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Fear of crime and criminal victimization: Gender-based contrasts
Joseph A. Schafer
a,
⁎, Beth M. Huebner
b
, Timothy S. Bynum
c
a
Center for the Study of Crime, Southern Illinois University - Carbondale, Mailcode 4504, Carbondale, IL 62901-4504, United States
b
Department of Criminology and Criminal Justice, University of Missouri - St. Louis, 324 Lucas Hall, 8001 Natural Bridge Road,
St. Louis, MO, 63121-4499, United States
c
School of Criminal Justice, Michigan State University, 560 Baker Hall, East Lansing, MI 48824-1118, United States
Abstract
Extant research on the fear of crime and criminal victimization had generally found that women express greater levels of fear
than men. Using survey data, this study contrasted perceptions of safety and the fear of personal and property victimization among
male and female respondents. Specifically considered was the relationship between demographic characteristics, fear facilitators,
fear inhibitors, neighborhood context, and crime-related fear. Results indicated some gender differences in the influence
explanatory variables had on fear, although not all achieved statistical significance. For both gender groups, respondents'
perceptions of their neighborhood as orderly and satisfactory had the largest effect on perceptions. Gender-based differences in the
outcome of the analyses further supported that males and females experienced fear based upon different factors.
© 2006 Elsevier Ltd. All rights reserved.
Introduction
Studies conducted in the past four decades probed fear
of crime and perceived risk of criminal victimization
among the American public. It had been widely
recognized that Americans had a substantial fear of
crime and that crime-related fear was greater among
women (Covington & Taylor, 1991; Garofalo, 1981;
McGarrell, Giacomazzi, & Thurman, 1997; Parker,
Onyekwuluje, & Komanduri, 1995), although variation
had been noted (Hale, 1996; Haynie, 1998; Thompson,
Bankston, & St. Pierre, 1992). Initial studies viewed
crime-related fear as a product of demographic character-
istics and individual experiences with crime and victim-
ization. More recently, scholars had attempted to broaden
insight into crime-related fear to understand why women
report greater levels of fear than men. Researchers had
most often argued that fear among women was over-
shadowed by their fear of sexual victimization, even when
respondents were prompted to consider nonpersonal and
nonviolent offenses (Ferraro, 1995; Fisher & Sloan, 2003;
Pain, 2001; Valentine, 1989; Warr, 1984).
Although the hypothesized shadow effect of fear of
sexual assault provided important insight into the fear
among women, less was known about what differenti-
ated fear between the genders. This study sought to
understand the factors driving variation in the level of
fear expressed by both genders. Given the possibility
that men and women experienced fear for different
reasons, it was hypothesized that explanatory models
would exhibit gender-specific variation; therefore,
separate models were estimated for male and female
respondents. Hierarchical linear modeling (HLM) was
used to separate the influence of individual predictors
from contextual neighborhood constructs when model-
ing variation in individual fear and perceptions of safety.
Journal of Criminal Justice 34 (2006) 285 –301
⁎Corresponding author. Tel.: +1 618 453 6376; fax: +1 618 453
6377.
E-mail address: jschafer@siu.edu (J.A. Schafer).
0047-2352/$ - see front matter © 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jcrimjus.2006.03.003
The utility of these predictors was explored using three
measures of fear including perceptions of safety,
fear of personal victimization, and fear of property
victimization.
Exploring fear among women
Research had consistently established gender-based
differences in reported levels of crime-related fear.
Higher rates of fear expressed by women are thought
to reflect a broader concern of sexual harassment and
assault (Ferraro, 1995; Pain, 2001; Valentine, 1989),
which some frame as an extension of gender roles, social
control, and societal oppression of women (Garofalo,
1981; Goodey, 1997; Madriz, 1997; Pain, 2001; Stanko,
1990; Valentine, 1989). Women are more likely to be
victims of sex crimes, generating an ever-present fear of
sexual victimization (Riger, Gordon, & Le Bailley, 1978;
Stanko, 1990; Valentine, 1989; Warr, 1984). Women
often feel less capable of defending themselves (Riger
et al., 1978) and are socialized to be highly sensitive
of their physical and social vulnerabilities (Burt &
Estep, 1981; Goodey, 1997; Madriz, 1997; Scott, 2003;
Stanko, 1995). In addition, gendered childcare roles
can compel women to feel that they must defend not
only themselves, but also their children (Gilchrist,
Bannister, Ditton, & Farrall, 1998; Smith, 1989).
For women, fear of sexual assault may serve as a
“master offense”that influences their fear and risk
assessment for other forms of victimization (Ferraro,
1996). Warr (1984) suggested that female fear of sexual
assault influenced fear of nonpersonal crimes, such as
burglary. While males fear having property stolen via
burglary, women fear being sexually victimized by a
burglar. In this way, fear of sexual victimization
becomes “perceptually contemporaneous”with the
fear of other forms of victimization (Warr, 1984).
Recent research had provided empirical validation of the
shadow hypothesis (Ferraro, 1995, 1996; Fisher &
Sloan, 2003; May, 2001).
It had been argued that most studies had operationa-
lized the risk of victimization in such a way that female
crime risks were underestimated. If fear-to-risk ratios
were modeled to place greater weight on sexual crimes
and accounted for the actual risk women face of being
the victim of broader forms of sex crimes (including
sexual harassment), their fear would be more propor-
tional to their risk (Pain, 2001; Sacco, 1990; Stanko,
1995). Additionally, women may believe that they will
be perceived as partially responsible (due to routine
behavior, clothing, lifestyle, etc.) if they are the victim
of a sex or personal crime (Burt & Estep, 1981; Gordon
& Riger, 1989; Pain, 2001; Sacco, 1990). This per-
ception may generate a heightened fear of victimization
because of greater psychological harm associated with
sexual crimes (Ferraro, 1996; Gordon & Riger, 1989;
Riger et al., 1978). Their heightened fear and/or
perceived risk may cause women to modify their
behavior, resulting in reductions in actual victimization
(Sacco, 1990; Valentine, 1989).
These explanations are helpful in understanding
some aspects of the differences between men and
women; however, they do not help one understand
differences within the genders (Haynie, 1998). Treating
gender as an independent variable masks within-gender
variation. Recognizing that fear differs by gender, how
does it differ within the gender groups? Women report
rates of crime-related fear that are nearly twice that of
men (Haynie, 1998), but men and women are not
homogeneous in their levels of crime-related fears
(Newburn & Stanko, 1994); there are fearful men and
fearless women (Goodey, 1997; Warr, 1985). Unfortu-
nately, with few exceptions (LaGrange & Ferraro, 1989;
Lane & Meeker, 2003; May, 2001) research had focused
on explaining differences between, rather than within
the genders (Gilchrist et al., 1998).
1
This study sought to
contribute to the understanding of the gender differences
in crime-related fear by conducting separate analyses for
male and female respondents.
Literature review
A comprehensive review of prior literature studying
crime-related fear was beyond the scope of this study;
however, the following section reviews the most salient
research.
2
Four main issues are reviewed. First, the
study of crime-related fear necessitated consideration of
how key concepts are defined and measured. Next,
research suggested that crime-related fear might be the
product of at least three categories of predictors:
individual factors, fear facilitators, and fear inhibitors
(McGarrell et al., 1997); each was reviewed in turn.
