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10.1177/0011128705285039CRIME & DELINQUENCY / MONTH XXXXFerguson, Mindel / FEAR OF CRIME IN DALLAS
Modeling Fear of Crime in Dallas Neighbor-
hoods: A Test of Social Capital Theory
Kristin M. Ferguson
Charles H. Mindel
Thisstudy tested a modelof theeffectsof differentpredictorson individuals’levelsof fear
of crime in Dallas neighborhoods. Given its dual focus on individual perceptions and
community-level interactions, social capitaltheory wasselected as themostappropriate
framework to explore fear of crime within the neighborhood milieu. A structural equa-
tionmodelwas developedand tested. Several positiveinfluencesof social capitalon low-eringfear were identified,including police presencein the neighborhood, social support
networks, neighborhood satisfaction, and collective efficacy. This study suggests that
social capital can be mobilized as a public safety, community resource in high-crime
neighborhoods.
Keywords: Social capital;fear of crime; perceivedrisk; incivility; neighborhoodsatis-
faction; collective efficacy
More than three decades have passed since the emergence of the first
studies exploring fear of crime in theUnited States(Baumer, 1979; Clemente
& Kleinman, 1977; Garofalo, 1979). Over time, three main areas have been
largely emphasized in the extant research. First, considerable evidence sup-
ports the influence of underlying demographic factors and the interactions
amongsocial structural correlates on levelsof fear. Generally, findingsreveal
that fear of crimetends to increasewith age, that womenexpresshigherlevels
of fear than men, and that non-Whites are more fearful of crime than Whites
1
KRISTIN M. FERGUSON, Ph.D.,is an assistant professorat the Schoolof Social Workat the
University of Southern California. She received her Ph.D. in international comparative social
welfare policy and social work in a binational, dual-degree program between the University of
Texas at Arlington and Universidad Autónoma de Nuevo León in Monterrey, Mexico. Her
research interests include homelessandstreet-livingyouth,social andspiritualcapital, outcomes
evaluation, and social development interventions with street youth. CHARLES H. MINDEL,
Ph.D.,is a professorof socialworkat theUniversityof Texas at Arlington. He hasbeen a faculty
memberat Universityof Texas at Arlingtonsince 1976. His primary research concerns in recent
years havebeen in thefields ofprogram evaluation.Recently, he hasalso been conducting major
evaluations of program effectiveness in the areasof community policing,gangprevention, juve-
nile offender services, substance abuse prevention, and in services to victims of domestic
violence.
CRIME & DELINQUENCY, Vol. 49 No. X, Month 2006 1-
DOI: 10.1177/0011128705285039
© 2006 Sage Publications
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(see Rountree & Land, 1996). Second, much of the existing work on thistopic has treated fear as both an emotional and cognitive response to crime-
related stimuli; nonetheless, several more recent studies have sought to dif-
ferentiate between fear of crime and perceived risk of crime as distinct out-
come variables. Whereas fear of crime refers to one’s emotional response to
crime-specific incidents, perceived risk of crime connotes one’s cognitive
assessment of surroundingcrime or victimization risk (Ferraro, 1995; Ferraro
& LaGrange, 1987; Rountree & Land, 1996). Finally, previous research has
also emphasized the relationship between macrolevel, communityvariables,
and residents’ levels of fear. A variety of structural factors involved in
explaining fear have been identified, including high community crime
indexes, high levels of racial and class segregation, high population density,
residential instability, low social cohesion, civic disengagement, and politi-
cal apathy (Pain, 2000; Rountree & Land, 1996; Sampson, 2001).
Identifying the individual, community, and structural correlates of fear of
crime has gained importance as Americans’ feelings of fear and anxiety
regarding their personal safetyhave increasedduringthepast decade. Never-
theless, reports from theFederal Bureauof Investigationat theendof thepast
decade show a decrease in criminal activity, specifically in violent criminal
acts, compared to previous years (May & Dunaway, 2000). This paradox
leads to speculation that fear of crime may largely be a result of individuals’
perceptions of latent influences present in the surrounding environment
rather than of manifest criminal activity in a particular community, per se. In
an effort to further explore the relationship between individual and commu-
nity factors and fear of crime, social science researchers have turned to the
notionof socialcapital,both as a possible explanationand asa potentialcom-munity-level resource that can be mobilizedto enhance neighborhood safety
(Bursik, 1988; Sampson, 2001). Despite disagreement in the existing litera-
ture as to how social capital is defined, authors concur that it consists of a set
of components found in social associations and interactions among people
that, when activated, empower individuals and facilitate cooperation toward
a mutual benefit. In essence, social capital refers to the social support net-
works, local institutions, shared norms of trust and reciprocity, and collec-
tive activities among community members to produce a common good
(Coleman, 1990; Putnam, 1993, 1995).
