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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



(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



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.

Ferguson, Mindel / FEAR OF CRIME IN DALLAS 3



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,




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




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


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



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




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 ?



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).




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 ?



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




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).


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.



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-


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



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


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


. 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 .



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-




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




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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