Conceptualizing and operationalizing crime-related
fear
The specific dimensions of crime-related fear mea-
sured in a study (for example, fear of being the victim of
a specific crime versus general views of one's safety in a
given area) may influence the significance, magnitude,
and direction of explanatory models and individual
independent predictors. Recent research underscored the
importance of using care in interpreting the concepts
actually reflected by measures of crime-related fear
286 J.A. Schafer et al. / Journal of Criminal Justice 34 (2006) 285–301
(Farrall, Bannister, Ditton, & Gilchrist, 1997; Ferraro,
1995; Ferraro & LaGrange, 1987; Rountree & Land,
1996; Thompson et al., 1992; Warr, 1985; Warr &
Ellison, 2000; Warr & Stafford, 1983). Crime-related
fear has often been conceptualized and operationalized
in a manner consistent with major federal research
initiatives, such as the National Crime Victimization
Survey (Lewis & Salem, 1986; McGarrell et al., 1997;
Skogan & Maxfield, 1981) and the General Social
Survey (Haynie, 1998).
3
The use of such broad measures
had been challenged as ambiguous and inconsistent
(Bursik & Grasmick, 1993; Ferraro, 1995; Ferraro &
LaGrange, 1987; Rountree & Land, 1996; Thompson
et al., 1992; Warr, 2000) considering fear encompasses
“a wide range of subjective and emotional assessments
and behavioral reports”(DuBow, McCabe, & Kaplan,
1979, p. 1). Conceptual specificity and an understanding
of the dimensions represented by common measures are
imperative.
Scholars had employed a range of concepts as
dependent measures, ranging from affective and emo-
tional responses (fear) toward the general notion of
crime, to more rational and cognitive risk assessments
(perceived risk) for specific types of criminal victimiza-
tion (Ferraro & LaGrange, 1987; Warr & Stafford, 1983).
Some (Bursik & Grasmick, 1993; Garofalo & Laub,
1978) contended research seeking to measure general
crime-related fear might be gauging perceived disorder,
rather than actual perceptions of crime. These measures
are all of importance in understanding responses to and
perceptions of crime, but they gauge distinct concepts
(Ferraro, 1995; Warr, 2000). Variation would be
expected in the strength, significance, and direction of
their association with independent variables, as “risk
perceptions and actual fear may have different socio-
demographic predictors”(Rountree & Land, 1996,
p. 1370). There was no consensus on optimum
measurement of fear, thus care must be used to
understand specific dimensions of crime-related fear
used across studies (LaGrange & Ferraro, 1989).
Individual factors
Fear can be generated by one's actual or perceived
physical or social vulnerabilities (Bennett & Flavin,
1994; Goodey, 1997; Skogan & Maxfield, 1981; Taylor
& Hale, 1986), as well as one's “position in social
space”(Garofalo, 1981, p. 842). Social vulnerabilities
produce fear when residents perceive they frequent high
crime areas and/or engage in lifestyle behaviors that
place them at greater risk of victimization (Austin, Furr,
& Spine, 2002; Bennett & Flavin, 1994; Garofalo, 1981;
O'Mahony & Quinn, 1999). Physical vulnerabilities
affect those perceiving they are at a physical disadvan-
tage against possible assailants (e.g., women and the
elderly). Social and physical vulnerabilities are nested
within the attributes, habits, and environments of
individual research participants. Research had generally
supported the physical and social vulnerability perspec-
tives, using measures including gender, age, race,
income, level of education, marital status, and prior
victimization.
As discussed above, gender had been consistently
associated with higher levels of fear.In addition, many had
found age predicts crime-related fear (Baumer, 1985;
Garofalo & Laub, 1978; McGarrell et al., 1997; Parker &
Ray, 1990; Skogan, 1995), although perceived and actual
risk may be disproportionate; some recent studies had not
fully supported the age-fear relationship (Chiricos,
Hogan, & Gerta, 1997; Ferraro, 1995). Minority residents
(Garofalo & Laub, 1978; Parker & Ray, 1990; Skogan,
1995; Taylor & Hale, 1986) and those with lower incomes
report greater levels of fear (Baumer, 1985; Bennett &
Flavin, 1994; Garofalo, 1981; Garofalo & Laub, 1978;
McGarrell et al., 1997; Taylor & Hale, 1986; Will &
McGrath, 1995). Education tends to be associated with
income and may influence crime-related fear through
residential patterns and routine activities (Baumer, 1978;
Riger et al., 1978). Married residents express less fear than
their non-married counterparts (Baumer, 1978; Haynie,
1998; Mesch, 2000), perhaps due to lifestyle and activity
modifications, as well as a decreased sense of physical
vulnerability (although Warr & Ellison, 2000, noted
important complexities). Finally, early crime-related fear
research suggested victims of crime were more fearful
(Garofalo, 1977). Recent studies had yielded mixed
results (cf. Baumer, 1985; Bursik & Grasmick, 1993;
Ferraro, 1996; May & Dunaway, 2000; McGarrell et al.,
1997; Mesch, 2000; Parker & Ray,1990; Will & McGrath,
1995), leading to speculation that victimization is stronger
when it is vicarious (i.e., through the media, family,
friends,or neighbors) (Covington & Taylor,1991; Skogan
& Maxfield, 1981).
Fear facilitators
Certain citizen behaviors and perceptions have the
capacity to “facilitate”higher levels of fear (McGarrell
et al., 1997). It had long been suggested that there was
a link between disorder and both neighborhood
attachment and crime-related fear. Disorder directly
affects fear when residents become concerned with the
impact of such conditions (e.g., rowdy youth, public
drinking, panhandling, etc.). Disorder indirectly affects
287J.A. Schafer et al. / Journal of Criminal Justice 34 (2006) 285–301
fear through residents' perceptions and concerns
(Taylor, 1995); social and physical decay may indicate
a neighborhood has lost the ability to exert informal
social control (Kelling & Coles, 1996; Wilson, 1975),
generating fear and perceived vulnerability (Bennett &
Flavin, 1994). The perceived inability to control
neighborhood conditions can erode residents' social
ties and sense of community (Conklin, 1975; Lewis &
Salem, 1986; Podolefsky & DuBow, 1981; Wilson,
1975). Viewed in this way, disorder can be seen as
starting a “spiral of decay”(Skogan, 1990), generating
anxiety, helplessness, withdrawal, and the propagation
of disorderly conditions.
Studies had offered theoretical arguments and
empirical evidence that disorder, or the perception
thereof, directly and/or indirectly contributed to the
fear of crime (Covington & Taylor, 1991; Garofalo &
Laub, 1978; Hale, Pack, & Salked, 1994; LaGrange,
Ferraro, & Supancic, 1992; Lewis & Salem, 1986;
McGarrell et al., 1997; O'Mahony & Quinn, 1999; Pate,
Wycoff, Skogan, & Sherman, 1986; Podolefsky &
DuBow, 1981; Skogan, 1990; Skogan & Maxfield,
1981; Taylor, 2001; Taylor, Gottfredson, & Brower,
1985; Taylor & Hale, 1986; Wilson & Kelling, 1982),
although the veracity of the “broken windows”thesis
remains empirically questionable (Crank, Giacomazzi,
& Heck, 2003). Extant studies assessing crime and
disorder had used both objective (economic and social
data) and subjective (residents' perceptions) indicators
(Covington & Taylor, 1991; Garofalo & Laub, 1978;
Taylor, Shumaker, & Gottfredson, 1985). When a citizen
subjectively believes disorder is significant, this percep-
tion becomes real in its consequences (i.e., increased
fear), despite reality (Taylor, 2001; Taylor & Hale,
1986). Objective measures such as local crime rates
could be expected to shape fear; those residing in high-
crime areas would be expected to express more fear.