Thefear of crimeresearch has largely been driven by severalpredominant
theoretical frameworks, including social disorganization theory and diverse
criminological theories. Drawing from these conceptual frameworks, vari-
ous recent studies testing nonrecursive models have focused on the recipro-cal relationships among disorder, crime, fear, and neighborhood cohesion
(Bellair, 2000; Ferraro, 1995; Markowitz, Bellair, Liska, & Liu, 2001;
2 CRIME & DELINQUENCY / MONTH XXXX
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Sampson, Raudenbush, & Earls, 1997). To date, however, empirical studiesassessing the relevance of social capital theory to fear of crime are scarce
within the literature. Thus, the purpose of this study is to test a hypothesized
structural equation model that builds on the work of Ferraro (1995) to deter-
mine whether the social capital theoretical framework can enhance our
understanding of the interrelationship among the key components of social
capital andcitizens’levels of fear of crime.Both thesocialcapital and fear of
crimebodiesof literature haveguided theselectionof allpredictors of fear of
crimeaswell as thecausal directionof thehypothesized model.Furthermore,
given that much of theextant research has failed to distinguish between gen-
eral, cognitive fear (i.e., perceived risk) and offense-specific, emotionally
based fear, this study assesses the relationship of the proposed correlates on
each concept separately. The key components of social capital are discussed
later in relation to fear of crime.
REVIEW OF SOCIAL CAPITAL AND
FEAR OF CRIME LITERATURE
Victimization
Review of theexisting literature on therelationshipbetween prior victim-
ization and fear of crime uncoversmixed results. Multiple studies have dem-
onstrated that prior victimization of criminal activity has a positive effect on
fear of crime (see May & Dunaway, 2000). Directly experiencing or indi-
rectly witnessing a victimization experience in one’s own neighborhood canaugment an individual’s level of anxiety, as the criminal activity has become
a real andmanifest event in thevictim’s psyche rather than a mere imagepro-
jected by the media or other symbol of crime present in the neighborhood,
such as graffiti or vandalism (Johnston, 2001). Other researchers, however,
have identified a weak relationship (Garofalo, 1979). Moreover, still others
have focused on the fear-victimization paradox, which describes the finding
that although the actual rates of victimization are higher for males than
females, females tend to experience higher levels of fear of victimization
(Ferraro, 1996; Warr, 1985). Whereas this inevitably suggests that fear of
crime may be much more a result of subjective experiences than objective
ones, research shows that women are overrepresented in certain types of
crimes(i.e.,domesticviolence,sexual assault, andharassment) that areoften
underreported to both authorities and in surveys (Sacco, 1990). Stafford andGalle (1984) purport that because of this reporting bias, victimization levels
in women may not be as low as previously suggested.
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ambiguity regarding the effects of social networks on fear of crime, it ishypothesized that high levels of social support will negatively influence fear
of crime, given the supportive role of social networks within the literature.
Collective Efficacy
The notion of collective efficacy refers to the shared expectations and
mutual civic engagement by community members in local social control,
with an emphasis on residents’joint capacity to act together to generate solu-
tions to local problems (Sampson, 2001).Examples of communitysafetyini-
tiatives grounded in principles of collective efficacy include Neighborhood
Watch and public forums, in which community problems are discussed and
locally driven solutions are generated. Given the variety of personal and col-
lective safetymechanisms to which individuals canbe exposed via their par-
ticipation in community activities with other residents, collective efficacy is
hypothesized to positively influenceadopting preventive measures to protect
oneself from crime.
Although many community-based, public-safety efforts aim to combat
crimeandreducecitizens’levels of fear of crime,considerableempiricalevi-
dence indicates that the relationship between collective efficacy and fear of
crime is not as clear as one may believe. Whereas some studies demonstrate
the effectiveness of programs such as Neighborhood Watch in achieving
their intended effects of lowering neighborhood fear of crime, other studies
show that crime vigilancegroups caninadvertently increase thelevels of fear
of crime in a given neighborhood (see Rosenbaum, 1987, for a complete
review). Collective efficacy can, however, have an indirect effect on fear of crime—that is, through a third mediatingvariable. Evidence does exist in the
literature linking collective efficacy to increased neighborhood satisfaction,
which in turn, canlower fear of crime(Silverman & Della-Giustina,2001). It
is thus hypothesized that high collective efficacy will have a positive impact
on neighborhood satisfaction.
Neighborhood Satisfaction
An individual’s perceived level of neighborhood satisfaction is another
predictor of fear of crime substantiated by prior research. Assessing the
effects of neighborhood satisfaction on fear of crime with the elderly,
McCoy, Wooldredge, Cullen, Dubeck, and Browning (1996) discovered
that an individual’s overall level of dissatisfaction with the surroundingneighborhood was the best predictor of fear of crime. Likewise, in a study
exploring fear of crime in women residing in publichousing, Alvi, Schwartz,
6 CRIME & DELINQUENCY / MONTH XXXX
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DeKeseredy, and Maume (2001) found that next to neighborhood disorder,neighborhood dissatisfaction had the strongest effect on fear of crime. As a
subjective measure of neighborhood quality, overall satisfaction (or dissatis-
faction) reflects residents’ perceptions of the extent to which other more
objective measures, such as symbols of crime, visible incivilities and neigh-
borhood disorder succeed in provoking fear in them. As such, in modeling
fear of crime, inclusion of neighborhood satisfaction as a mediatingvariable
between perceived neighborhood disorder and both perceived risk of crime
and fear of crime can facilitate a better understanding as to why residents
sharing thesameenvironmental and structural conditions canhave vastlydif-
ferent levels of cognitive and emotional fear of crime (Alvi et al., 2001;
Silverman & Della-Giustina, 2001). In light of existing findings, high levels
of neighborhood satisfaction are hypothesized to lead to low levels of
perceived risk of crime and fear of crime.
