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Janet L. Lauritsen and Kristin Carbone-Lopez PredictorsExamination of Individual-, Family-, and Community-Level
Gender Differences in Risk Factors for Violent Victimization: An
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Article
Gender Differences in Risk Factors for Violent Victimization: An Examination of Individual-, Family-, and Community-Level Predictors
Janet L. Lauritsen 1
and Kristin Carbone-Lopez 1
Abstract While gender is a well-known correlate of victimization risk, there has been a tendency to study women’s experiences of violence separately from those of men. As a result, relatively little attention has been paid to the question of whether gender moderates well-known risk factors for violent victimiza- tion. In this article, the authors use data from the Area-Identified National Crime Victimization Survey (NCVS) to examine whether the relationships between individual, family, and neighborhood factors and victimization risk are similar in strength and direction for males and females. The findings show that most risk factors for violent victimization are similar across gen- der and crime type. In a few important instances, however, risk factors such as neighborhood disadvantage were found to vary some across gender. The
1 University of Missouri, St. Louis, MO, USA
Corresponding Author:
Janet Lauritsen, One University Boulevard, St. Louis, MO 63121, USA
Email: [email protected]
Journal of Research in Crime and Delinquency
48(4) 538-565 ª The Author(s) 2011
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DOI: 10.1177/0022427810395356 http://jrcd.sagepub.com
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implications of these findings for the assumptions about gender differences underlying various theoretical perspectives are discussed.
Keywords feminist theories, gender, routine activity theory, victimization
Introduction
A significant amount of research over the past three decades has focused on
gender and violence and, in particular, victimization. By now, criminolo-
gists are well aware that males outnumber females in rates of criminal vic-
timization, except for violence by intimate partners and sexual assault (e.g.,
Craven 1997; Felson 2002; Kruttschnitt, McLaughlin, and Petrie 2004;
Lauritsen and Heimer 2008; Rand 2008). Research on gender and victimi-
zation generally takes one of two approaches. On one hand, research on vio-
lence against women tends to be specialized by crime type and largely
focuses on those crimes that women are more likely to experience, such
as intimate partner or sexual violence (but see Dugan and Apel 2003). In
contrast, multivariate analyses of violent victimization more generally
include gender as a control variable, or as one of several covariates thought
to be mediated by various lifestyle factors. However, little research has
examined whether there are similarities and differences in the factors that
predict victimization across gender. Thus, an unresolved issue in victimiza-
tion research is the extent to which gender moderates the effects of estab-
lished correlates of victimization.
An examination of the similarities and differences in the risk factors
for violent victimization for males and females has potentially important
theoretical implications. If male and female victimization risk is largely a
function of similar correlates, then general approaches to the study of vic-
timization are indicated. Depending on the strength of the similarities and
the factors that are examined, such findings would challenge the assumption
that the study of female victimization requires specialized theories of victi-
mization. If, however, the risk factors are found to be substantively differ-
ent, then general theoretical approaches are challenged and the current
intellectual separation of studies of violence against women from broader
research on victimization is warranted.
The purpose of the present research is to fill an important empirical gap
by examining whether the individual-, family-, and neighborhood-level risk
factors for violent victimization differ by gender. We examine these risk
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factors for violence by strangers and nonstrangers using data from the 1995
Area-Identified National Crime Victimization Survey (NCVS) that offer
the ability to assess individual-, family-, and neighborhood-level factors
across a nationally representative sample of adult males and females. The
question we pose is arguably a simple one, but to our knowledge it has not
been previously addressed.
Prior Research on Gender and Violent Victimization
Existing research has been limited to some extent by the manner in which it
has examined gender and violent victimization. Aggregate-level studies on
gender and victimization primarily include descriptive trend estimations or
time-series studies of changes in national victimization rates over time, with
a particular emphasis on male and female homicide rates or multivariate
studies of variations in victimization rates across place (e.g., Batton
2004; Gartner 1990; LaFree and Hunnicutt 2006; Lauritsen and Heimer
2008; Marvell and Moody 1999; Rosenfeld 1997; Smith and Brewer
1992; Smith and Chiricos 2003). Findings from studies of homicide trends
in the United States suggest that male and female homicide victimization
tends to vary similarly over time and that the factors associated with tem-
poral patterns in lethal violence are similar across gender (e.g., Batton
2004; LaFree and Hunnicutt 2006). Research on homicide also suggests that
there has been little trend in the gender gap in victimization. However,
when homicide is limited to incidents committed by intimate partners, the
findings differ: unlike other forms of homicide, these trends have declined
continuously over the past 25 years, with male rates declining at a some-
what faster pace (Dugan, Nagin, and Rosenfeld 2003; Rosenfeld 1997).
Comparisons of trends in nonlethal violence by gender have found stability
in the gender gap for robbery; however, male and female assault victimiza-
tion trends have followed somewhat different patterns over time. More spe-
cifically, the gender gap in aggravated assault and simple assault appears to
have closed since the early 1990s, primarily because male rates declined to
a greater degree than did female rates during this period (Lauritsen and
Heimer 2008). In general, past research comparing gender-specific violence
trends has found both similar and unique patterns for males and females
depending on historical period and type of victimization.
Multivariate research of victimization rates across time and place has
found a great degree of overlap in the macro-structural risk factors for male
and female victimization as factors like age structure, poverty, female labor
force participation, and divorce rates tend to affect both male and female
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rates of victimization in similar ways. For example, Smith and Chiricos
(2003) used police data to examine sex-specific aggravated assault rates for
Florida counties. They found that the same factors (i.e., racial and ethnic
diversity of counties and the proportion of women divorced or separated)
that are related to rates of assault against women also predict rates of assault
against men. This suggests that although male and female rates of victimi-
zation differ considerably in level, there is a great deal of similarity in the
covariates. Moreover, researchers have not uncovered macro-level factors
that mediate or account for the differences in male and female violent vic-
timization rates.
While these macro-level factors may influence both male and female
victimization, their impact is not necessarily invariant across gender. Smith
and Brewer’s (1992) research on the impact of structural disadvantage fac-
tors for male and female homicide victimization suggests that disorganiza-
tion increases the likelihood that both males and females will experience
lethal violence within a community. At the same time, however, neighbor-
hood social disorganization may have slightly ‘‘different ramifications’’ for
the victimization of men because it gives rise to participation in a ‘‘host of
alternative, albeit illegal, economic activities’’ (284). In other words, ele-
ments of social disorganization may lead men to become more involved
in criminal activity that then may heighten their risk of victimization (see
also Miller 2008). Such findings suggest that gender may in fact moderate
the effect of some contextual factors on victimization, including disorgani-
zation and disadvantage.