4
Fear may also be facilitated by vicarious crime
knowledge acquired from the media, family, friends, co-
workers, and neighbors (Covington & Taylor, 1991;
Rountree & Land, 1996). Further, those who are more
“in-tune”with crime problems in their community can
be more fearful as a result of their increased knowledge
(Garofalo, 1981; Lewis & Salem, 1986; McGarrell et al.,
1997; Skogan & Maxfield, 1981; Zhao, Gibson,
Lovrich, & Gaffney, 2002), particularly citizen volun-
teers who work with local police.
Fear inhibitors
Scholars have given less consideration to factors that
insulate people from fear or reduce its impact upon them
(McGarrell et al., 1997). Fear inhibitors generate
feelings of collective efficacy and capability to thwart
the incursion of disorderly, dangerous, and/or criminal
conditions. Inhibitors allow people to feel as if they are
not alone in wanting to make their community a safe and
stable place to live. Fear inhibitors include respondent's
bonds with their neighborhood; conceptually, stronger
attachments should work to reduce residents' fear,
although the opposite has also been found (Covington &
Taylor, 1991; Zhao et al., 2002). Other inhibitors include
perceived social integration in a neighborhood, resi-
dents' investment within a neighborhood (i.e., home-
ownership) (McGarrell et al., 1997), a neighborhood's
stability, and residents' perceptions of neighborhood
quality.
A citizen's perception of their neighborhood's infor-
mal networks and informal social control reflect beliefs
about that neighborhood's capacity to self-regulate
(Skogan & Maxfield, 1981). Neighborhood self-
regulation can contribute to crime and disorder
(Bursik & Grasmick, 1993), both real and perceived; as
fear is facilitated by disorder (Skogan, 1990); it is
inhibited by neighborhood integration (McGarrell et al.,
1997). A neighborhood's culture, efficacy, and integration
may shape local crime rates, necessitating the inclusion of
salient measures in modeling fear. For example, home-
owners have a long-term interest in the quality and
security of their neighborhood and might be more willing
to take an active role in neighborhood self-regulation.
Both homeowners and those residing in neighborhoods
withahighrateofhomeownershipwouldbeexpectedto
report a lower fear of crime (McGarrell et al., 1997).
The inhibition of fear can be furthered through
contextual factors exhibited in a citizen's neighborhood
(Baumer, 1985; Hale et al., 1994; Lewis & Salem, 1986;
Rountree & Land, 1996; Skogan & Maxfield, 1981). At a
most basic level, residents' global assessments of their
neighborhood broadly capture perceptions of their
residential environment. Neighborhood contextual fac-
tors indirectly reflected in measures of neighborhood
stability. Residents reporting they are likely to be living
in their neighborhood in another year can be viewed as
offering an indirect endorsement of that neighborhood.
In some cases, however, such a situation may actually
reflect a respondent's lack of social mobility.
Research objectives
This study sought to contribute to the understanding
of crime-related fear in three ways. First, the study
sought to examine if the utility of the analytical models
differed for males and females. Given the theoretical
288 J.A. Schafer et al. / Journal of Criminal Justice 34 (2006) 285–301
reasons men and women could experience crime-related
fear for divergent reasons, observable differences were
expected in the significance, magnitude, and direction
of observed relationships. Second, the analyses sought
to better discern the relationship between individual
factors, fear inhibitors, fear facilitators, and crime-
related fear. Few studies had incorporated all of these
factors into multivariate models to understand their
inter-relationships, a weakness of much of the early
crime-related fear literature (Bennett & Flavin, 1994).
Third, three different outcome measures of crime-
related fear (perceptions of safety, fear of personal
victimization, and fear of property victimization) were
used to better understand the relationships between
independent variables and various dimensions of crime-
related fear.
Data and methods
The data for the study were drawn from a larger
research project examining attitudes toward crime,
public safety, and the police in a midwestern commu-
nity. The study city was the largest community (with
approximately 125,000 residents) located in a metro-
politan area of over 300,000 residents. Its primary
economic base was the auto industry, government, and
education. The local police department employed 250
officers. In 1996 (the year preceding data collection), the
department received 275,000 calls for service (36
percent were emergencies), reported 10,000 index
crimes (a rate of 8,100 per 100,000 residents), and
handled 2,000 drug-related violations (1,600 per
100,000 residents). The community's population was
52 percent female, 68 percent White (non-Hispanic), 23
percent African American, and 10 percent Hispanic; the
average resident was thirty-one years of age. The
median household income was almost $35,000 and the
median household value was $73,000.
5
Data used in the analysis were based on the outcome
of structured telephone interviews conducted with 2,058
community residents. Residential telephone numbers
within the community were stratified for random
selection based upon the city's eighteen patrol beats;
at least one hundred residents over the age of eighteen
were interviewed within each patrol beat. Screening
mechanisms were employed to ensure that there were
similar numbers of male and female respondents.
6
Although there was variation in the gender distribution
within various patrol beats (women ranged from 44.6
percent to 54.3 percent of respondents by beat), through
serendipity the final sample contained an equal number
of male and female respondents.
Dependent variables
Three unique outcome variables were designed to
capture distinct aspects of perceived safety and fear of
personal and property crime victimization. The per-
ceived safety construct reflected a general emotional
response to fear in a broad context; whereas, the fear of
personal and property victimization measures were
more focused indicators of crime-specific assessments
of fear. The variables and scales listed below, as well as
the individual items that comprise them, are described in
Appendix A. Descriptive statistics are presented in
Table 1 for the total sample and by gender.
Perceived safety was operationalized using a two-
item additive scale (α= 0.652).
7
Respondents were
asked to rate, (1) “How safe do you feel after dark?”(1 =
very safe, 2 = somewhat safe, 3 = somewhat unsafe, 4 =
very unsafe), and (2) “How often does your worry about
crime prevent you from doing things you would like to
do in your neighborhood?”(1 = never, 2 = rarely, 3 =
sometimes, 4 = often, 5 = very often).
Fear of property and personal victimization were both
constructed using three-item additive scales. For the
property victimization measure, individuals were asked
about their fear in relationship to how worried they were
that: someone will try to break into your home while no
one is there, vandalize your home, or steal something
outside of your home (α= 0.710). For the personal
victimization measure, respondents were queried about
their fear that: someone will try to attack you while you
are outside of your neighborhood, you will be a victim of
a violent crime in your neighborhood, or you will be a
victim of a violent crime in your home (α= 0.801). The
response options for the personal and property victim-
ization items ranged from 1 to 3 and included not
worried, somewhat worried, and very worried.
When considered by gender, there were significant
differences in the mean scale scores for fear of personal
crime victimization and perceived safety measures (see
Table 2). On both measures, female respondents were
significantly more fearful than males. Male respondents
actually reported a higher fear of property victimization,
although the difference was not statistically significant.
Even though certain significant relationships emerged, it
was interesting to note that the overall fear reported by
the respondents (particularly for the personal and
property victimization measures) was moderate to low.
The majority of respondents indicated they had a low
level of fear in respect to both perceived lack of safety
and the victimization-specific measures. In order to
mitigate some of the skewness in the additive scales,
each measure was reclassified to reflect high, medium,
289J.A. Schafer et al. / Journal of Criminal Justice 34 (2006) 285–301
and low levels of fear. For the victimization measures,
scores of 3 were classified as low, 4–6 were classified as
medium, and 7–9 were classified as high. For the
perceived safety measure, scores of 2 and 3 were
classified as low, 4–6 as medium, and 7–9 as high.
Independent variables
Citizen level controls
Consistent with previous research, a number of
individual-level demographic characteristics were in-
cluded in the model as statistical controls. A series of
dummy variables were constructed to measure demo-
graphic influences including household income, race,
employment and the natural log of the repondent’s age in
years.