Perceived Risk of Crime
Prior researchsuggests thatan individual’s perceived level of riskof crime
in the neighborhood is a strong predictor of emotional fear. Earlier studies
tended to focus on the relationship between sociodemographic variables
(e.g., gender, race, age, etc.) and perceived risk. Findings generally reveal
that both women and the elderly report higher perceived risk of crime
(Baumer,1979;Garofalo,1979). More recentworkhasfocusedon conceptu-
ally distinguishing between cognitive perceptions of risk and actual fear of
crime (Ferraro, 1995; LaGrange et al., 1992; Rountree & Land, 1996; Warr,
1987). Findings from these studies suggest that risk perception is an impor-tant predictor of fear of crime and that risk and fear constitute distinct con-
cepts. Likewise, Ferraro (1995) found perceived risk to be the strongest pre-
dictor of fear of crime as well as a moderate predictor of efforts to protect
oneself from crime.In light of these findings, high levels of perceived risk are
hypothesized to lead to both increased preventive and protective measures
and high levels of fear of crime.
Preventive and Protective Measures
From a citizens’ perspective, by acting in tandem with community-polic-
ing efforts and other community members to reduce crime, local residents
can also assume a proactive stance in addressing and lowering their levels of
fear of crime. Several authors refer to this notion as active citizenship, inwhichcommunityresidentsundertakesecurity strategies to reduce theiranx-
iety toward crime (Johnston, 2001; Vacha & McLaughlin, 2000). Examples
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of individual, proactive securityactivities consist of buying a large watchdog
to guard one’s home andproperty, carrying a self-defenseweapon, or install-
ing extra security devices in one’s home, such as burglar alarms. As such,
adopting increasedmeasuresto protect oneself is anticipated tobe associated
with lower levels of fear of crime.
HYPOTHESIZED MODEL
Coalescing the oft-cited correlates of fear of crime, Figure 1 displays the
structural portion of the hypothesized full structural equation model of citi-
zens’levelsof fear of crime.The causal ordering and specific hypotheses are
based on social capital theory and empirical precedents of fear of crime.
METHODS
Data and Sample
Thedataused in thepresent study originatefroma researchproject assess-
ingcrimein Dallasneighborhoods, which beganin 1995 andwas fundedby a
National Institute of Justice community-policing grant (# 95-IJ-CX-0070).
Respondents for the survey were sampled using random telephone numbers
8 CRIME & DELINQUENCY / MONTH XXXX
Incivility
Police Presencein Nghbrhd
Preventive / ProtectiveMeasures
Fear of Crime
Social SupportNetworks
CollectiveEfficacy
Gender
Family
Income
Age
Race
+
PerceivedRisk of Crime
Victimization
+
+
_
_
+
+
+
+
+
_
_
+
+
+
+
+
_
_
+
+
+
_
_
+
+
+
NeighborhoodSatisfaction
+
_
_
+
_
+
_
_
_
+
++
_
Figure 1: Hypothesized Structural Model
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purchased from a commercial survey sampling company. The phone num-bers were selected by useof therandom digit dialing method. Eligiblepartic-
ipantswere residentsof Dallasolderthantheage of 18years. Interviewswere
conducted by college students who were trained and supervised by project
staff. Calls were conducted primarily in the evenings and on weekends dur-
ing theday to avoidoversampling persons who typically stay at home during
the day. The telephone survey was conducted between March and May of
1996. Individuals were called back up to four times to minimize non-
response. The first person who qualified for the survey was interviewed.
Thesampleconsisted of 1,367 respondents with a response rate of 33.4%.
The mean age was 42.7 years (SD = 17.2 years). Approximately 59% were
female; 45% were married, and 60% were White, not of Hispanic origin.
Additionally, 63% of the respondents held more than a high school degree;
59% wereemployed fulltimeand 52% had a total household incomeof more
than $40,000 for all sources before taxes in 1995.
Although it was impossible to determinewhether the nonrespondents dif-
fered substantially from the respondents, we were able to compare the char-
acteristicsof thesampleto U.S. Censuspopulation data for thecity of Dallas.
Our sample somewhat approximates the population of Dallas residents
across several key predictors but slightly overrepresents females, Whites,
and educated individuals. Roughly 50% of the 1,188,580 Dallas city resi-
dents were female; 49% were married, and 53% were White, not of Hispanic
origin. Furthermore, 51% had attained more than a high school degree, and
47% of Dallas residents had a total household incomeof $40,000 or more for
allsourcesbefore taxes in1999 (U.S. CensusBureau,2000a,2000b,2000c).