Research on gender and victimization conducted at the individual-level
of analysis typically relies on survey data to study risk of victimization and
tends to treat gender as a control variable and not conduct comparative
assessments (e.g., Jensen and Brownfield, 1986; Kennedy and Ford 1990;
Lauritsen, Sampson, and Laub 1991; Lauritsen and White 2001; Sampson
and Wooldredge 1987). 1
The results of such inquiries are well known to
criminologists; in general, males are more likely to be the victims of violent
crime. When efforts are made to understand why males experience greater
risk than females, the inclusion of potential mediating variables, such as
lifestyle factors, association with delinquent peers, low parental supervi-
sion, low self-control, and delinquency involvement, are typically found
to account for some but not all of the gender differences in risk levels
(e.g., Jensen and Brownfield 1986; Kennedy and Ford 1990; Lauritsen
et al. 1991; Miethe, Stafford, and Long 1987; Sampson and Wooldredge
1987; Schreck, Osgood, and Stewart 2008). Although this analytical
approach provides important information about why males have higher
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risks than females, it does not address the question of whether various
factors are equally important correlates of risk for males and females or
whether separate theories of female violence might be warranted. Those
questions can be answered most directly by assessing separate models of
victimization for females and males and testing whether the coefficients are
significantly different across the models. 2
Assumptions Underlying Existing Theoretical Frameworks
Generally speaking, theories of violent victimization are unclear about
whether various factors should operate similarly across gender. The most
prominent theories of victimization—lifestyle and routine activities
theories—provide little direct discussion of whether differential effects
by gender or other demographic correlates are expected (Cohen and
Felson 1979; Hindelang, Gottfredson, and Garafalo 1978). According
to Hindelang et al. (1978), individuals’ lifestyles should be related to
their sociodemographic characteristics (including gender), however,
these characteristics are considered proxies for factors that reflect indi-
vidual adaptations to the constraints and expectations imposed by role
obligations and social structure (see also Garofalo 1987; Meier and
Meithe 1993). 3
Importantly, lifestyle theories do not posit that there will
be different predictors of victimization across subgroups. Routine activ-
ities theory, in its original form, does not mention gender differences
explicitly, noting only that persons in ‘‘less active’’ statuses (e.g., keep-
ing house) have lower rates of victimization because their time is largely
confined to family activities within the household (Cohen and Felson
1979:596). 4
Instead, routine activities theory focuses on how changes
in domestic activities and living arrangements, including the movement
of females into the labor force, will increase criminal opportunities as
greater proportions of the population have increased contact with stran-
gers and as homes are more often left unattended.
Other theories that focus on the relationship between neighborhood
conditions and crime are similarly silent about potential gender differ-
ences in victimization risk factors, primarily because victims’ character-
istics are not a key concern of the theory. For example, social
disorganization theories and related hypotheses (Bursik and Grasmick
1993; Sampson, Raudenbush, and Earls 1997; Shaw and McKay 1969) are
general in scope and do not offer discussions about whether neighborhood
factors might affect violence against males or violence against females
542 Journal of Research in Crime and Delinquency 48(4)
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differently. As with lifestyle and routine activities perspectives, this
silence implies that no such differences are expected and that community
correlates should be similar across gender. In general, the possibility
that different dimensions of social disorganization might have different
consequences for men and women has been ‘‘little explored’’ (Smith and
Chiricos 2004:56).
Recent research on violence against women has relied on the insights
from the above perspectives to examine how various forms of women’s vic-
timization, including intimate partner violence, are affected by individual,
family, and community factors (e.g., Benson et al. 2003; Browning 2002;
Lauritsen and Schaum 2004; Miles-Doan 1998). This research has found
that neighborhood conditions are predictive of women’s violence in ways
that are generally consistent with social disorganization perspectives and
lifestyle and routine activities theories. However, these studies focused
exclusively on female victimization and did not compare models across
gender. Instead, insights were drawn by comparing the findings about vio-
lence against women to those derived from more general studies of victimi-
zation that combine the experiences of males and females. Despite these
important new insights, it remains unknown, for example, whether neigh-
borhood disadvantage is an equally important correlate of violence for
males and females.
In sum, the intellectual separation of studies of violence against
women from other research on victimization suggests that women’s vio-
lence and men’s violence share few common causes. In contrast, general
theories of victimization and crime imply that gender does not moderate
the influences of individual lifestyles or routine activities or community
or other characteristics on risk. We examine the competing assumptions
underlying both bodies of research by assessing whether gender moder-
ates the influence of various factors on victimization. The results of this
comparison will provide insight into the strengths and weaknesses of
models that assume either that there are unique causes of women’s vio-
lence or that the causes of male and female victimization are shared. In
addition, because gender is inextricably linked to some forms of victimi-
zation, such as violence in the home, it is important to compare the pre-
dictors of intimate partner violence with stranger and nonstranger
victimization. Such a comparison helps clarify whether potential gender
differences in risk factors for nonstranger violence are largely attributa-
ble to women’s disproportionate level of intimate partner victimization
and assesses the value of general versus crime-specific approaches to the
study of victimization risk.
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Research Questions
To examine whether gender moderates the effect of important correlates of
violence, we assess whether the effects of family, individual, and commu-
nity factors on violent victimization are similar for males and females. The
influences of family factors are examined by considering measures of fam-
ily structure, household income, and length of residence. These factors have
been shown in past research to have effects on the risk of violence (e.g.,
Lauritsen 2001; Maxfield 1987), but the possibility that they may differ
in their direction or importance across gender has not been examined. We
investigate the relative effects of individual characteristics by considering
measures of age, race/ethnicity, and a crude measure of exposure to stran-
gers—time spent away from home in the evenings. We also compare the
effects of neighborhood socioeconomic disadvantage, residential instabil-
ity, and immigrant concentration on the risk of violent victimization. These
specific community factors have been found to have direct influences on
levels of violent victimization and homicide (Sampson et al. 1997), but it
is unknown to what extent they differ across gender. Finally, we examine
whether central city residence has a similar relationship with violence for
males and females. Much multilevel research on victimization has been lim-
ited to data from specific urban areas (e.g., Chicago, Seattle) and thus can-
not assess whether central city residence has similar effects on males and
females. Other multilevel research that has used nationally representative
data has not examined whether gender moderates the relationships between
community factors and violence risk (Lauritsen 2001).