8
A central goal of the current research was to study
the correlates of fear among gender groups, thus the
analysis was disaggregated by gender groups and gender
was excluded as an independent variable.
Fear inhibitors
It had been suggested that more cohesive and
integrated neighborhoods have a stronger capacity to
insulate residents from fear of crime. As such, the models
included measures of neighborhood integration, neigh-
borhood assessment, neighborhood stability, and home-
owner status. A four-item weighted factor score was
developed to measure neighborhood integration. Resi-
dents were asked how often they have a friendly chat
with neighbors, get together socially with neighbors,
agree to watch a neighbor's home, and share tools or
other things with neighbors (eigenvalue 2.385 factor >
0.68, α= 0.77). A dichotomousmeasure ofneighborhood
assessment was derived from respondents' perceptions of
their neighborhoods as a place to live. Dichotomous
measures of respondent homeowner status and neighbor-
hood stability (likelihood respondent would still be
residing in their neighborhood in one year) were also
included in the predictive models.
Table 1
Descriptive statistics—total sample and by gender
Male (n = 1,029) Female (n = 1,029) Total sample (n = 2,058)
Mean S.D. Mean S.D. Mean S.D.
Outcome variable
Perceived safety 1.54 0.64 1.89 0.72 1.71 0.70
Fear of personal victimization 1.36 0.57 1.54 0.64 1.45 0.62
Fear of property victimization 1.98 0.62 1.95 0.65 1.96 0.63
Independent measures
Individual factors
Married 0.51 0.50 0.48 0.50 0.50 0.50
Education 0.66 0.47 0.56 0.50 0.61 0.49
Age 3.72 0.38 3.78 0.43 3.75 0.41
Income 0.32 0.47 0.23 0.42 0.28 0.45
Race 0.81 0.39 0.82 0.38 0.82 0.39
Employment 0.61 0.49 0.35 0.48 0.48 0.50
Fear facilitators
Perceptions of disorder 0.05 1.01 −0.05 0.99 0.00 1.00
Perceptions of major crime 0.04 1.05 −0.04 0.95 0.00 1.00
Neighborhood association 0.16 0.37 0.16 0.37 0.16 0.37
Children in home 0.32 0.47 0.37 0.48 0.35 0.48
Personal crime rate 0.26 0.44 0.24 0.43 0.22 0.43
Fear inhibitors
Homeowner 0.69 0.46 0.72 0.45 0.71 0.46
Neighborhood integration −0.06 1.00 0.06 0.98 0.00 1.00
Neighborhood assessment 0.67 0.47 0.70 0.46 0.68 0.47
Neighborhood stability 0.82 0.38 0.78 0.41 0.80 0.40
Table 2
Distribution of initial victimization and perceived safety constructs by
gender
Range Gender Mean Std. deviation
Perceived safety⁎2–9 Male 3.7461 1.7790
Female 4.7880 1.9937
Personal victimization⁎3–9 Male 3.7168 1.2936
Female 4.0923 1.5289
Property victimization 3–9 Male 5.0860 1.6899
Female 4.9970 1.6929
⁎Scores were significantly different at p < .05 by gender.
290 J.A. Schafer et al. / Journal of Criminal Justice 34 (2006) 285–301
Fear facilitators
Five fear facilitator constructs were incorporated into
the current models. Perceptions of major crime was a
three-item weighted factor score and calculated based on
how problematic respondents perceived assaults in
public, shootings and other public violence, and violent
attacks were within their neighborhood (eigenvalue
1.89, factor loadings > 0.77, α= 0.71). Perceptions of
disorder was measured using a seven-item weighted
factor score based on how problematic respondents
perceived litter and trash, loitering, public drinking,
drug dealing, parents who don't supervise their children,
landlords not maintaining their property, and gangs were
within their neighborhood (eigenvalue 3.21, factor
loadings > 0.69, α= 0.81). A dichotomous indicator of
minor children in the home was incorporated into the
models based on the finding that some reported fear
might be altruistic concern for the safety of others, rather
than personalized fear for one's self (Warr & Ellison,
2000). A dichotomous measure of participation in a
neighborhood association was also included.
Finally, an indicator of arrests for serious personal
crimes in each of the eighteen patrol beats was included
in the model at the neighborhood level.
9
Data for the
personal crime measure were obtained from official
police records, and represented the number of murder,
sexual assault, felony assault, and robbery arrests made
by the police in the study community in 1997. Data on
the population of each patrol beat were not available;
therefore, the incidence of personal crime arrests in each
policepatrolbeatwasranked into quartiles. The
personal crime measure was then dichotomized into
those individuals living in police beats who had arrests
for personal crimes in the top quartile and those
residents who inhabited police patrol beats in the lowest
three quartiles (reference category).
Analysis
A series of hierarchical ordered probit models were
used to explore the relative effects of individual factors,
fear inhibitors, and fear facilitators on safety and crime-
specific perceptions. Hierarchical linear modeling
(HLM) was the most appropriate technique for this
analysis because it allowed individual data to be nested
within patrol beats, while examining variation in fear
and perceived safety at the individual level (Raudenbush
& Bryk, 2002). The dependent variables used in this
analysis were ordinal-level variables; therefore, the
ordered probit model was most appropriate.
Theoretically, the ordered probit statistical model is
based on the assumption that y, the dependent measure,
is a ratio-level variable (see Long, 1997). As ywas
unobserved in this model, the ordered probit model was
developed to estimate the latent variable y⁎. The τin the
model represented thresholds or cutpoints that were
imposed on the infinite number of outcomes that were
possible with y⁎. To estimate ybased on y⁎, the
following measurement model was used:
1Zlow if s0¼lzyi
⁎<τ1
yi ¼2⇒medium if s1zyi
⁎<τ2
3⇒high if s2zyi
⁎<s3¼l
Two models were estimated for each dependent
variable in the analysis. The first (unconditional) model
provided estimates of model-fit and a reliability
coefficient for the sample mean. In the second model,
exogenous predictors were added to evaluate the
probability that perceptions of safety and fear of
victimization varied as a function of individual and
neighborhood level variables. In addition, Z scores were
calculated for each of the exogenous predictors in the
models according to the formula presented by Pater-
noster, Brame, Mazerolle, and Piquero (1998). These
coefficient comparison tests provided greater context by
informing whether the effects of the individual factors,
fear facilitators, and fear inhibitors varied by gender.
Tests for multicollinearity were conduced for each
dependent measure; no variance inflation factor or
tolerance scores were high enough to suggest the
presence of multicollinearity. To facilitate additional
comparisons, all three models were estimated for the full
sample, treating gender as an independent variable (see
Appendix B).
Findings
When considered by gender, there were significant
differences in the mean scale scores for fear of personal
crime victimization and perceived safety measures (see
Table 2). On both measures, female respondents were
significantly more fearful than males. Male respondents
actually reported a higher fear of property victimiza-
tion, although the difference was not statistically
significant. Even though certain significant relation-
ships emerged, it was interesting to note that the overall
fear reported by the respondents (particularly for the
personal and property victimization measures) was
moderate to low.