Measures
Victimization. Prior victimization refers to whether neighborhood resi-
dentswere exposed toa range of possibleattemptedcriminal activities,either
successful or unsuccessful. Each variable was dichotomous in nature, with 1
referring to a positive victimization experience or attempt. Using confirma-
tory factor analysis (CFA), 4 indicators were selected from the original 15
items. Factor loadings ranged from .40 to .58 (see Table 1).
Perceived neighborhood incivility. Perceived neighborhood incivility con-
notes theextent towhich residentsconsidera variety of signs of visible disor-
der (e.g., vacant lots, abandoned cars andbuildings, graffiti, publicdrinking,
loitering, truancy, andcriminal activity) to be problems in theneighborhood.
In the present study, this predictor of fear of crime is operationalized by the
sum score of the 18 total items of a social disorder scale. High values on this
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10
T A B L E 1 :
D e
f i n i t i o n s o
f M e a s u r e s a n
d D e s c r i p
t i o
n s o
f M o
d e
l V a r i a
b l e s
f o r
V i c t i m i z a
t i o n ,
I n c
i v i l i t y
, a n
d P o
l i c e
P r e s e n c e
C r o n b a c h
F a c t o r
V a r i a b l e L a b e l
O p
e r a t i o n a l D e f i n i t i o n ( S u r v e y I t e m )
R a n g e
M
S D
A l p h a ( α )
V i c t i m i z a t i o n
P h y s i c a l a t t a c k
H a s a n y o n e p h y s i c a l l y a t t a c k e d y o u o r a c t u a l l y
0 - 1
0 . 0 4 0
0 . 1 9 8
b e e n v i o l e n t w i t h y o u i n a n a r g u m e n t o r f i g h t ?
P h y s i c a l t h r e a t e n
I n t h e p a s t y e a r , h a s a n y o n e t h r e a t e n e d o r t r i e d
0 - 1
0 . 0 6 0
0 . 2 4 1
t o h u r t y o u e v e n t h o u g h t h e y d i d n o t a c t u a l l y h u r t y o u ?
T h e f t s e l f
H a s a n y o n e t r i e d t o s t e a l s o m e t h i n g f r o m y o u
0 - 1
0 . 0 2 0
0 . 1 4 6
f o r c e f u l l y
e v e n t h o u g h t h e y d i d n o t g e t i t ?
S e x u a l a t t a c k
H a s a n y o n e s e x u a l l y a t t a c k e d y o u o r t r i e d t o ?
0 - 1
0 . 0 1 0
0 . 1 1 5
N e i g h b o r h
o o d
S u m p r o b l e m s
C o m p o s i t e s c o r e o f r e s p o n s e s o f b i g p r o b l e m , s o m e
5 - 5 4
2 4 . 6 9 1
8 . 1 5 3
. 9 4
i n c i v i l i t y
p r o b l e m ,
o r n o p r o b l e m t o l i s t o f 1 8 n e i g h b o r h o o d
p r o b l e m s
( e . g . , v a c a n t l o t s , a b a n d o n e d c a r s ,
a b a n d o n
e d h o u s e s , g r a f f i t i , p u b l i c d r i n k i n g , t r u a n c y ,
d r u g d e a
l i n g , e t c . )
P o l i c e p r e s e n c e
P o l i c e g i v e t i c k e t s
D u r i n g t h e
p a s t m o n t h , h o w o f t e n h a v e y o u s e e n . . .
0 - 2
0 . 9 0 4
0 . 7 6 9
i n n e i g h b
o r h o o d
a p o l i c e o f f i c e r p u l l s o m e o n e o v e r f o r a t r a f f i c t i c k e t
i n y o u r n e i g h b o r h o o d ?
P o l i c e c h e c k a l l e y s
A p o l i c e o f f i c e r p a t r o l l i n g i n t h e a l l e y o r c h e c k i n g
0 - 2
0 . 3 9 9
0 . 6 5 9
g a r a g e s
o r i n t h e b a c k o f b u i l d i n g s ?
P o l i c e f r i s k
A p o l i c e o f f i c e r s e a r c h i n g o r f r i s k i n g a n y o n e h e r e i n
0 - 2
0 . 3 4 3
0 . 5 9 6
t h e n e i g h
b o r h o o d o r b r e a k i n g u p g r o u p s o r
a r r e s t i n g
a n y o n e ?
P o l i c e t a l k f r i e n d l y
A p o l i c e o f f i c e r c h a t t i n g o r h a v i n g a f r i e n d l y
0 - 2
0 . 5 0 9
0 . 6 9 1
c o n v e r s a
t i o n w i t h p e o p l e i n t h e n e i g h b o r h o o d ?
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composite variable reflect high levels of perceived neighborhood incivility,as reported by neighborhoodresidents. Allitems from theoriginalscalewere
used, given that CFA produced moderate to high factor loadings, ranging
from .59 to .77. Also, the Cronbach’s alpha for the 18 indicators was well
above conventional standards at α = .94 (see Table 1).
