A Multilevel Analysis of the Vulnerability Disorder and Social Integration Models of Fear of Crime

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A Multilevel Analysis of the Vulnerability, Disorder,

and Social Integration Models of Fear of Crime

Travis W. Franklin Cortney A. FranklinNoelle E. Fearn

Published online: 17 June 2008 Springer Science+Business Media, LLC 2008

Abstract The current research tests three conceptual models designed to explain

citizens fear of crimevulnerability, disorder, and social integration. These

models are assessed for differential impact across the cognitive and affective

dimensions of fear of crime. The analysis reported here considers the consecutive

and simultaneous influence of individual- and city-level factors using multilevel

modeling techniques. Recently collected survey data for 2,599 citizens nested

within 21 cities across Washington State provide the empirical evidence for theanalysis. Results indicate that the disorder model is best able to explain variation in

both the cognitive and affective dimensions of citizens fear of crime across cities.

The vulnerability and social integration models explain significantly less variation.

Further, the vulnerability model lacks directional consistency across the observed

dimensions of fear. Societal implications of the research findings are discussed.

Keywords Fear of crime Victimization Multilevel analysis

Vulnerability Disorder Social integration

Fear of crime has been recognized as a significant social problem, affecting the quality

of life across various demographic and socio-economic conditions. Attempts to

understand the dynamics underlying the fear of crime have led to numerous empirical

and theoretical developments. Three dominant models have emerged as possible

explanations of variation in fear of crime among citizensthe vulnerability, disorder,

and social integration models. While each of these models has received some

T. W. Franklin C. A. FranklinCollege of Criminal Justice, Sam Houston State University, Huntsville, TX, USA

Soc Just Res (2008) 21:204227DOI 10.1007/s11211-008-0069-9


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empirical support in the literature (see Hale, 1996for a comprehensive review), no

studies have compared their explanatory power simultaneously in the context of a

multilevel analysis,1 and to the best of our knowledge no studies have done so in a

predominantly rural setting. As Taylor (2002, p. 774) makes clear, prior research on

the fear of crime is based largely on data from well-developed, relatively large cities,raising the need to investigate the determinants of fear in cities whose crime problems

may be qualitatively and quantitatively different from those in big cities.

An equally pressing concern in this area of research surrounds the operational

definitions of fear of crime found within the existing literature. There has been little

consensus regarding the most accurate way to capture fear of crime (Ferraro &

LaGrange, 1987; Hale, 1996), and some researchers have made use of measures

lacking clear and reliable face validity. Early studies often employed a measure that

was subsequently identified as more appropriate for capturing perceptions of risk

rather than the emotion of fear (e.g., Baumer, 1985; Kennedy & Krahn, 1984;Maxfield,1984; Yin,1982). While this research reveals less about feelings of fear,

its usefulness has persisted in light of the multidimensional nature of the construct.

In this sense, fear of crime is conceptualized as reflecting three related dimensions:

cognitive, affective, and behavioral (Fattah & Sacco, 1989). The cognitive

dimension involves a rational thought process whereby perceptions of risk are

developed; the affective dimension recognizes emotions associated with fear; and

the behavioral dimension captures physical responses to the situation at hand. Given

the multidimensionality of fear, it is important for researchers to distinguish

between and draw comparisons across the various dimensions.The purpose of the current study is to compare the efficacy of the vulnerability,

disorder, and social integration models across the cognitive and affective

dimensions of fear of crime.2 To make these comparisons, the analysis reported

here considers the simultaneous impact of individual- and city-level factors using

multilevel statistical modeling techniques. Such an analysis will help to clarify our

theoretical and empirical understanding of the three hypothesized models and shed

light on their differential impact (assessed by the amount of explained variance) on

both cognitive and affective dimensions of fear of crime.

Measuring Fear of Crime

Previous research exploring the dynamics of the fear of crime has led to a complex

of ideas surrounding the appropriate operationalization of the construct. With little

consensus regarding the most suitable measure of the fear of crime, the empirical

research has evolved with considerable inconsistencies. Initial research employed a

simple unidimensional measure of fear of crime derived from the National Crime

1 Rountree and Land (1996a) explored measures associated with each model of fear of crime; however, asystematic comparison of the models was not the focus of their study. Further, their measures of disorder

d i l i t ti d t th i hb h d l l hil th t t d i d ith

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Victimization Survey (NCVS) (e.g., Baumer, 1985; Kennedy & Krahn, 1984;

Maxfield,1984; Yin,1982). In these studies, respondents were asked some variation

of the following question: How safe do you feel or would you feel walking alone in

your neighborhood at night? As noted by Garofalo (1979) and further discussed by

Ferraro and LaGrange (1987), such a measure introduces various problems. While itis not necessary to repeat the various limitations associated with this operational

definition, it is sufficient to note its failure to distinguish between emotional fear of

crime and cognitive judgments concerning risk of crime victimization. More recent

research, including the study presented here, distinguishes between measures of

perceived risk and emotional fear, avoiding the earlier ambiguity surrounding the

NCVS-based measure (LaGrange, Ferraro, & Supancic, 1992; Rader, 2004;

Rountree, 1998; Rountree & Land,1996a; Williams, McShane, & Akers, 2000).