Data and Method
The only data that are nationally representative and permit an analysis of
individual, family, and community influences on male and female violent
victimization risk are the ‘‘Area-Identified’’ NCVS (AI-NCVS). These files
consist of the public-use NCVS files, plus the state, county, and census tract
codes for each household in the sample. Our analyses use data from the
1995 AI-NCVS that were merged with tract-level information from the
1990 decennial census. The AI-NCVS data were made available to
researchers through the Census Regional Data Centers from approximately
1997 to 2003. 5
As most criminologists are aware, the NCVS is a large sample survey
representing the victimization experiences of persons ages 12 and older liv-
ing in households in the United States. Respondents are interviewed once
544 Journal of Research in Crime and Delinquency 48(4)
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every 6 months about their victimization experiences over the previous 6
months. NCVS data are known for their high response rates and broad sam-
ple coverage. In 1995, 95 percent of households and 91 percent of individ-
uals agreed to participate in the survey. 6
Victimization information is
obtained through a series of common language screening questions and for
each potential incident reported during the screening, an additional set of
questions are asked, including details about the nature of the event, the loca-
tion of the incident, and the victim–offender relationship. Our analyses are
based on data from approximately 162,000 interviews with women and men
ages 18 and older. 7
Victimization Measures
Our comparison focuses on three types of violence—violence committed
by strangers and by nonstrangers and, for comparative purposes, intimate
partner violence against women. Prevalence measures were constructed
for each type of victimization and respondents who reported at least one
incident of attempted or completed assault, robbery, rape, or sexual
assault over the 6-month recall period were coded as victims (1 ¼ yes, 0 ¼ no). We limit our analyses to incidents that occur within one mile of the respondent’s home because it is reasonable to assume that neighbor-
hood factors are more strongly related to violence that occurs near the
home than violence that occurs outside of the area. 8
Persons who reported
that they were a victim of a violent event in which the offender was a
friend, acquaintance, or family member were coded as victims of ‘‘non-
stranger violence’’ (1 ¼ yes, 0 ¼ no). Persons who reported that they were victimized by someone with whom they had no prior relationship were
coded as victims of ‘‘stranger violence’’ (1 ¼ yes, 0 ¼ no). A dichotomous measure of ‘‘intimate partner violence’’ victimization—a subset of non-
stranger incidents—was also created for women to capture whether they
were victimized by a spouse, ex-spouse, boyfriend, or ex-boyfriend
(1 ¼ yes, 0 ¼ no).9 Although men and women may be victims of more than one type of victimization during a 6-month period, the vast majority of
victims report experiencing one incident. 10
Family, Individual, and Community Measures
To compare the importance of family factors on men’s and women’s victi-
mization, we constructed measures of family structure, household income,
and length of residence in the current home. Family structure is coded using
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a 32-category household composition variable available in the NCVS.
Adult living arrangements are complex; however, preliminary analyses
showed that these categories could be reduced to three primary family types
without confounding groups at high or low levels for risk of violence. These
three family categories include (a) husband–wife households (with or with-
out children or others in the household), (b) single-headed households with
children (with or without others in the household), and (c) single-headed
households without children (with or without others in the household). 11
Length of residence represents the number of years the family has resided
in the current home. 12
Family income in the NCVS is measured using an
ordinal scale consisting of 14 categories of unequal width. 13
Individual demographic variables include age (coded in years), and
race and ethnicity, which are self-designations based on Census cate-
gories. We used this information to create three mutually exclusive cate-
gories of race/ethnicity: non-Latino black, non-Latino white, and Latinos
(of any ‘‘race’’ category). American Indians, Asians, and those selecting
‘‘other’’ are excluded from the analyses because their sample sizes are too
small to support reliable gender-specific comparisons. 14
Respondents
were also asked to indicate how many evenings they spend away from
home in a typical week, and this measure is used to capture individual dif-
ferences in relative exposure to strangers. 15
Our measures of community characteristics include central city resi-
dence (1 ¼ yes, 0 ¼ no) plus three indices commonly used in contextual analyses of crime: socioeconomic disadvantage, immigrant concentration,
and residential instability (e.g., Lauritsen 2001; Sampson et al. 1997). These
indices were developed for each respondent’s census tract using the results
from principal components factor analyses with orthogonal rotation. Socio-
economic disadvantage is a standardized weighted index combining tract-
level indicators of poverty, households with children that are female
headed, unemployment, public assistance, and percentage black. Immigrant
concentration is a weighted index that combines the percentage of the pop-
ulation that is Hispanic and the percentage foreign-born. The residential
instability index combines the percentage of households in the same house
5 years ago (reverse coded) and the percentage of vacant housing units. 16
Analytic Approach
Hierarchical modeling techniques are one option for assessing individual-,
family-, and community-level effects on victimization risk; however, pre-
liminary analyses of the AI-NCVS indicated that there are too few cases
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within census tracts to do so. Therefore, we rely on the traditional multilevel
regression approach of incorporating community, family, and individual
covariates in individual-level models (e.g., Lauritsen 2001; Miethe and
McDowall 1993). The NCVS uses multistage cluster sampling and the
potential problems posed by correlated errors among cases in sample clus-
ters and between area and individual characteristics are addressed using
survey-weighted logistic regression routines that take into account sample
design and clustering to ensure that standard errors are not underestimated
(StataCorp 1997). Multivariate models of neighborhood victimization by
crime type and gender are estimated, and Z tests are used to assess whether
the coefficients for the factors differ significantly across men and women
(Clogg, Petkova, and Haritou 1995).
Results
Descriptive statistics for the variables used in our analyses are presented in
Table 1. We begin by noting a number of differences across gender in terms
of individual and family characteristics. On average, women tend to be
slightly older and report spending fewer evenings away from home than
men. They are also more likely than men to be living in households as single
parents and are proportionately less likely to be married. Average house-
hold incomes are higher for males than females, to some extent because
men are more likely to be living in two adult households than are women.
Average length of residence in the current home also differs by gender with
women reporting longer periods of time in the same residence. Males and
females do not differ significantly across any of the neighborhood charac-
teristics we examine, such as central city residence or level of neighborhood
disadvantage. This reflects the fact that residential segregation is associated
with race, ethnicity, and economic factors but not gender.
