents involved
a major victim injury regardless of the offender grouping. Also, it is important to note
that some additional crimes, such as assault, were more likely among coed pair inci-
dents relative to incidents that involved solo FSOs, all-female groups, or MPGs.
Last, our investigation of sexual assault incidents involving all-female groups sup-
ports that these types of incidents are more likely to occur in a jail setting compared
with incidents involving one or more male co-offenders. This diverges from Vandiver
(2006) who found no significant differences in terms of the sexual assault location. This
may be, in part, due to Vandiver’s (2006) coding of location as residence and non-
residence, whereas our research used the specific location of jail and did not collapse it
with other location categories. Pertaining to sexual assaults in jails, it is difficult to
know whether this is a function of inmates assaulting other inmates or a feature of
female prison guards exploiting prisoners. Importantly, a sizable portion (42%) of solo
female and all-female incidents occurring within jails or prisons involved females
assaulting male victims. Presumably, these represent sexual abuse by female prison staff
against male inmates, as few jails in the United States are coed. The remaining portion
of cases could be comprised of inmate–inmate violence, inmates assaulting staff, or staff
assaulting one another. Offending in jails represented a substantively small portion of the
assaults in this data set (less than 3%). Still, understanding these offending patterns may
represent an important and underexplored context to female sexual offending.
Limitations
Although this research adds to the body of knowledge on female sexual offending, this
research is not without limitations. The NIBRS is limited in that it only collects inci-
dent-level data, and as a result, this analysis does not model the offenders themselves
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17
Table 2. Multinomial Logistic Regression Predicting the Likelihood of an Incident Involving a Coed Pair, All-Female Group, or MPG
(Reference Category: Solo Female Offender Incidents; n = 34,468).
Coed pair incidents All-female group incidents MPG (3+) incidents
Coefficient (SD) OR Coefficient (SD) OR Coefficient (SD) OR
Offender
Age (years) 0.014 (0.001) 1.01*** −0.014 (0.003) 0.99*** −0.004 (0.002) 0.99*
Race
Black −0.419 (0.067) 0.66*** −0.169 (0.089) −0.434 (0.092) 0.65***
Other −0.960 (0.213) 0.38*** 0.186 (0.251) −1.183 (0.325) 0.31***
Substance use
Alcohol 0.510 (0.076) 1.67*** 0.194 (0.138) 0.780 (0.091) 2.18***
Drugs 0.519 (0.113) 1.68*** −0.076 (0.221) 0.744 (0.131) 2.11***
Victim
Count of victims 0.156 (0.038) 1.17*** 1.352 (0.037) 3.87*** 1.089 (0.037) 2.97***
Age (years) 0.006 (0.002) 1.01*** 0.008 (0.003) 1.01** 0.010 (0.003) 1.01***
Race
Black 0.152 (0.066) 1.16* 0.441 (0.087) 1.55*** 0.074 (0.091)
Other 0.445 (0.173) 1.55** 0.019 (0.280) 0.516 (0.223) 1.68*
Hispanic 0.154 (0.071) 1.17* 0.002 (0.117) 0.016 (0.099)
Multiple 0.217 (0.133) −0.940 (0.217) 0.39*** −0.061 (0.165)
Gender