Perceived safety
Table 3 presents the results from the perceived safety
analysis. Overall, subtle differences were found in the
291J.A. Schafer et al. / Journal of Criminal Justice 34 (2006) 285–301
reported safety of men and women with most of the
variation between gender groups being driven by
individual factors. The results from the analysis of the
male subgroup suggested that older and non-White men
were less likely to perceive being safe in their
neighborhood. In fact, the comparison coefficients
tests for both age (z = 2.91) and race (z = −3.04) were
statistically significant. This finding suggested that age
and race have significantly different effects on men's
perceptions of safety than they do for women's
perceptions. Based on the odds ratio statistics presented,
older males and non-White males were the least likely to
report feeling safe. Consistent with the physical and
social vulnerability perspectives, one would expect that
older persons and non-Whites would be most likely to
report fear; however, one would also assume that these
factors would be more likely to work in concert for
female members of the sample because they are
typically thought to perceive themselves as being
more physically vulnerable than men.
The remaining demographic variables had divergent
effects on perceived safety, although the difference
between the genders was not statistically significant.
Income had a significant, negative effect on safety for
the male subgroup analysis, but did not reach a level of
significance for women. Education was the only
significant coefficient in the female subgroup analysis;
women with post-high school educations felt less safe.
Respondents' perceptions of neighborhood disorder
and major crime were the only statistically significant
fear facilitators. Men and women who perceived high
levels of disorder and major crime in their neighbor-
hood felt less safe in their neighborhood. Participation
in neighborhood meetings and having minor children
in the home were not significant for either gender
group. Personal crime was also not a significant
Table 3
Perceived safety by gender
Male (N = 1,029) Female (N = 1,029) Comparison
Coeff. s.e. Odds Coeff. s.e. Odds Z-score
Constant −4.55⁎0.85 –−0.23 0.68 –
Level I—individual level
Individual factors
Married 0.13 0.15 0.94 0.10 0.13 1.11 0.15
Education −0.10 0.14 0.90 −0.33⁎⁎ 0.13 0.72 1.20
Age 0.77⁎⁎ 0.22 2.16 −0.04 0.17 0.96 2.91⁎⁎
Income −0.60⁎⁎ 0.16 0.55 −0.22 0.16 0.80 −1.68
Race −0.52⁎⁎ 0.17 0.59 0.19 0.16 1.21 −3.04⁎⁎
Employment −0.18 0.15 0.84 −0.22 0.14 0.80 0.19
Fear facilitators
Perceptions of disorder 0.50⁎⁎ 0.09 1.65 0.30⁎⁎ 0.09 1.35 1.57
Perceptions of major crime 0.40⁎⁎ 0.08 1.50 0.33⁎⁎ 0.09 1.39 0.58
Neighborhood association −0.17 0.19 0.84 0.15 0.17 1.16 −1.26
Children in home −0.16 0.16 0.85 −0.12 0.15 0.89 −0.18
Fear inhibitors
Homeowner −0.04 0.17 0.96 −0.19 0.15 0.83 0.66
Neighborhood integration −0.19⁎⁎ 0.07 0.83 −0.13 0.07 0.88 −0.61
Neighborhood assessment −0.55⁎⁎ 0.16 0.58 −0.70⁎⁎ 0.17 0.50 0.64
Neighborhood stability −0.26 0.17 0.77 −0.46⁎⁎ 0.17 0.63 0.83
Level II—neighborhood level
Personal crime 0.17 0.16 1.19 0.05 0.15 1.05 0.55
Model fit
Threshold difference
Unconditional model 2.32⁎⁎ 0.11 2.13⁎⁎ 0.08
Conditional model 2.89⁎⁎ 0.14 2.48⁎⁎ 0.10
Level II variance
Unconditional model 0.19⁎⁎ 0.10⁎⁎
Conditional model 0.01 0.00
Reliability 0.72 0.61
⁎p < .05.
⁎⁎ p < .01 (two-tailed tests).
292 J.A. Schafer et al. / Journal of Criminal Justice 34 (2006) 285–301
predictor of perceived safety. For both men and
women, perceptions of crime and disorder outweighed
the effect of the official crime measure. Respondents
were influenced by their subjective impressions of
crime and disorder, rather than objective measures of
personal crime.
Although a number of inhibitors were found to be
significant predictors of perceived safety, none varied
significantly by gender. Overall, respondents who felt
their neighborhood was a good place to live also
reported that they felt safe in their neighborhood; this
effect was quite strong for both men and women. Based
on the reported odds ratios, it was possible to conclude
that women or men who indicated that their neighbor-
hood was a good place to live were approximately half
as likely to report low levels of perceived safety. In
addition, women who indicated that they were likely to
remain in their neighborhood over the following year
and men who reported high levels of neighborhood
integration were more likely to report feeling safe in
their neighborhood.
Fear of personal victimization
Based on research conducted to date, it would be
expected that the predictive models for fear of
personal victimization would vary significantly by
gender, with women reporting greater fear of being
victimized. Although female respondents reported
significantly higher levels of fear of personal victim-
ization, the models as estimated did not yield dramatic
gender-based variation (see Table 4). The majority of
the demographic, facilitator, and inhibitor variables did
not vary significantly by gender. Only the perceptions
of disorder (z = 2.75) and age (z = 3.64) contrasts
achieved statistical significance in the model. For both
men and women, individuals who perceived high
levels of disorder in their neighborhood feared
personal victimization. Based on the odds ratio
calculations, men were more likely to be negatively
affected by neighborhood disorder. In addition, the
effect of age on fear of personal victimization varied
by gender with older men reporting a disparately
higher level of fear of personal victimization.
In reference to the individual factors, the income and
race constructs were statistically significant for the male
sample. Male respondents with incomes less than
$50,000 were significantly more likely to report being
fearful of personal victimization. Minority males were
also more likely to indicate a fear of personal
victimization. A similar relationship between race,
income, and fear of personal victimization was not
found for women. Apart from perceptions of major
crime and disorder, the remaining fear facilitators did
not achieve statistical significance. For both genders,
individuals that perceived a high level of crime in their
neighborhood were more likely to report fear of personal
victimization. The official crime measures had little
impact in the models. The presence of children in the
home and participation in a neighborhood association
were not significantly related to fear of personal
victimization. None of the fear inhibitors significantly
differentiated between gender groups. Men who per-
ceived a high level of neighborhood integration in their
neighborhood were less likely to fear personal victim-
ization, as were women with positive neighborhood
assessments.
Fear of property victimization
Very little difference was found between gender
groups in the fear of property victimization model
presented in Tab le 5. Employment was the only
significant individual factor. For women, employment
increased the likelihood of reporting property-related
fear. Unlike the perceived crime and personal victimi-
zation models, age and race did not achieve a level of
statistical significance. Based on these findings, it
appeared that the physical vulnerability perspective
had little utility in explaining fear of property
victimization.
Consistent with the perceived safety and fear of
personal crime models, both men and women who
perceived their neighborhood to be disorderly and to
have high levels of major crime reported higher levels of
fear of property victimization; the personal crime
measure did not influence fear of property victimization.
Based on the significant z-score (z = 2.30), it was
important to note that the effect of perceptions of
disorder on fear of property victimization varied by
gender. Judging from the odds ratios, men's perceptions
were more adversely affected by perceptions of disorder.
Only homeownership status and neighborhood assess-
ment were found to be statistically significant inhibitors
of fear. Overall, respondents who reported that their
neighborhood was a good place to live were less likely
to fear property crime victimization. Consistent with
previous models, the effect of neighborhood assessment
was moderately strong. In fact, for both men and
women, reporting a positive neighborhood assessment
decreased the odds that an individual would report a
medium or high level of fear of personal victimization
by approximately 40 percent, holding all other variables
constant. In contrast, women and men who owned
293J.A. Schafer et al. / Journal of Criminal Justice 34 (2006) 285–301
homes were significantly more likely to report being
fearful of property victimization. Although current
research suggested that homeownership reduces fear
of crime, acquiring substantial possessions might
actually increase the fear that these assets will be
victimized.