Comparing the prevalence of victimization, we note a number of differ-
ences in risk both within and across gender, some of which are well known
but others that have not received much attention previously. The overall pre-
valence of violent victimization displays the well-known findings that rates of
victimization and stranger violence are significantly higher for males and that
intimate partner violence is significantly higher for females. However, the risk
of other forms of nonstranger violence (e.g., from other family members,
friends, and acquaintances) is also relatively large for women. Past research
has ignored this type of violence because it tends to focus on either stranger
violence or intimate partner violence. These patterns for the 1995 data remain
consistent with more recent victimization data as well: In 2005, for example,
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women reported that their victimizations more often included friends and
acquaintances (39 percent) than intimate partners (18 percent; Catalano 2006).
Past analyses of victimization risk rarely disaggregate events by loca-
tion. When this is done, we find that there are no significant differences
Table 1. Descriptive Statistics for Individual, Family, and Neighborhood Character- istics, Adults (Ages 18þ): Area-Identified NCVS (1995)
Females (N ¼ 84,594)
Males (N ¼ 70,489)
t TestM SE M SE
Individual characteristics Age 45.46 .16 43.52 .15 * Non-Latino/Latina Black .12 .01 .11 .01 Latino/Latina .09 .01 .09 .01 Non-Latino/Latina White .79 .01 .81 .01 Evenings away from home 3.55 .01 3.78 .01 *
Family characteristics Single with children .12 <.01 .05 <.01 * Single no children .29 <.01 .27 <.01 Married .59 .01 .68 .01 * Length residence 11.38 .15 10.63 .15 * Household incomea 8.73 .06 9.44 .06 *
Neighborhood characteristics b
Neighborhood disadvantage �.11 .03 �.17 .02 Immigrant concentration .00 .04 .01 .04 Neighborhood residential instability �.09 .02 �.07 .02 Central city .30 .01 .29 .01
Victimization prevalence (6 month, per 1,000) All incidents 14.38 .51 20.08 .73 *
By strangers 6.00 .34 14.34 .64 * By nonstrangers 8.67 .36 6.38 .35 * By intimate partners 3.57 .24 .54 .09 *
Neighborhood incidents 8.33 .37 8.22 .44 By strangers 2.69 .22 4.94 .34 * By nonstrangers 5.78 .29 3.48 .26 * By intimate partners 2.83 .20 .50 .09 *
Note: NCVS ¼ National Crime Victimization Survey. a. If income is recoded to category midpoints, the mean values of 8.73 and 9.44 correspond to approximately $33,960 and $37,350, respectively. b. The number of cases with neighborhood variables is less (females ¼ 60,967, males ¼ 50,044). *p < .05
548 Journal of Research in Crime and Delinquency 48(4)
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between women and men in rates of neighborhood violence (8.33 vs. 8.22
per 1,000 for females and males, respectively). Compared to men, a greater
proportion of women’s violence occurs within their neighborhood.
Male neighborhood violence is slightly more likely to involve strangers
than nonstrangers. However, for women, neighborhood violence is predo-
minantly committed by nonstrangers, not all of whom are intimate part-
ners. Clearly, intimate partner victimization is a large component of
nonstranger violence for women, but women also report that they experi-
ence violence at the hands of strangers and other nonstrangers at similar
levels in their neighborhood. 17
Table 2 presents the results of our comparative multivariate analysis of
the risk of neighborhood victimization by strangers and nonstrangers for
males and females, along with the Z tests that determine whether the coef-
ficients differ significantly across gender. Overall, we find that the gender
differences in most of the coefficients are not statistically significant. In a
few instances however, we find significant differences. In most of these lat-
ter instances, the difference lies in the magnitude of the relationship and not
in the direction of the effect.
The analyses in Table 2 show that women’s risk of neighborhood stran-
ger violence is significantly related to age, length of residence in the current
home, and whether they reside in a central city area (see column 1). In addi-
tion, single women with children and single women without children are
significantly more likely than married women to be victims of stranger vio-
lence. Race and ethnicity, evenings spent away from home, household
income, and neighborhood measures of disadvantage, immigrant concentra-
tion, and residential instability are not significant predictors of women’s
risk of stranger violence in the full multivariate model. 18
Similarly, men’s
risk of stranger violence also is found to be influenced by age, central city
residence, and family structure. As was the case with women, race and eth-
nicity, evenings away from home, and neighborhood residential instability
have no significant independent effects (see column 3). For males, however,
household income, neighborhood disadvantage, and immigrant concentra-
tion are significantly associated with stranger victimization, and this is not
the case among females. For women, these coefficients are not as large and
fail to attain statistical significance.
The results of the tests of the differences in the stranger violence coeffi-
cients across gender appear in column 5 of Table 2. In one instance, the dif-
ference is statistically significant at p � .05 (two-tailed test), and in two additional instances, the factors differ significantly at slightly higher alpha
levels. Length of residence in the current home has a significantly stronger
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(0 .2
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in a
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e 0 .0
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in co
m e
� 0 .0
3 6
(0 .0
2 2 )
� 0 .0
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(0 .0
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* �
0 .0
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(0 .0
1 7 )
** �
0 .0
4 9
(0 .0
2 4 )
* L e n gt
h o f re
si d e n ce
� 0 .0
5 0
(0 .0
1 4 )
** * �
0 .0
0 9
(0 .0
0 8 )
2 .5
3 0 �
0 .0
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(0 .0
1 4 )
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0 .0
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(0 .0
1 2 )
* 2 .1
1 5
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al ci
ty 0 .3
4 6
(0 .1
5 1 )
* 0 .3
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(0 .1
5 1 )
* 0 .0
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(0 .1
3 8 )
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7 6
(0 .1
8 6 )
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h b o rh
o o d
ch ar
ac te
ri st
ic s
D is
ad va
n ta
ge 0 .1
1 9
(0 .0
8 8 )
0 .3
4 3
(0 .0
8 4 )
** *
1 .8
4 1
0 .0
7 9
(0 .0
6 8 )
0 .0
3 7
(0 .1
3 5 )
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ig ra
n t
co n ce
n tr
at io
n 0 .1
1 6
(0 .0
6 4 )
0 .1
3 7
(0 .0
5 4 )
* �
0 .0
6 5
(0 .0
7 9 )
� 0 .0
9 0
(0 .1
3 2 )
R e si
d e n ti al
in st
ab ili
ty �
0 .0
9 2
(0 .0
8 0 )
0 .0
3 4
(0 .0
5 0 )
� 0 .1
6 7
(0 .0
6 7 )
* 0 .0
4 8
(0 .0
7 5 )
2 .1
3 8
C o n st
an t
� 4 .2
4 5
(0 .2
5 5 )
** * �
3 .7
6 5
(0 .2
9 2 )
** *
� 3 .6
7 8
(0 .2
7 3 )
** * �
4 .5
2 8
(0 .3
7 9 )
** *
N o te
: T
h e
st at
is ti ca
l si
gn if ic
an ce
o f th
e co
e ff ic
ie n t
w it h in
ge n d e r
is in
d ic
at e d
b y
th e
fo llo
w in
g: *p
< .0
5 , **
p <
.0 1 , an
d **
*p <
.0 0 1
(t w
o -t
ai l te
st ).