Police presence. The concept of police presence in the neighborhood is
defined as the frequency of occasions in which residents have seen a police
officer or officers in the neighborhood. Police presence in the neighborhood
is operationalized by four indicators, which were selected from the original
seven survey items on the basis of construct validity. CFA produced factor
loadings that ranged from .54 to .69. Responses were scored so that higher
values represent greater police presence in the neighborhood (see Table 1).
Social support networks. Social networks connote the social relationships
between residentsin a communityandtheresulting socialsupport that canbe
derived from these interactions. In the original survey, four items measured
residents’ supportive networks. CFA results reveal that twoof these hadhigh
factor loadings (.77 and .78), whereas the other two had low loadings. The
two indicators with high loadings were selected for this study and refer to
whether neighbors asked each other to watch their homes for them while
away. Higher values on the survey items are associated with higher levels of
social support networks (see Table 2).
Collective efficacy. Collective efficacy is defined as the civic engagement
activities performed by community members in an effort to solve local prob-lems. Four items from the original survey measured collective efficacy. CFA
findings indicate that two of these variables had moderate loadings of .55,
whereas the other loadings were less than the accepted cut-off of .40. In the
present study, collective efficacy assesses residents’ participation in collec-
tive-action initiatives, such as Neighborhood Watch and community meet-
ings. Higher scores are associated with increased collective efficacy in the
neighborhood (see Table 2).
Neighborhoodsatisfaction. Neighborhood satisfactionrefers to theextent
to whichresidents feelpositively about their surrounding neighborhood con-
text. This single-item indicator is defined as subjects’responses to the ques-
tion “On the whole, how do you feel about your neighborhood as a place to
live?”Highscores indicatea greater degree of overallneighborhood satisfac-tion (see Table 2).
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12
T A B L E 2 :
D e
f i n i t i o n s o
f M e a s u r e s a n
d D e s c r i p
t i o
n s o
f M o
d e
l V a r i a
b l e s
f o r
S o c
i a l S u p p o r t ,
C o
l l e c
t i v e
E f f i c a c y ,
S a
t i s f a c
t i o n ,
a n
d P e r -
c e
i v e
d R i s k
C r o n b a c h
F a c t o r
V a r i a b l e L a b e l
O p
e r a t i o n a l D e f i n i t i o n ( S u r v e y I t e m )
R a n g e
M
S D
A l p h a ( α )
S o c i a l s u p
p o r t
Y o u r h o m e
P l e a s e t h i n k
a b o u t t h e l a s t t i m e w h e n n o o n e w a s h o m e
0 - 1
0 . 5 5 0
. 4 9 8
n e t w o r k s
f o r a t l e a s t
a d a y o r t w o . D i d y o u a s k a n e i g h b o r t o
w a t c h y o u r
h o m e ?
N e i g h b o r h o m e
I n t h e p a s t y
e a r , h a v e a n y o f y o u r n e i g h b o r s a s k e d y o u
0 - 1
0 . 5 4 0
. 4 9 8
t o w a t c h t h
e i r h o m e ?
C o l l e c t i v e
e f f i c a c y
N e i g h b o r w a t c h
H a v e y o u e v
e r p a r t i c i p a t e d i n n e i g h b o r h o o d w a t c h ?
0 - 1
0 . 3 2 0
. 4 6 7
A t t e n d M e e t i n g s
H a v e y o u b e
e n a b l e t o a t t e n d a n y c o m m u n i t y m e e t i n g s
h e l d i n y o u
r n e i g h b o r h o o d ?
0 - 1
0 . 3 1 0
. 4 6 2
N e i g h b o r h
o o d
P l a c e t o l i v e
O n t h e w h o l e , h o w d o y o u f e e l a b o u t y o u r n e i g h b o r h o o d
1 - 4
3 . 2 5 9
. 8 2 9
s a t i s f a c t i o n
a s a p l a c e
t o l i v e ?
P e r c e i v e d
r i s k
P e r c e i v e d r i s k
H o w o f t e n d
o e s w o r r y a b o u t c r i m e p r e v e n t y o u f r o m
1 - 4
2 . 0 7 7
. 9 8 3
o f c r i m e
d o i n g t h i n g
s t h a t y o u w o u l d l i k e t o d o i n y o u r
n e i g h b o r h o
o d ?
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Perceived risk of crime. Perceived risk of crime denotes a general,cognitively based assessment of surrounding risk in the neighborhood. The
single-item indicator assesses how often the individual’s worryprevents him
or her from going out in the neighborhood. Responses were measured on a 4-
point ordinal scale ranging from never to very often. High values reflect
higher levels of perceived risk (see Table 2).