Female respondents perceived that they were less
safe and were more worried about personal victimiza-
tion; however, the predictive models did little to
illuminate differences between the genders. In order to
further understand the relative effect of gender on
perceived safety and personal and property victimiza-
tion, a series of models were estimated using gender as
an independent variable (see Appendix B). Consistent
with the baseline models, women felt less safe overall
and were more fearful of personal victimization than
men. The standardized coefficients further revealed the
magnitude of the effect of gender on perceived safety
and fear of personal victimization. In contrast, gender
had little effect in the property victimization model.
Finally, assessments of disorder and major crime in the
neighborhood and negative perceptions of neighbor-
hood quality of life consistently increased levels of
reported fear.
Discussion
Research exploring gender difference in crime-
related fear had generally been limited to comparison
of mean scores for specific types of victimization
(Ferraro, 1996; Fisher & Sloan, 2003) and with few
exceptions (Lane & Meeker, 2003) multivariate models
had treated gender as an independent variable. Prior
shadow hypotheses studies had varied in sampling
schemes (c.f. Ferraro, 1996; Fisher & Sloan, 2003; Warr,
1984), offenses studied, and the measurement of fear
Table 4
Fear of personal victimization by gender
Male (N = 1,029) Female (N = 1,029) Comparison
Coeff. s.e. Odds Coeff. s.e. Odds Z-score
Constant −6.74⁎⁎ 0.99 –−1.93⁎0.72 –
Level I—individual level
Individual factors
Married 0.13 0.17 1.14 0.10 0.14 1.11 0.14
Education 0.01 0.16 1.01 −0.08 0.13 0.92 0.44
Age 1.05⁎⁎ 0.25 2.85 −0.07 0.18 0.93 3.64⁎⁎
Income −0.60⁎⁎ 0.19 0.55 −0.32 0.17 0.72 −1.10
Race −0.56⁎⁎ 0.18 0.57 −0.15 0.17 0.86 −1.66
Employment 0.08 0.17 1.08 −0.01 0.15 0.99 0.40
Fear facilitators
Perceptions of disorder 0.56⁎⁎ 0.10 1.75 0.19⁎0.09 1.21 2.75⁎⁎
Perceptions of major crime 0.42⁎⁎ 0.08 1.52 0.51⁎⁎ 0.09 1.67 −0.75
Neighborhood association 0.30 0.20 1.35 −0.00 0.18 1.00 1.11
Children in home 0.12 0.18 1.13 −0.02 0.16 0.98 0.58
Fear inhibitors
Homeowner −0.34 0.18 0.71 −0.09 0.16 0.91 −1.04
Neighborhood integration −0.17⁎0.08 0.84 −0.11 0.07 0.89 −0.56
Neighborhood assessment −0.22 0.18 0.80 −0.51⁎⁎ 0.17 0.60 1.17
Neighborhood stability −0.06 0.19 0.94 0.06 0.18 1.06 −0.46
Level II—neighborhood level
Personal crime 0.07 0.18 1.07 0.01 0.16 1.01 0.25
Model fit
Threshold difference
Unconditional model 2.62⁎⁎ 0.14 2.30⁎⁎ 0.11
Conditional model 2.69⁎⁎ 0.17 2.63⁎⁎ 0.13
Level II variance
Unconditional model 0.24⁎⁎ 0.34⁎⁎
Conditional model 0.00 0.00
Reliability 0.73 0.63
⁎p < .05.
⁎⁎ p < .01 (two-tailed tests).
294 J.A. Schafer et al. / Journal of Criminal Justice 34 (2006) 285–301
(c.f. Ferraro, 1996; Fisher & Sloan, 2003; Lane &
Meeker, 2003). This study further differed by using
HLM (as opposed to regression) for multivariate
modeling. Nonetheless, parallels could be drawn
between this analysis and other studies of fear and the
shadow hypothesis. Consistent with prior studies,
women were equally or more fearful of crime then
were men (Ferraro, 1996; Fisher & Sloan, 2003; Lane &
Meeker, 2003; Warr, 1984). In particular, women
expressed more fear of personal victimization and
more concern with their perceived safety. Contrary to
prior research findings (Ferraro, 1996; Fisher & Sloan,
2003; Warr, 1984), men and women reported compara-
ble fear of property victimization.
Despite the differences in the baseline of fear, men
and women were only significantly different on a
small number of independent variables. The cases of
gender-based differences, coupled with nonsignificant
differences in male and female models did, however,
lend some support to the shadow of sexual assault
hypothesis (Ferraro, 1995; Fisher & Sloan, 2003;
Pain, 2001; Valentine, 1989). With only two excep-
tions (education in the perceived safety model and
employment in the fear of property victimization
model), the views reported by women did not vary
by demographic factors. Findings from studies
aggregating men and women suggested that demo-
graphic factors should be prominent in predicting
safety and perceived risk (Ferraro, 1996; Haynie,
1998; Rountree & Land, 1996; McGarrell et al.,
1997) although exceptions could be noted (Lane &
Meeker, 2003).
This analysis found demographics to be consistent-
ly significant predictors for men (as seen in the
perceived safety and fear of personal victimization
models), although they were rarely significant for
Table 5
Fear of property victimization by gender
Male (N = 1,029) Female (N = 1,029) Comparison
Coeff. s.e. Odds Coeff. s.e. Odds Z score
Constant −2.11⁎0.86 –−1.37 0.70 –
Level I—individual level
Individual factors
Married 0.00 0.15 1.00 0.20 0.14 1.22 −0.97
Education 0.05 0.15 1.05 0.15 0.13 1.16 −0.50
Age 0.04 0.21 1.04 −0.12 0.18 0.89 0.58
Income 0.12 0.16 1.27 −0.17 0.17 0.84 1.24
Race −0.06 0.17 0.94 0.22 0.17 1.25 −1.16
Employment 0.23 0.15 1.26 0.36⁎⁎ 0.14 1.43 −0.63
Fear facilitators
Perceptions of disorder 0.69⁎⁎ 0.10 1.99 0.38⁎⁎ 0.09 1.46 2.30⁎
Perceptions of major crime 0.37⁎⁎ 0.08 1.45 0.25⁎⁎ 0.09 1.28 1.00
Neighborhood association −0.18 0.18 0.84 −0.06 0.18 0.94 −0.47
Children in home 0.07 0.16 1.07 0.16 0.15 1.17 −0.41
Fear inhibitors
Homeowner 0.35⁎0.17 1.42 0.32⁎0.16 1.38 0.13
Neighborhood integration −0.11 0.07 0.90 −0.05 0.07 0.95 −0.61
Neighborhood assessment −0.46⁎⁎ 0.17 0.63 −0.53⁎⁎ 0.17 0.59 0.29
Neighborhood stability −0.05 0.17 0.95 −0.16 0.18 0.85 0.44
Level II—neighborhood level
Personal crime −0.09 0.24 0.91 −0.17 0.16 0.84 0.28
Model fit
Threshold difference
Unconditional model 2.95⁎⁎ 0.10 2.70⁎⁎ 0.09
Conditional model 3.55⁎⁎ 0.13 3.01⁎⁎ 0.11
Level II variance
Unconditional model 0.10⁎⁎ 0.06⁎⁎
Conditional model 0.10⁎⁎ 0.00
Reliability 0.57 0.46
⁎p < .05.
⁎⁎ p < .01 (two-tailed tests).