In cl
u d e d
Z -s
co re
s d e n o te
co e ff ic
ie n ts
th at
d if fe
r si
gn if ic
an tl y
ac ro
ss ge
n d e r
(a t
p <
.1 0 , tw
o -t
ai l te
st ).
550 at University of Texas at San Antonio on November 16, 2013jrc.sagepub.comDownloaded from
effect on women’s risk of stranger violence than on men’s (Z ¼ 2.530, p ¼ .011).19 The effects of being single with children (Z ¼ 1.928, p ¼ .054) and living in a disadvantaged area (Z ¼ 1.841, p ¼ .066) are margin- ally significantly different between men and women. Although these latter
two differences are marginally significant, they are noted here because
they are important variables in studies of victimization. Neighborhood
disadvantage appears to have a somewhat stronger effect on men’s risk
of stranger violence than on women’s, while the effect of being single and
living with children appears to have a somewhat stronger influence on
women’s risk of stranger violence.
In columns 6–9 of Table 2, we present the results of the analysis of non-
stranger violence. Women’s risk of this type of victimization is found to be
significantly related to age, length of residence in the current home, house-
hold income, and whether they are single (with or without children). Race and
ethnicity and evenings spent away from home do not significantly predict
women’s risk of nonstranger violence. The only neighborhood characteristic
found to be related to women’s risk in the final multivariate model of non-
stranger violence was residential instability. 20
Like women, men’s risk of
nonstranger violence also is significantly associated with age, length of resi-
dence in the current home, household income, and whether they are single
(with or without children). In addition, none of the neighborhood measures
significantly predict men’s risk of nonstranger violence in the final model. 21
The results of the tests of the difference in the nonstranger violence coef-
ficients across gender appear in column 10. We find three instances in
which the difference in the coefficients is statistically significant at p � .05. Length of residence in the current home has a greater effect on
women’s risk of nonstranger violence than men’s (Z ¼ 2.115, p ¼ .034) although for both genders, those who have been in their homes longer expe-
rience lower risks of nonstranger violence. Similarly, living in an area of
residential instability has a significantly different effect on risk of women
and men (Z ¼ 2.138, p ¼ .032). Men’s risk of nonstranger violence is unaf- fected by levels of residential turnover; however, women in areas of higher
turnover experience less victimization by nonstrangers. Finally, the mea-
sure of evenings spent away from home has a significantly greater effect
on men’s risk than on women’s risk of nonstranger violence (Z ¼ 2.092, p ¼ .037). Among women, this indicator is not significantly associated with risk of nonstranger violence; however, men who spend more time away
from home are significantly less likely to be victimized by nonstrangers.
Because this measure is typically used as an indicator of exposure to stran-
gers (away from home), this pattern may reflect the fact that women are
Lauritsen and Carbone-Lopez 551
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equally likely to be exposed to nonstrangers when they are away from home
as when they are at home. A more specific indicator that directly captures
the degree of exposure to nonstrangers is needed to better understand the
meaning of this difference.
For comparative purposes, we also model intimate partner violence for
women using the same factors. This allows us to examine the extent to
which this form of victimization shares risk factors with other types of vio-
lence against women and men (see Table 3). These results show that age,
family structure, and length of residence are significantly related to
women’s risk of intimate partner violence. Older, married women who have
resided in their homes for longer periods of time are at significantly lower
risk of experiencing violence at the hands of an intimate partner. The high-
est risks for intimate partner violence are experienced by younger women,
particularly those who are single parents with children, or single without
children, and those who have lived for shorter periods in their current home.
These factors were also found to predict stranger violence as well as non-
stranger violence among women.
Table 3. Logistic Regression Analyses of Risk of Violence for Women by Intimate Partners on Demographic, Family, and Other Individual and Household Characteristics
b SE p Z test
Individual characteristics Age �0.047 0.007 *** 2.386 Non-Latina Black �0.191 0.256 Latina �0.490 0.374 Evenings away home �0.025 0.068
Family characteristics Single with children 1.765 0.225 *** 2.898 Single no children 0.770 0.245 ** Household income �0.025 0.021 Length residence �0.071 0.018 ***
Neighborhood characteristics Disadvantage 0.104 0.091 Immigrant concentration �0.083 0.114 Residential instability �0.043 0.078 Central city �0.138 0.175 2.093
Constant �3.861 0.368 ***
Note: The statistical significance of the coefficient within victimization type is indicated by the following: *p < .05, **p < .01, and ***p < .001 (two-tail test). Included Z-scores denote coeffi- cients that differ significantly from women’s violence by strangers (at p < .10, two-tail test).
552 Journal of Research in Crime and Delinquency 48(4)
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Additionally, we examined whether the predictors of women’s intimate
partner violence differed significantly from the predictors of women’s
risk of stranger violence. 22
These tests showed that three factors had
significantly different effects on stranger compared to intimate partner
violence—age, being single with children, and central city residence. Both
age and being a single parent with children had significantly greater effects
on intimate partner violence than on stranger violence (Z ¼ 2.386, p ¼ .017 and Z ¼ 2.898, p ¼ .004, respectively). Central city residence, on the other hand, had significantly greater effects on women’s risk of stranger violence
(Z ¼ 2.093, p ¼ .037) than on intimate partner violence where the effect was found to be null.
Interestingly, our analyses found that the index of neighborhood disad-
vantage did not have a significant independent effect on any form of
women’s risk of violence. This finding appears to be in contrast with pre-
vious research using the NCVS that has shown some neighborhood condi-
tions to be associated with women’s victimization (Lauritsen and Schaum
2004), as well as research using other data that has found a relationship
between neighborhood disadvantage and intimate partner violence (e.g.,
Benson et al. 2003; Browning 2002; Miles-Doan 1998). Because our study
and the research by Lauritsen and Schaum (2004) rely on the same data, we
further assessed the neighborhood measures and found that the apparent
differences between the findings reflect two issues. First, we note that
the neighborhood disadvantage index is significantly related to women’s
victimization (by all perpetrators) in our models that exclude controls for
individual and family factors (see Appendix A). Second, Lauritsen and
Schaum (2004) used specific neighborhood variables rather than the general
disadvantage index used here. More specifically, they used several of the
components of the disadvantage index and found that the percentage of
households with children that are female-headed was the aspect of neigh-
borhood disadvantage that best predicted female victimization risk.