Preventive and protective measures. Preventive and protective measures
refer to theindividualsecuritystrategiesresidents mayadoptto protect them-
selves and their homes from crime in the neighborhood. The factor is
operationalized by twocompositevariables, whichwerecreatedby summing
the original survey items for each respective category. High values reflect a
greater amount of safety and security measures undertaken by residents to
protect themselves and their homes. Despite the modest reliability coeffi-
cients for the composite variables, the original indicators were used for each
variableon thebasisof content validity, given that each item represents a spe-
cific type of safetymeasure that residents cancarry out to protect themselves
and their homes (see Table 3).
Fear of crime. Fear of crime constitutes a measure of the crime-specific,
emotionallybased fear of crime.It isoperationalizedby four of thefive origi-
nal variables, each measured on a 4-point ordinal scale, which seek to deter-
minehowoftenindividuals think aboutbeingvictimized,whether personally
or through property theft. High values are associated with higher levels of
emotional fear. CFA produced factor loadings that ranged from .72 to .77.
Onevariablefrom theoriginal survey wasdropped because of a substantiallylower factor score (see Table 3).
Control variables. Gender constituted a nominal, dichotomous variable,
consisting of 0 (males) and 1 ( females). Race was a nominal, dichotomous
variable represented by 0 (non-Whites) and 1 (Whites). Age was a ratio-level
variablemeasured at the time of the survey. Last, family income was an ordi-
nal-level variable ranging from 0 (< $10,000) to 6 (> $100,000). It was
defined as the total household income for all sources before taxes in 1995
(see Table 3).
RESULTS
Given that structural equation modeling is a multistage process, CFA was
firstused to determinewhether themeasuredvariables were considered to be
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valid indicators of their underlying constructs (Bollen, 1989). Factor load-
ingsfor all indicatorsin theoveridentifiedmeasurement models weremoder-
ate to high and all loadings were statistically significant, lending credence to
the convergent validity of the indicators (Hatcher, 1994).
Structural Model Estimates for Fear of Crime
Themeasurementmodels were combined into a full latentvariable model,
and a structural equation analysis was conducted using the AMOS 5.0 pro-
gram and the maximum likelihood estimation method. Missing data were
handled using AMOS’s Full Information Maximum Likelihood method of
estimation (Arbuckle, 1996). Thefull latentmodeldisplayed inFigure2 pro-
duced a testable, overidentified model with 250 degrees of freedom.
Table 4 presents thegoodness-of-fit estimatesfor thefull model.Basedon
the measures of overall fit, there is evidence that the hypothesized model of
fear of crime,derived from socialcapital theory, is a good-fittingmodel.The
Comparative Fit Index was above the acceptable cut-off value, at .918(desiredvalues = .90or higher). Theroot mean squareerrorof approximation
(RMSEA) of .039 is reflective of a good-fitting model (desired value < .05).
Ferguson, Mindel / FEAR OF CRIME IN DALLAS 15
Incivility
Police Presencein Nghbrhd
Preventive / ProtectiveMeasures
Fear of Crime
Robbed
Protecthome
Protectself
Police givetickets
Police checkalleys
Policefrisk
Police talkfriendly
Social SupportNetworks
Yourhome
Nghbrhome
CollectiveEfficacy
Attendmtgs
Nghbrwatch
Gender
FamilyIncome
Age
1
Race
1
Homevandalized
PerceivedRisk of Crime
Victimization
Physicalattack
Physicalthreaten
Theftself
Sexualattack
0.173
Afraid athome
Crimevictim
1
1
0.135
0.361
1
10.243
-0.227
-0.117
0.229
0.152
-0.082
0.439
-0.163
0.472
0.526
0.252
0.072
0.098
NeighborhoodSatisfaction
0.131
0.234
-0.105
0.162
0.069
0.114
0.213
0.197
-0.141
-0.127
0.1650.081
-0.29
-0.19
0.163
Figure 2: Full Model of Fear of CrimeNOTE: An earlier iteration was run of the hypothesized full model, as depicted in Fig-ure 1. Nonsignificant paths were dropped. The final full model displays only significantpaths.
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The relativelynarrow confidence intervalranging from.036 to .042 indicates
a high degree of precision (Byrne, 2001). As such, one can be 90% confident
that the true RMSEA value in the population is located within the range of
.036 and.042. Theoverall R2 forfear of crimewas0.508. Thus, the12 predic-
torvariables in this modelaccount for51% of thevariance in fear ofcrime.
With respect to the individually hypothesized relationships among the
variablesin themodel, the initial speculationswerefairly accurate.Standard-
ized regression coefficients are listed in Table 5 and explained more fully
later on by predictor variables.