295J.A. Schafer et al. / Journal of Criminal Justice 34 (2006) 285–301
women. If the fear of sexual assault overrides fear of
other crimes, it makes intuitive sense that this would
be the case, regardless of a woman's age, race,
education, income, marital status, and employment
status. That other factors, such as perceptions of
neighborhood conditions, were significant for women
provided further support. Although a female respon-
dent might not have been at more/less risk based upon
her race, her perceptions might have varied depending
on the level of security she saw in her surrounding
environment. Further, if fear among women was
overshadowed by concern over sexual victimization,
it would be expected that women would express
greater fear for their safety and personal victimization.
If men had less fear of sexual victimization, their fear
of personal victimization would be constrained to a
smaller range of possible offenses.
This study also provided important insight into the
study of fear of crime in general. Specifically, fear and
safety were related more with subjective perceptions of
neighborhood quality of life than with objective
measures of neighborhood dangers. Perceptions of
major crime, disorder, and neighborhood quality were
statistically significant in nearly all of the models
estimated. In contrast, the measure of personal crime
included at the patrol beat level was never statistically
significant. This was a critical finding bolstering the
argument that subjective evaluations of neighborhoods
inform the understanding of fear (Bursik & Grasmick,
1993; Hale et al., 1994; O'Mahony & Quinn, 1999;
Taylor & Hale, 1986). Residents who believed their
neighborhoods were disorderly or less satisfactory
reported more concern over safety and risk of criminal
victimization, net of reported personal crime at the
neighborhood level. This suggests reducing the personal
crime rate in a neighborhood can yield only negligible
effects on fear; by itself, reducing crime may not reduce
fear (Lane & Meeker, 2000). Instead, reducing per-
ceived disorderly situations and improving the quality of
life in the neighborhood may go farther in reducing fear
for residents.
In a similar light, neighborhood integration was
significant in the perceived safety and personal
victimization models, but only for male respondents.
The lack of statistical significance for property victim-
ization, and among female respondents, was an
interesting outcome of the study. Conceptually, neigh-
borhood integration should not exhibit a variable effect
for male and female respondents; the nature of this
concept was such that gender differences in its affect
would not be expected. In addition, the effect of
neighborhood integration would be expected to have a
constant impact across the dependent variables. The
explanation for this outcome was unclear. It was
possible that neighborhood integration, as a concept,
did not have the expected broad influence.
In contrast, involvement in neighborhood associa-
tions was never a significant predictor of fear or
perceived safety. Contrary to recent findings (Zhao
et al., 2002), involvement in a neighborhood associa-
tion did not facilitate or mitigate fear in this study. This
might be the result of different samples and operational
decisions. Zhao and colleagues contrasted citizen
volunteers with “average”residents, finding greater
reported fear among those who volunteered their time to
assist the police. This study compared those who had
some level of involvement in a neighborhood associa-
tion. Simply having occasional exchanges of informa-
tion with officers in the context of working with
neighbors to improve a neighborhood's quality of life
was not enough to generate greater levels of fear.
The results of this study must be tempered with
several acknowledged limitations. First, the data did not
include a victimization measure. Some had found prior
victimization (direct and/or vicarious) shaped fear of
crime and perceptions of victimization risk (Covington
& Taylor, 1991; Ferraro, 1996; Parker & Ray, 1990;
Rountree & Land, 1996; Skogan, 1987); unfortunately,
victimization measures were not available. Victimiza-
tion has conceptual significance for shaping fear;
however studies had failed to conclusively establish a
relationship (Bennett & Flavin, 1994; Bursik &
Grasmick, 1993; Gates & Rohe, 1987; McGarrell
et al., 1997; Miethe, 1995; Skogan & Maxfield, 1981).
Second, the items used to create the two fear of
victimization measures cued respondents to report their
level of “worry”about possible victimization; although
some (Lane & Meeker, 2000) had suggested measures
of worry were adequate reflections of fear, the extent to
which these were analogous was not clear. Third, the
dependent measures assessed fear of victimization for
several personal and property offenses, but they did not
account for as many possible offenses as others had used
(Ferraro, 1996; LaGrange & Ferraro, 1989; Warr, 1984;
Warr & Stafford, 1983). This limitation arose from the
broader intent of the original data project; resource
constraints limited the number of survey items measur-
ing fear-related issues. Finally, the analysis did not
directly assess explanations suggesting fear of sexual
victimization overrode broader fear among women
(Ferraro, 1995, 1996; Gordon & Riger, 1989; Warr,
1984).
Overall, these findings hold important implications
for the understanding of fear of crime and criminal
296 J.A. Schafer et al. / Journal of Criminal Justice 34 (2006) 285–301
victimization. Gender does matter in determining the
extent to which a citizen is fearful or fearless; however,
the differences in this sample were less than would have
been expected based upon existing research. Even
though women reported higher levels of fear, the
predictive models were a better statistical fit for male
respondents. Findings suggested that fear among
women (and men) might be more complex than
originally conceived (see Lane & Meeker, 2003). Future
research should delve deeper into gender differences in
perceptions of fear and criminal victimization. In
specific, additional inquiry into the root causes of the
fear of personal victimization among women is
warranted.
Acknowledgements
This research project was supported by a grant
from the National Institute of Justice (95IJCX0093).
Points of view or options expressed are those of the
authors and do not necessarily represent the official
position or policy of the National Institute of Justice.
An earlier version of this study was presented at the
2002 meetings of the Academy of Criminal Justice
Sciences in Anaheim, California. The authors thank
Stephen D. Mastrofski, the editor, and the anonymous
reviewers for their many contributions to this research
project.
Appendix A. Description of variables
Outcome variables
Perceived safety A two-item scale (alpha 0.652) including: how
safe do you feel walking alone in your
neighborhood after dark? (1 = very safe, 2 =
somewhat safe, 3 = somewhat unsafe, 4 = very
unsafe) and how often does your worry about
crime prevent you from doing things you would
like to do in your neighborhood? (1 = never, 2 =
rarely, 3 = sometimes, 4 = often, 5 = very often).
Fear of personal
victimization
A three-item scale (alpha 0.801) including: how
worried are you that: someone will try to attack
you while you are outside your neighborhood,
you will be a victim of a violent crime in your
neighborhood, and you will be a victim of a
violent crime in your home? (1 = not worried,
2 = somewhat worried, 3 = very worried).
Fear of property
victimization
A three-item scale (alpha 0.710) including: how
worried are you that: someone will try to break
into your home while no one is there, vandalize
your home, or steal something outside of your
home? (1 = not worried, 2 = somewhat worried,
3 = very worried).
(continued on next page)
Independent measures
Individual factors
Married A dummy variable with married = 1.
Individuals who were single, divorced, or
widowed served as the reference category.
Education A dummy variable with post high school
education = 1.
Age The natural log of the respondent's age in years.
Income A dummy variable with household income
more than $50,000 = 1.
Race A dummy variable with White = 1.
Employment A dummy variable with fulltime employment =
1. Individuals who were unemployed or worked
part time served as the reference category.
Female A dummy variable with female = 1.
Fear facilitators
Perceptions of
disorder
A seven-item factor score (eigenvalue 3.21,
factor loadings > 0.69, α= 0.81) including:
tell me whether you think that lately litter and
trash, loitering, public drinking, parents who
don't supervise their children, landlords not
maintaining their property, drug dealing, and
gangs is (1 = not a problem, 2 = somewhat of
a problem, 3 = a big problem).
Perceptions of major
crime
A three-item factor score (eigenvalue 1.89,
factor loadings > 0.77, α= 0.71) including: tell
me whether you think that assaults in public,
shootings and other public violence, and violent
attacks on neighborhood residents is (1 = not a
problem, 2 = somewhat of a problem, 3 = a big
problem).