We also reviewed the main components of the disadvantage index and
similarly found that the measure of female-headed households had signifi-
cant effects on stranger and nonstranger violence against women (see
Appendix B). However, these analyses also showed that the key component
of disadvantage for predicting male victimization appears to be the level of
poverty in the neighborhood. For stranger violence, we found that poverty
rates had a significantly greater effect on men’s risk than on women’s risk
(Z ¼ 1.985, p ¼ .047). For nonstranger violence, we found that the effects of female-headed households on risk were marginally greater for females than
for males (Z ¼ 1.908, p ¼ .056).
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These supplementary analyses suggest that a general indicator of
neighborhood disadvantage, particularly when dominated by indicators
of poverty, appears to be sufficient for capturing some of the important
effects of local context on male victimization risk. However, this
index may be too broad of an indicator to capture the aspects of
communities that are associated with female victimization risk. In terms
of predicting various forms of violence against women, measures of
neighborhood family composition appear to be more salient than levels
of poverty.
Discussion and Conclusion
The purpose of this research was to assess the extent to which family,
individual, and community correlates of male and female violent victimi-
zation are shared versus unique. The main findings from our study indicate
that there are important similarities and differences in risk factors for
males and females, underscoring the value of comparative analyses by
gender. Without direct comparative assessments of male and female risk
using the same data and measures, it cannot be determined whether the
similarities and differences in findings across studies reflect actual simi-
larities or differences in risk factors or the use of alternative data and mea-
sures to assess the phenomenon.
Earlier we noted that the intellectual separation of studies of violence
against women from other research on victimization tends to reinforce the
assumption that women’s violence and men’s violence share few common
causes. The findings from our research, to some degree, challenge that
assumption. More specifically, age, marriage, and household income are
associated with lower risks of stranger, nonstranger, and intimate partner
violent victimization in ways that are comparable in magnitude for both
men and women. A thorough explanation of violence against women must
be able to account for why these factors appear to protect men and women
equally from violence and appear to protect equally, regardless of victim–
offender relationship.
Consider, for example, our finding that the risk of stranger violence is
lower among married men and women than among single men and women,
net of differences found for other individual, family, and community fac-
tors, including a crude measure of exposure to strangers (i.e., evenings away
from home). The reduction in risk associated with marriage may be the
result of an important selection mechanism, but it may also represent a
social arrangement that leads to reductions in men’s and women’s risk of
554 Journal of Research in Crime and Delinquency 48(4)
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stranger violence because it provides capable guardianship to both
husbands and wives. Future research, including feminist analyses, should
examine in greater detail how marriage in its contemporary manifestation
might operate to protect both men and women from stranger violence.
We also noted that many theories of victimization, including more
general perspectives about neighborhood conditions and crime, are
silent about the possibility of gender differences in risk factors and that
this silence reinforces the alternative assumption that the causes of male
and female violence are the same. Our finding that gender moderates
the relationships between stranger violence and three important factors
(length of residence in the home, neighborhood disadvantage, and
single parenthood) challenges this assumption. As discussed above, the
difference in the neighborhood disadvantage finding may be tied to the
fact that the measure is dominated by economic indicators (poverty,
unemployment, and public assistance rates) and these factors may be
more important indicators of men’s exposure to potential stranger offen-
ders. Neighborhood economic conditions also may be related to the beha-
vioral expectations for men in these disadvantaged communities, as noted
by Anderson (1999) and others (e.g., Miller 2008), because of the exis-
tence of norms that emphasize masculine reputation and independence
and a willingness to use violence. Even though some residents may
believe that such norms protect against victimization, recent research
based on African American youth has found that holding such norms
increases one’s risk of violence (Stewart, Schreck, and Simons 2006).
If depressed economic conditions promote such norms that are ultimately
associated with increased risk of victimization, they appear to be operat-
ing disproportionately on males. Why this should be the case is not easily
explained by existing social disorganization, lifestyle, or routine activi-
ties models because they lack explicit attention to gender dynamics (see
e.g., Miller 2008).
In contrast, women’s risk of stranger violence appears to be more sen-
sitive to length of residence, single parenthood, and the family composi-
tion of neighborhoods. Again, the reasons for this are not readily apparent
in existing social disorganization, lifestyle, or routine activities models.
One possibility is that women raising children on their own and those who
are new to their communities may be less likely to be enmeshed within
local support systems. This lack of collective efficacy and social capital,
coupled with the shortage of male guardians within these communities,
may be related to women’s increased risk of victimization in such con-
texts. Yet, feminist analyses have demonstrated that even in
Lauritsen and Carbone-Lopez 555
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neighborhoods where women outnumber men, public community life is
still highly gendered; men participate more in the ‘‘action spaces’’ within
neighborhoods and thus public space may be, largely, male space (Miller
2008).
It is clear that an understanding of these differences in predictors for
women and men must explicitly consider theories of gender, and the possi-
bility that what makes men and women vulnerable or attractive targets, or
affects their levels of guardianship or exposure to motivated offenders, may
differ in important ways. In other words, in developing and modifying
explanations for crime and victimization, our results suggest that gender
should be taken seriously as a ‘‘structural, interactional, and symbolic
source of inequality’’ (Miller 2008). While recent work has begun to do
so, continued focus should be placed on the gendered structural and cultural
components of risk.
Our assessment of whether gender moderates well-known risk factors for
victimization showed that many of the factors associated with violence are
similar for women and men and that several important factors had differing
effects. Such findings both support and challenge assumptions underlying
the intellectual separation of studies of violence against women from more
general research on victimization. Future analyses of these issues will need
to rely on data with a greater wealth of potential explanatory measures than
are available in the NCVS and that also include representative samples of
men and women, to better understand which causal processes may be
gender-neutral and which may be gender-specific. Moreover, further anal-
yses should examine whether the gender differences and similarities
described here hold across racial and ethnic groups. Accounting for both the
similarities and the differences across gender can further our understanding
of violence against women as well as our knowledge about victimization
risk more generally.
556 Journal of Research in Crime and Delinquency 48(4)
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A p
p e n
d ix
A
T a b
le A
1 .
