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Janet L. Lauritsen and Kristin Carbone-Lopez PredictorsExamination of Individual-, Family-, and Community-Level

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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

546 Journal of Research in Crime and Delinquency 48(4)

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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

Lauritsen and Carbone-Lopez 549

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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

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rs an

d N

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ra n ge

rs o n

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h b o rh

o o d

C h ar

ac te

ri st

ic s

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le n ce

b y

S tr

an ge

rs V

io le

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b y

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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

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re b

(S E )

p b

(S E )

p Z

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h b o rh

o o d

d is

ad va

n ta

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(0 .0

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: T

h e

st at

is ti ca

l si

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an ce

o f th

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w it h in

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is in

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g: *p

< .0

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u d e d

Z sc

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o -t

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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

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e ig

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n ta

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d e x

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b y

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b (S

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(S E )

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(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

from the University of Illinois in 1989. Her current research focuses on long-term

changes in violent victimization by gender, age, race, and ethnicity.

Kristin Carbone-Lopez is an assistant professor in the Department of Criminology

and Criminal Justice at the University of Missouri, St. Louis. She received her PhD

in Sociology from the University of Minnesota in 2006. Her research focuses on

gender, offending, and victimization across the life course.

Lauritsen and Carbone-Lopez 565

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http://jrc.sagepub.com/ and Delinquency

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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http://jrc.sagepub.com/content/34/3/275 The online version of this article can be found at:

DOI: 10.1177/0022427897034003001

1997 34: 275Journal of Research in Crime and Delinquency LISA BROIDY and ROBERT AGNEW

Gender and Crime: A General Strain Theory Perspective

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