Female 2.210 (0.045) 9.08*** 0.261 (0.056) 1.29*** 2.442 (0.066) 11.49***
Multiple 3.162 (0.079) 23.62*** −1.468 (0.136) 0.23*** 2.044 (0.102) 7.72***
Relation to offender
Dependent children 4.623 (0.097) 101.83*** 2.780 (0.124) 16.28*** 4.868 (0.107) 130.09***
Intrafamilial 3.555 (0.097) 34.98*** 2.972 (0.120) 19.55*** 4.217 (0.106) 67.80***
Extrafamilial 4.095 (0.096) 60.05*** 3.210 (0.119) 24.78*** 4.952 (0.105) 141.41***
Significant other 4.117 (0.126) 61.37*** 2.801 (0.182) 16.45*** 4.08 (0.142) 59.32***
(continued)
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Coed pair incidents All-female group incidents MPG (3+) incidents
Coefficient (SD) OR Coefficient (SD) OR Coefficient (SD) OR
Homosexual relation 0.986 (0.352) 2.68** 3.194 (0.229) 24.39*** 1.764 (0.434) 5.83***
Stranger 4.174 (0.132) 64.98*** 3.109 (0.184) 22.41*** 5.234 (0.142) 187.61***
Crime
Sexual assault behavior
Rape 1.989 (0.134) 7.31*** −0.059 (0.228) 1.743 (0.146) 6.64***
Sodomy 0.351 (0.132) 1.42** −0.036 (0.223) 0.170 (0.146)
Object penetration −0.084 (0.140) 0.086 (0.232) −0.390 (0.159)
Forced molestation −0.038 (0.136) −0.239 (0.227) −0.503 (0.148) 0.66**
Incest 0.485 (0.433) 0.434 (0.692) −0.379 (0.604)
Statutory rape 0.794 (0.319) 2.21**
Use of force
Blunt −0.221 (0.230) 0.487 (0.282) 0.203 (0.274)
Drug 0.073 (0.340) 0.234 (0.669) 0.802 (0.362) 2.23*
Gun 1.084 (0.319) 2.96** 1.017 (0.485) 1.944 (0.324) 6.99***
Personal weapons 0.101 (0.035) 1.11** 0.0002 (0.053) 0.146 (0.048) 1.16**
Knife −0.062 (0.233) 0.461 (0.324) 0.351 (0.257)
Other 0.177 (0.087) 1.19* 0.032 (0.135) 0.287 (0.117) 1.33**
Injury
Minor 0.418 (0.050) 1.52*** 0.136 (0.087) 0.604 (0.064) 1.83***
Major 0.525 (0.093) 1.69*** 0.007 (0.180) 0.825 (0.111) 2.28***
Location
Home 0.098 (0.066) −0.066 (0.098) −0.026 (0.089)
Table 2. (continued)
(continued)
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Coed pair incidents All-female group incidents MPG (3+) incidents
Coefficient (SD) OR Coefficient (SD) OR Coefficient (SD) OR
Jail −2.164 (0.263) 0.11*** 0.253 (0.166) −2.047 (0.387) 0.13***
School −0.178 (0.117) 0.076 (0.152) −0.320 (0.169)
Hotel 0.707 (0.137) 2.03*** 0.091 (0.252) 0.783 (0.166) 2.19***
Service −1.002 (0.152) 0.38*** −0.021 (0.183) −0.903 (0.202) 0.41***
Business −0.013 (0.134) 0.018 (0.206) −0.250 (0.183)
Outdoors 0.453 (0.098) 1.57*** 0.123 (0.151) 0.543 (0.123) 1.72***
Moves 0.945 (0.239) 2.57*** 0.934 (0.325) 2.45** 1.151 (0.255) 3.16***
Additional crimes
Computer involved 1.220 (0.231) 3.39*** −0.854 (0.374) 0.43* 0.639 (0.259) 1.89**
Pimping/prostitution 1.104 (0.329) 3.02** −0.446 (0.554) 0.815 (0.353) 2.26*
Drugs 1.460 (0.222) 4.30*** −0.894 (0.447) 0.41* 0.819 (0.239) 2.27**
Robbery 0.844 (0.294) 2.33** 0.593 (0.466) 1.722 (0.295) 5.59***
Assault 1.074 (0.114) 2.93*** 0.167 (0.190) 0.602 (0.135) 1.83***
Note. For clarity, odds ratios are only shown if statistically significant. Intrafamilial victim excludes dependent children. Personal weapons are defined as hands,
feet, teeth, and so on. MPG = Multiple perpetrators groups; OR = odds ratio.
*p ≤ .03. **p ≤ .01. ***p ≤ .001.