Victimization
As hypothesized, victimization was found to have a significant and posi-
tive impact on adopting preventive and protective measures (β = .114, p <
.01), on police presence in the neighborhood (β = .213, p < .001), on per-
ceived risk of crime (β = .165, p < .001), and on fear of crime (β = .081, p <
.05). The effects of victimization on these four variables are consistent with
extant literature (Johnston, 2001; May & Dunaway, 2000; Silverman &
Della-Giustina, 2001). It is interesting to note that one’s prior victimization
had a stronger effect on perceived risk than on actual emotional levels of
fear—that is, having experienced prior victimization was more strongly
related to perceived cognitive risk of crime than to actually experiencing
emotional fear of crime. Victimization also had a significant and negative
effect on neighborhood satisfaction (β = –.127, p < .001). The negative rela-
16 CRIME & DELINQUENCY / MONTH XXXX
TABLE 4: Overall Goodness-of-Fit Estimates for Modeling Fear of Crime
Fit Index Estimate
Overall chi square 768.0
Degrees of freedom 250
Significance 0.001
Number of parameters 100
Discrepancy/degrees of freedom 3.01
Comparative Fit Index .918
Normed Fit Index .884
Relative Fit Index .849
Incremental Fit Index .919
Tucker-Lewis Index .893
Root Mean Square Error of Approximation (RMSEA) .039
RMSEA lower bound .036
RMSEA upper bound .042Overall R
2(for fear of crime) .508
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17
T A B L E 5 :
S t a n
d a r d
i z e
d R e g r e s s
i o n
C o e
f f i c i e n
t s i n M o
d e
l
P r e d i c t o r
P o
l i c e
S o c i a l
C o l l e c t i v e
N e i g h b o r h
o o d
P e r c e i v e d
P r e v e n t i v e
F e a r o f
V a r i a b l e s
V i c t i m i z a t i o n
I n c i v i l i t y
P r e s
e n c e
S u p p o r t
E f f i c a c y
S a t i s f a c t i o n
R i s k
M e a s u r e s
C r i m e
V i c t i m i z a t i o n
. 2
1 3 * * *
– . 1 2 7 * * *
. 1 6 5 * * *
. 1 1 4 * *
. 0 8 1 *
I n c i v i l i t y
. 2 5 2 * * *
. 2
2 9 * * *
– . 2 9 0 * * *
. 1 6 2 * * *
. 0 6 9 * *
P o l i c e p r e s e n c e
. 1 3 5 * *
S o c i a l s u p
p o r t
. 4 7 2 * * *
. 1 7 3 * *
C o l l e c t i v e
e f f i c a c y
. 1 9 7 * * *
. 4 3 9 * * *
N e i g h b o r h
o o d
– . 1 9 0 * * *
– . 1 4 1 * * *
s a t i s f a c t i o n
P e r c e i v e d
r i s k
. 1 3 1 * * *
. 5 2 6 * * *
P r e v e n t i v e
m e a s u r e s
. 2 3 4 * * *
G e n d e r
. 1 6 3 * * *
. 0 7 2 * *
R a c e
– . 0 8 2 * *
. 1 5 2 * * *
– . 1 6 3 * * *
A g e
– . 1 1 7 * *
– . 2 2 7 * * *
. 2 4 3 * * *
. 0 9 8 * * *
F a m i l y i n c o m e
– . 1 0 5 * *
. 3 6 1 * * *
R 2
. 0 9 1
. 0 7 4
. 1
2 5
. 0 8 2
. 3 7 9
. 1 6 4
. 1 9 5
. 3 2 2
. 5 0 8
* p < 0 . 0 5 .
* * p < 0 . 0 1 . * * * p < 0 . 0 0 1 .
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2001; McCoy et al., 1996; Silverman & Della-Giustina, 2001). The findingshere reveal that neighborhood satisfaction had a stronger impact on per-
ceived risk than on emotional levels of fear of crime. This relationship is
novel within the literature, as prior work assessing the effects of neigh-
borhood satisfaction on fear of crime has not conceptually distinguished
between perceived risk and fear of crime.
Perceived Risk of Crime
Perceived risk of crime was found to have a significant and positive effect
on adopting preventive and protective measures and on fear of crime. The
standardized path coefficients were .131 ( p < .001) and .526 ( p < .001),
respectively. Both relationships are consistent with prior findings by Ferraro
(1995) that perceived risk is the strongest predictor of fear of crime and that
the higher one’s levels of perceived risk, the more likely one is to constrain
behavior to protect oneself from crime and victimization.
Preventive and Protective Measures
Contrary to the negative relationship initially hypothesized between pro-
tective measures and fear of crime, adopting preventive and protective mea-
sures actuallyhada significant andpositiveeffect on fear of crime.Thus, car-
rying out increased measures to protect oneself was associated with higher
levels of fear of crime. The standardized structural coefficient had a value of
.234 ( p < .001) and was the second strongest effect on fear of crime of all
those depicted by the model. Nonetheless, the cross-sectional nature of thedata used here limit our ability to causally interpret this finding, given the
absence of any measure of residents’ fear prior to implementing preventive
measures designed to lower their fear. This possibility for the lack of support
of our initial hypothesis will be further discussed in the final section.