L o gi
st ic
R e gr
e ss
io n
M o d e ls
o f R
is k
o f V
io le
n ce
b y
S tr
an ge
rs an
d N
o n st
ra n ge
rs o n
N e ig
h b o rh
o o d
C h ar
ac te
ri st
ic s
V io
le n ce
b y
S tr
an ge
rs V
io le
n ce
b y
N o n st
ra n ge
rs
F e m
al e s
M al
e s
F e m
al e s
M al
e s
b (S
E )
p b
(S E )
p Z
S co
re b
(S E )
p b
(S E )
p Z
S co
re
N e ig
h b o rh
o o d
d is
ad va
n ta
ge 0 .2
4 2
(0 .0
7 4 )
** *
0 .3
2 5
(0 .0
6 5 )
** *
0 .2
9 6
(0 .0
5 2 )
** *
0 .1
1 4
(0 .1
0 3 )
Im m
ig ra
n t
co n ce
n tr
at io
n 0 .1
0 6
(0 .0
4 9 )
* 0 .1
2 3
(0 .0
4 1 )
** �
0 .0
9 5
(0 .0
6 1 )
� 0 .1
0 9
(0 .1
1 0 )
H o u se
h o ld
re si
d e n ti al
in st
ab ili
ty
0 .0
4 4
(0 .0
7 8 )
0 .1
8 5
(0 .0
5 4 )
** *
� 0 .0
2 8
(0 .0
6 5 )
0 .2
1 3
(0 .0
7 1 )
** 2 .5
0 4
C e n tr
al ci
ty 0 .5
0 4
(0 .1
4 9 )
** *
0 .5
3 4
(0 .1
5 5 )
** *
0 .2
1 7
(0 .1
3 8 )
0 .0
4 0
(0 .1
7 7 )
C o n st
an t
� 5 .5
9 1
(0 .1
0 7 )
** * �
5 .2
8 3
(0 .0
9 1 )
** *
� 5 .2
0 8
(0 .0
8 5 )
** * �
5 .6
8 8
(0 .1
1 0 )
** *
N o te
: T
h e
st at
is ti ca
l si
gn if ic
an ce
o f th
e co
e ff ic
ie n t
w it h in
ge n d e r
is in
d ic
at e d
b y
th e
fo llo
w in
g: *p
< .0
5 , **
p <
.0 1 , an
d **
*p <
.0 0 1
(t w
o -t
ai le
d te
st ).
In cl
u d e d
Z sc
o re
s d e n o te
co e ff ic
ie n ts
th at
d if fe
r si
gn if ic
an tl y
ac ro
ss ge
n d e r
(a t
p <
.1 0 , tw
o -t
ai le
d te
st ).
557 at University of Texas at San Antonio on November 16, 2013jrc.sagepub.comDownloaded from
A p
p e n
d ix
B
T a b
le B
1 .
L o gi
st ic
R e gr
e ss
io n
M o d e ls
o f R
is k
o f V
io le
n ce
b y
S tr
an ge
rs an
d N
o n st
ra n ge
rs o n
C o m
p o n e n ts
o f N
e ig
h b o rh
o o d
D is
ad va
n ta
ge In
d e x
V io
le n ce
b y
S tr
an ge
rs V
io le
n ce
b y
N o n st
ra n ge
rs
F e m
al e s
M al
e s
F e m
al e s
M al
e s
b (S
E )
p b
(S E )
p Z
S co
re b
(S E )
p b
(S E )
p Z
S co
re
P e rc
e n ta
ge fe
m al
e -h
e ad
e d
h o u se
h o ld
s
0 .0
2 7
(0 .0
0 7 )
** *
0 .0
1 9
(0 .0
0 7 )
** *
0 .0
3 1
(0 .0
0 5 )
** *
0 .0
1 3
(0 .0
0 8 )
1 .9
0 8
P e rc
e n ta
ge B la
ck �
0 .0
0 4
(0 .0
0 3 )
� 0 .0
0 2
(0 .0
0 4 )
� 0 .0
0 2
(0 .0
0 3 )
� 0 .0
0 8
(0 .0
0 5 )
P e rc
e n ta
ge in
p o ve
rt y
0 .0
0 2
(0 .0
0 8 )
0 .0
1 8
(0 .0
0 1 )
** �
1 .9
8 5 �
0 .0
1 0
(0 .0
0 6 )
0 .0
1 1
(0 .0
0 8 )
� 2 .1
0 0
C o n st
an t
� 5 .9
2 2
(0 .1
1 6 )
** * �
5 .6
9 4
(0 .1
1 8 )
** *
� 5 .6
4 7
(0 .0
9 9 )
** * �
6 .0
2 1
(0 .1
3 3 )
** *
N o te
: T
h e
st at
is ti ca
l si
gn if ic
an ce
o f
th e
co e ff ic
ie n t
w it h in
ge n d e r
is in
d ic
at e d
b y
th e
fo llo
w in
g: *p
< .0
5 , **
p <
.0 1 , an
d **
*p <
.0 0 1
(t w
o -t
ai le
d te
st ).
In cl
u d e d
Z sc
o re
s d e n o te
co e ff ic
ie n ts
th at
d if fe
r si
gn if ic
an tl y
ac ro
ss ge
n d e r
(a t
p <
.1 0 , tw
o -t
ai le
d te
st ).
558 at University of Texas at San Antonio on November 16, 2013jrc.sagepub.comDownloaded from
Authors’ Note
The data for these analyses were made available to the first author through the
National Consortium for Violence Research (National Science Foundation #SBR
9513040) under the supervision of the Census Bureau and in cooperation with the
Bureau of Justice Statistics.
Acknowledgments
We thank Alfred Blumstein and Brian Wiersema for their assistance with the
Area-Identified NCVS data and several anonymous reviewers for their helpful
suggestions. None of the above agencies or persons bears any responsibility for the
findings presented here.
Declaration of Conflicting Interests
The authors declared no conflicts of interest with respect to the authorship and/or
publication of this article.
Funding
The authors received no financial support for the research and/or authorship of
this article.
Notes
1. There is some recent research that examines women’s experiences of victimiza-
tion more broadly, beyond violence by intimates only. For example, Dugan and
Apel (2003) focus on the role of race and ethnicity in understanding risk factors
and situational characteristics of different types of victimization experiences.
Likely because gender and crime type are linked (i.e., males experience more
stranger violence while females are more likely to be victimized by intimates),
they do not conduct corresponding analyses for men.