Neighborhood
association
A dummy variable with those individuals
who indicated they attended neighborhood
association meetings = 1.
Children in home A dummy variable with respondents who were
living with one or more children under the age
of eighteen = 1.
Personal crime A dummy variable with neighborhoods with
murder, sexual assault, robbery, and felony
assault arrests in 1997 within the highest
quartile of all neighborhoods = 1.
Fear inhibitors
Homeowner A dummy variable with homeowner = 1.
Neighborhood
integration
A four-item factor score (eigenvalue 2.385,
factor loadings > 0.68) including: have a
friendly chat with neighbors on your block
when they are outside; get together socially
with neighbors on your block; agree to watch a
neighbor's home when they are on vacation;
share tools or other things with your neighbors
(1 = never, 2 = rarely, 3 = sometimes, 4 = often).
Neighborhood
assessment
A dummy variable with those individuals who
indicated their neighborhood as an excellent or
good place to live = 1.
Neighborhood
stability
A dummy variable with those individuals who
are somewhat or very likely to remain in their
neighborhood a year from now = 1.
297J.A. Schafer et al. / Journal of Criminal Justice 34 (2006) 285–301
Appendix B. Hierarchical ordinal regression models for perceived safety, personal and property
victimization—total sample (n = 2,058)
Perceived safety Personal victimization Property victimization
Coeff. s.e. Odds Coeff. s.e. Odds Coeff. s.e. Odds
Constant −2.44⁎⁎⁎ 0.57 −4.25⁎⁎⁎ .62 −1.61⁎.57
Level I—individual level
Individual factors
Female 1.16⁎⁎⁎ .09 3.18 0.76⁎⁎⁎ .10 2.14 0.04 .09
Married 0.08 .11 0.11 .12 0.12 .11
Education −0.20⁎.09 0.81 −0.03 .10 0.12 .10
Age 0.24 .14 0.41⁎⁎ .15 1.50 −0.09 .14
Income −0.39⁎⁎⁎ .11 0.67 −0.45⁎⁎⁎ .13 0.64 −0.04 .11
Race −0.14 .12 −0.39⁎⁎ .12 0.68 0.10 .12
Employment −0.22⁎.10 0.80 −0.02 .11 0.27⁎.10 1.30
Fear facilitators
Perceptions of disorder 0.39⁎⁎⁎ .06 1.47 0.35⁎⁎⁎ .07 1.42 0.52⁎⁎⁎ .07 1.68
Perceptions of major crime 0.37⁎⁎⁎ .05 1.44 0.46⁎⁎⁎ .06 1.58 0.32⁎⁎⁎ .06 1.37
Neighborhood association 0.02 .12 0.13 .13 −0.11 .13
Children in home −0.12 .11 0.03 .12 0.12 .11
Fear inhibitors
Homeowner −0.11 .11 −0.15 .12 0.35⁎⁎ .11 1.42
Neighborhood integration −0.15⁎⁎ .05 0.86 −0.13⁎.05 0.87 −0.09 .05
Neighborhood assessment −0.62⁎⁎⁎ .11 0.54 −0.40⁎⁎⁎ .12 0.67 −0.49⁎⁎⁎ .12 0.62
Neighborhood stability −0.34⁎⁎ .11 0.71 0.00 .13 −0.07 .12
Level II—neighborhood level
Personal crime 0.13 .11 0.01 .12 −0.15 .15
Model fit
Threshold difference
Unconditional model 2.10⁎⁎⁎ .06 2.25⁎⁎⁎ .09 2.78⁎⁎⁎ .07
Conditional model 2.61⁎⁎⁎ .08 2.62⁎⁎⁎ .10 3.20⁎⁎⁎ .08
Level II variance
Unconditional model 0.12⁎⁎⁎ .35 0.16⁎⁎⁎ .40 0.06⁎⁎⁎ .25
Conditional model 0.00 0.01 0.02 .00 0.17⁎.03
Reliability 0.79 0.81 0.63
Note: Female is a dichotomous variable (1 = female; 0 = male).
⁎p <.05.
⁎⁎ p <. 01.
⁎⁎⁎ p <. 001 (two-tailed tests).
Notes
1. Prior multivariate analyses contrasting fear and risk among
men and women had relied on focused samples and/or contexts. May
(2001) and May and Dunaway (2000) utilized an adolescent sample;
the latter study focused on victimization while at school. Lane (2002)
and Lane and Meeker (2003) restricted their analyses to gang crimes.
Fisher and Sloan (2003) used a sample of college students. LaGrange
and Ferraro (1989) limited their gender comparison to differences in
means.
2. Comprehensive treatments of fear of crime literature had been
provided by others (Ferraro & LaGrange, 1987; Flanagan &
Longmire, 1993; Garofalo, 1977; Hale, 1996).
3. The National Crime Victimization Survey asks respondents
“How safe do you feel being outside and alone in your neighborhood
at night (during the day)?”The General Social Survey asks “Is there
any area, right around here, that is, within a mile where you would be
afraid to walk alone at night?”
4. This may not be the case for a number of reasons. Residents
may be unaware of the actual prevalence of crime in their
neighborhood, evidenced in disproportionate fear-to-risk ratios
(Garofalo, 1981). Alternatively, citizens living in a high crime
neighborhood who accept the reality of their physical environment
may have a low fear of crime in relation to actual risk (Gilchrist et al.,
1998). The relationship between risk and fear has theoretical integrity
(Taylor, Shumaker, & Gottfredson, 1985), but it may be subject to
variation among neighborhoods and residents.
5. Demographic and economic indicators were based on 2000
U.S. Census data. Data on arrests and departmental characteristics
were obtained from the city police department.
6. Survey services were subcontracted; the research firm geo-
coded the community’s phone listings, facilitating the stratification
298 J.A. Schafer et al. / Journal of Criminal Justice 34 (2006) 285–301
based on patrol beat boundaries. On average, contact was attempted
with 235 residential phone numbers to complete one hundred
interviews; this rate was proportional with the experiences of others
engaged in large-scale telephone surveys (e.g., Chermak, McGarrell, &
Weiss, 2001; Weeks, Kulka, & Pierson, 1987; Williams & Nofziger,
2003). As the interviews progressed, respondents were disproportio-
nately men. The staff solicited female respondents where possible; men
who were willing to be interviewed were accepted in the absence of a
willing female.
7. Confirmatory factor analyses were conducted for each of the
dependent measures. Statistics for each of the dependent variables were
as following: perceived lack of safety (eigenvalue 1.48 factor > 0.86),
property victimization (eigenvalue 1.91 factor > 0.76), and personal
victimization (eigenvalue 2.14 factor > 0.87).
8. Due to limitations in the data, a continuous measure of
household income was not available; this measure was developed to
represent respondents with above average incomes. Further decon-
struction of non-Whites was precluded by the composition of
respondents. Only 20 percent of respondents were non-White; half of
these respondents were African American and the remainder was
Asian, Hispanic, or other races.
9. A range of conceptual and operational definitions had been used
in constructing “neighborhoods,”including: involving citizens in
defining areas by cuing respondents to consider “their neighborhood”
(Frank, Brandl, Worden, & Bynum, 1996) or using interviews to
identify defined neighborhoods (Skogan, 1990; Taylor, 1996); creating
aggregations using census tracts (Baumer, 2002; Bellair, 2000;
Morenoff, Sampson, & Raudenbush, 2001; Rountree & Warner,
1999; Sampson & Raudenbush, 2001; Skogan, 1990); or using police
beats (Vélez, 2001). The stratified nature of the sampling procedure
yielded data most representative of police beats.
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