Control Variables
Various sociodemographic controls were found to be correlates of fear of
crime in this study. Namely, women were significantly more likely than men
to have higher levels of perceived risk (β = .163, p < .001) and fear of crime
(β = .072, p < .01). Non-Whites had higher levels of perceived risk of crime
(β= –.163, p < .001),buttherewasno significant relationshipfound between
race and fear of crime.Non-Whites also perceivedgreater amounts of neigh-borhood incivility than Whites (β = –.082, p < .01). Older individuals were
less likely than younger people to be victimized (β = –.117, p < .01) and per-
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perceived risk and on fear of crime than both the more objective measures of neighborhood disorder (e.g., litter, vandalism, abandoned infrastructure,
graffiti, gangs, etc.)and the traditional indicators of social vulnerability (e.g.,
being femaleand beingelderly).Nonetheless, inaneffort toreduceresidents’
fear of crime, neighborhood vigilance efforts, crime prevention programs
and public-safety campaigns generally tend to focus on eradicating the visi-
ble incivilities in the neighborhood as well as on decreasing residents’likeli-
hood of being victimized, instead of on enhancing residents’ levels of overall
satisfaction directly (Rosenbaum, 1987). With multiple studies demonstrat-
ing the strength of the variable of overall neighborhood satisfaction as a pre-
dictor of fear of crime, it may behoove local community associations and
police-neighborhood partnerships to begin to implement strategies that seek
to enhance neighborhood satisfaction,alongwith the more traditional efforts
employed to lower fear of crime.
The findings linking overall satisfaction to lower levels of fear of crime
are consistent with the propositions of social capital theory, which suggest
that residents who are more satisfied with their neighborhoods tend to dis-
play more interpersonal trust with neighbors, thus fearing less the likelihood
of themselves becoming victims of crime (Sampson, 2001). Similarly, the
positive relationship between collective efficacy and neighborhood satisfac-
tion supports social capital theory as well, which proposes that individuals
who actively participate in efforts to enhance neighborhood well-being tend
to be more satisfied overall with their surrounding milieu (Portney & Berry,
1997; Putnam, 2000). In this study, the moreneighborhood residentspartici-
pated in Neighborhood Watch and community meetings, the more satisfied
citizens were, overall, with their surrounding environment.Second, local institutions of social control, such as the police, can have a
motivating effect on residents to collectively work together to lower neigh-
borhood crime rates and accompanying levels of fear. The indirect effect of
police presence in the neighborhood on lowering citizens’ perceived risk of
crime (–.005) and on lowering fear of crime (–.004), mediated through high
collective efficacy and high neighborhood satisfaction, is particularly prom-
ising forfuture collaboration between residentsand law enforcement incom-
bating neighborhood crime. Consistent with the literature on social capital,
neighborhoods with a diverse stock of institutional resources (e.g., police,
schools, churches, and service organizations) are more likely to successfully
enforce social norms of desired behavior and to provide citizens with oppor-
tunities to participate in local organizations on behalfof thecollective neigh-
borhood well-being (Sampson, 2001). One of the positive externalities of high levels of collective efficacy and overall satisfaction, as seen above, is a
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hypothesis, discovering that increasedprecautionary measureswereactuallyassociated with higher levels of fear of crime.
Given the presence of paradoxical findings in the literature related to sev-
eral adopting preventive and protective measures, it is speculated that the
nature of the recursive model used here may have limited our understanding
of the true relationship between preventive and protective measures and fear
of crime. In the absence of a nonrecursive model to evaluate the possible
reciprocal relationship between fear of crime and adopting preventive and
protective measures, the direction of causality remains questionable. In an
effort toassessthe possible reciprocal effectsbetween these twovariables,an
additionalpath from fear of crimeback topreventiveandprotectivemeasures
was added in a second nonrecursive model. Although the path was non-
significant here, future studies using a nonrecursive model will likelyhelp to
answer an important question: Does fear of crime incite individuals to adopt
increased security measures to protect themselves from crime or does
employing these tactics to protect oneself from crime lead to increases (or
decreases) in levels of anxiety, or both?
Theseexplanations, along with thefindings, shouldbe taken with caution,
considering the study’s limitations. First of all, although we have based the
temporalordering of ourmodelpredictorsonsocialcapital theoryand empir-
ical precedents of fear of crime,caution shouldbe used in inferringcausation
from the results, given the cross-sectional nature of the data. For instance,
regarding the unanticipated finding between preventive and protective mea-
sures and fear of crime, the absence of any measure of residents’fear prior to
implementing safety measures limits our ability to determinewhat residents’
preimplementation levels of fear were. As such, residents’ levels of fear of crimemayhavebeen reduced on thehomeandpersonalsafety measuresused
here, yet their initial fear levels mayhave been much higher—a measure that
was not captured in these data. Future longitudinal analysis will help to clar-
ify the relationship between adopting safety measures and fear of crime as
well as to address this common limitation of cross-sectional data.
Secondly, caution must also be taken in the interpretation of results
regarding the relationship between the sociodemographic correlates and fear
of crime because of the sample bias detected here. In this study, women were
more likely than mento have higher levels of perceived risk of crime andfear
of crime, whereas minorities were more likely than Whites to have higher
levels of perceived risk. Given that this sample somewhat overrepresents
both females and Whites, it is possible that the findingswould have been dif-
ferent with a more representative sample. Future studies using samples thatare representative of the national population across key variables or that
weight nonrepresentative data and conduct multiple analyses to compare
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