2. An alternative method would be to examine interactions between gender and
important correlates of victimization. One such example is Miethe et al.’s
(1987) work with the NCS that examined whether routine activities mediated
the effect of gender and other demographic correlates of victimization (see also
Jensen and Brownfield 1986; Schreck et al. 2008, footnote 5). Aside from not-
ing a significant interaction between gender and ‘‘night activity’’ (i.e., that
being male and more frequently going out at night increases risk) for violent
victimization, they do not focus on the question of whether correlates of victi-
mization operate differently for males and females.
3. Interestingly, Hindelang et al. suggest that convergence in female and male
rates of victimization will occur as ‘‘sex role expectations become increasingly
less differentiated and sex-linked structural barriers become less rigid, with a
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corresponding convergence of the adaptations and lifestyles of males and
females’’ (1978:269). Whether this has occurred, however, is beyond the scope
of this research.
4. In further expositions of their theory, Cohen and colleagues examine how cer-
tain dimensions of social stratification are related to predatory criminal victimi-
zation (Cohen, Kluegel, and Land 1981). They focus on income, race, and age
but not gender.
5. The Bureau of Justice Statistics and the Census Bureau are currently developing
programs and procedures that would allow researchers to regain access to the
area-identification information in the NCVS data.
6. Person-level weights, created by the Census Bureau to account for the fact that
participation is somewhat correlated with the age, race, and gender of respon-
dents, are used in these analyses to adjust for potential nonresponse bias.
7. Because we are interested in assessing the role of family structure and compo-
sition on male and female victimization, it is necessary to exclude juveniles
(12–17) from these analyses. The meaning of family structure measures
depends on one’s age. In contrast to adults, marital status among juveniles
is a constant (i.e., never married) and they rarely choose the type of household
in which they live.
8. According to victims, about 39 percent of stranger incidents, 54 percent of non-
stranger incidents, and 80 percent of intimate partner violent incidents take
place within one mile of the victim’s home.
9. Levels of intimate partner violence against males are too low to be assessed
using multivariate modeling.
10. There is some association between being the victim of stranger violence and
being the victim of nonstranger violence. For example, the overall risk of stran-
ger violence was 6.0 per 1,000 for females but among female victims of non-
stranger violence, the risk of stranger violence was 35.3 per 1,000. Similar
patterns were found among men. However, the similarities from our compara-
tive analyses of stranger and nonstranger violence are not driven by this overlap
of some victims because the total number of persons suffering both experiences
is statistically very low (N ¼ 68) compared to the full sample (N ¼ 162,194). 11. Unfortunately, respondents are not asked about cohabitating partnerships and it
is not possible to distinguish men and women who are living with intimate part-
ners from those who are living with other nonrelatives (e.g., roommates). These
households and other multi-adult households (such as roommates) are treated as
‘‘single-headed households’’ and then subcategorized depending on the pres-
ence of children.
12. Families with less than 1 year in the current home were coded based on the
number of months in residence (e.g., 6 months ¼ 0.5 years).
560 Journal of Research in Crime and Delinquency 48(4)
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13. Information about family income is missing for roughly 12 percent of the
sample. We assessed the sensitivity of our final models to missing data on this
measure and found no significant changes in any of the coefficients.
14. Approximately 4.5 percent of the sample selected American Indian, Asian, or
other as their racial designation in 1995. It is not useful to combine these groups
into a single ‘‘other’’ category because such a strategy would mask large differ-
ences in risk between American Indians and Asians (e.g., Dugan and Apel 2003).
15. We also considered the effect of an individual’s employment status on risk of
stranger and nonstranger violence and on women’s intimate partner violence.
In each instance, we found no significant relationship and therefore do not
include this variable in the models presented here. However, it is worth noting
that employed males and females are significantly more likely to experience
stranger violence outside of their neighborhood. Because our information about
the neighborhood reflects where the respondent lives and not where the event
occurred, we are unable to assess how neighborhood conditions influences the
risk of such events.
16. Roughly 26 percent of the cases in the AI-NCVS are missing tract identification
codes. Analyses comparing individual and household characteristics across
cases that were missing versus nonmissing tract codes showed that nonmissing
cases were composed of slightly more central city and Black respondents. This
is because tract codes were more likely to be missing in newly developed areas
of the country. We found no relationship between gender, victimization, and
missing information on neighborhood characteristics. See Lauritsen (2001:28-
30) for more information about tract codes in the AI-NCVS.
17. The fact that levels of risk of violence in one’s neighborhood are similar for
males and females may help explain some of the gap in levels of fear of crime
between men and women that has yet to be accounted for in the existing liter-
ature. Commonly used measures of fear of crime focus on areas within a mile of
one’s home, yet compare this place-specific fear to levels of victimization risk
that are determined without consideration of the location of the incident.
18. In models including only community-level indicators, neighborhood disadvan-
tage, immigrant concentration, and central city residence were each signifi-
cantly associated with women and men’s risk of stranger violence (see
Appendix A). In addition, these coefficients were found not to differ signifi-
cantly across genders.
19. We further explored the nature of this difference by testing for nonlinear rela-
tionships and found that for both females and males, a somewhat greater rate of
decline in stranger victimization occurred within the first 5 years of residence
than afterward (net of other factors in the models). The more notable pattern
was that the overall rate of decline in risk was greater for females than for males.
Lauritsen and Carbone-Lopez 561
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20. In the models limited to community indicators only, neighborhood
disadvantage was found to be significantly associated with a higher risk of non-
stranger violence among women (see Appendix A).
21. Residential instability was significantly associated with higher risks for non-
stranger violence among men in the model limited to neighborhood factors (see
Appendix A).
22. Nonstranger violence and intimate partner violence were found to be affected
similarly by the factors considered here, however, this is not surprising because
intimate partner violence is a large component of nonstranger violence.
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Bios
Janet L. Lauritsen is a professor in the Department of Criminology and Criminal
Justice at the University of Missouri, St. Louis. She received her PhD in Sociology
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Lauritsen and Carbone-Lopez 565
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Journal of Research in Crime
http://jrc.sagepub.com/content/35/2/123 The online version of this article can be found at:
DOI: 10.1177/0022427898035002001
1998 35: 123Journal of Research in Crime and Delinquency FIFTAL ALARID and R. GREGORY DUNAWAY
VELMER S. BURTON, JR., FRANCIS T. CULLEN, T. DAVID EVANS, LEANNE Gender, Self-Control, and Crime
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1997 34: 275Journal of Research in Crime and Delinquency LISA BROIDY and ROBERT AGNEW
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