Table 2. (continued)
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20 Sexual Abuse
but the incidents and their characteristics. Therefore, future research should examine
sexual assault behavior as individual-level analyses. In addition, there is no way to
disentangle whether an FSO was involved with more than one incident; for example,
one FSO could have been involved in a coed incident and an MPG incident. NIBRS
does not collect information on certain variables that may be of interest, such as the
criminal history of victims or offenders. In regard to crime types, the NIBRS collects
data on six sexual offenses involving offender–victim contact, but it does not include
non-contact crimes such as indecent exposure, voyeurism, and child pornography.
The NIBRS data over-represent small and medium sized police departments. Also,
they do not include data from some areas of the nation with disproportionately high
crime (e.g., New York City). This may limit the generalizability of data only to agencies
that report to the NIBRS. Because this is a criminal justice sample, there is the issue of
underreporting. Underreporting of female perpetrated sex crimes may be due to cultural
factors such as lack of awareness about female sexual offending, gender role stereo-
types, and/or traditional gender scripts, the perception sex offenses committed by
women are less serious than those committed by a man, and/or a reluctance to report
due to the gender of the perpetrator (Becker, Hall, & Stinson 2001; Bunting, 2007;
Center for Sex Offender Management [CSOM], 2007; Denov, 2003, 2004; Vandiver &
Kercher, 2004). Therefore, although this research uses the largest sample of FSO sexual
assault incidents to date, it does not capture all sex crimes perpetrated by females and
should not be construed as being inclusive of offending not reported to police.
The data system also required data entry by more than 6,000 different police agen-
cies, a process that may generate important differences over time or place with respect
to the meaning of a particular item. We might wonder, for example, whether police
agencies differed with respect to the working definition of incest in these data, given
that approximately 20% of cases involved the sexual assault of a dependent child, but
only 3% were deemed incest. The difference may be that “incest” here excluded inci-
dence with force, whereas the assault of a dependent child could include force.
However, a portion of the difference could also be associated with differing defini-
tions. The FBI reports that the NIBRS system is audited, and agencies are required to
maintain an error rate below 3% to retain accreditation. Regardless, it is likely that
interrater reliability remains an untested, yet plausible, challenge in these data. Perhaps
the most striking limitation, however, is the lack of specific contextual or qualitative
data. Unlike research drawing on case files or interviews, we were unable to seek out
more nuance in patterns as they emerged. For example, we cannot obtain more detail
on which offender (if any) took the lead during the crimes, how the situations trans-
pired, and what occurred over time between offenders engaged in these crimes.
Likewise, the data system provided no additional details on victims or offender histo-
ries that may have further illuminated how these crimes emerged.
Conclusion
As the body of research addressing FSOs continues to grow, there is still a lack of
research discerning FSO offending behavior when comparing different types of FSO
groupings. This research is one step toward filling that gap because we had the ability
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Budd et al. 21
to analyze more than 30,000 sexual assault incidents committed by FSOs that span
across a large portion of the nation, and which had accumulated more than 2 decades
of time. Although this analysis used a more precise grouping of incidents of female
sexual offending than in prior work, there is still much work to be done to better under-
stand the sexual offending patterns of females. Future research should continue to
investigate and compare FSO group behavior within subgroups of FSO offenders such
as juvenile FSOs, adult FSOs, MPG FSO offenders, and all-female groups. By con-
tinuing to address FSO offending dynamics, we can better understand these variations
in offending as they have implications for offenders, victims, and communities.
Acknowledgment
We would like to thank Dr. Michael Bourke, Paul J. Detar, and Dr. Sarah Mustillo for sugges-
tions throughout the process of conducting this research. We would also like to thank Dr. Franca
Cortoni, the action editor, and the anonymous reviewers for their invaluable feedback on the
manuscript throughout the revision process.
Authors’ Note
The views and opinions of this research do not necessarily represent those of the U.S. Department
of Justice or any component therein.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of
this article.
Notes
1. This research received institutional review board (IRB) approval with exempt status.
2. The analysis was rerun with adult female sexual offenders (FSOs) only. The results were
generally similar to the overall patterns reported here; therefore, all incidents that involved
an FSO, regardless of offender age, remained in the analysis.
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