Recent research has also moved away from abstract conceptualizations of fear to

more clearly specified, concrete conceptualizations (LaGrange et al., 1992;Rountree, 1998; Rountree & Land, 1996a). Garofalo and Laub (1978) note that

operational definitions based on how afraid one feels in his/her neighborhood fail to

question respondents about concerns regarding specific crimes. Instead, they invoke

responses concerning a formless or global feeling of fear, making it difficult or

impossible to identify precisely what it is that respondents fear (Hale, 1996, p. 85).

To remedy this concern, many contemporary researchers have suggested a more

concrete measure based on multiple items tapping fear of specific crimes (Ferraro &

LaGrange, 1987). In order to address this issue, the present analysis employs a

measure of fear (worry of victimization) based on multiple crime scenarios.

Explaining Fear of Crime

Numerous theoretical developments have emerged to explain the various dynamics

of citizens fear of crime. Generally speaking, these theoretical frameworks fall into

two broad categories. The first category incorporates theories focusing on

facilitators of fear or factors that would rationally lead one to be more (vs. less)

fearful (e.g., increased vulnerability, disorderly local surroundings). The second

category incorporates theoretical developments whereby fear of crime is understood

through characteristics that inhibitor reduce the grounds for fear (e.g., social ties,

neighborhood cohesion, collective efficacy, and community attachment). Three

specific theoretical approaches to understanding fear of crimethe vulnerability,

disorder, and social integration modelshave emerged as relatively dominant in the

literature, with the first two models focusing on facilitators of fear and the latter

focusing on inhibitors of fear.

Vulnerability Model

A substantial research literature indicates that perceptions of personal vulnerability

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have the capacity for self-protection. This concept of perceived vulnerability has

been divided into two main categoriesphysical and social vulnerability. Physical

vulnerability pertains to the perception of increased risk to physical assault. This

stems from the decreased ability to fend off attack due to limited mobility or a lack

of physical strength and competence. Accordingly, gender and age affect fear ofcrime, as women and the elderly likely feel less capable of physically protecting

themselves when compared to those who are younger and/or male (Denkers &

Winkel, 1998; Ferraro & LaGrange, 1992; Fisher & Sloan, 2003; Gilchrist,

Bannister, Ditton, & Farrall, 1998; Ginsberg, 1985; Hughes, Marshall, & Sherrill,

2003; Kennedy & Silverman,1985; Killias & Clerici,2000; Smith & Torstensson,

1997; Warr,1984; Yin,1982).

Social vulnerability assumes increased exposure to victimization as a result of a

range of factors. For example, living in economically distressed, high-crime

neighborhoods often presents increased potential for victimization.3

In addition toresiding in high-crime neighborhoods, individuals lacking the material resources

necessary to protect their homes and/or recoup financial losses in the event of

victimization may feel increased social vulnerability. Finally, those deficient in

material and social resources or community and political networks that enable them

to cope successfully with anxiety-provoking situations (e.g., individual and

institutionalized racism) are likely to experience increased social vulnerability.

Consequently, racial and ethnic minorities, people living in poverty, and those with

lower educational levels may report higher levels of fear of crime than their

counterparts who are white, affluent, and well educated. These assumptions havebeen supported in previous research (Baumer, 1978; Clement & Kleiman, 1977;

Covington & Taylor, 1991; Erskine, 1974; Furstenberg, 1971; Jaycox, 1978;

Pantazis,2000; Parker & Onyekwuluje,1992; Skogan & Maxfield,1981; Taylor &

Hale,1986; Will & McGrath,1995).

Despite the empirical support afforded the vulnerability model, some researchers

have questioned its theoretical value based on findings that those who experience

higher levels of fear (women and the elderly) are, in fact, the least likely segment of

society to be victimized (Fattah & Sacco, 1989). Moreover, those who are most

likely to be victimized (young men) report lower levels of fear (Garofalo & Laub,

1978). This set of findings has been referred to as the fear-victimization paradox

(see Hale,1996). Attempts to resolve this apparent illogicality have been numerous

and sometimes insightful.

Sacco (1990) identifies two explanations of the fear-victimization paradox that shed

light on womens fear of crime. First, scholars argue that official crime data fail to

capture the full extent of female victimization (e.g., rape and domestic abuse are highly

underreported). Thus, hidden violence is not appreciated when determining the

rationality of womens fear. Once these unreported threats are acknowledged, the level

3 Because poorer individuals are also more likely to have experienced victimization as compared towealthier individuals, it could be argued that social vulnerability (as captured by income) should only be

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of womens fear is more appropriate and reasonable, leaving the charge of illogicality

unfounded. The second explanation assumes differences between fear of crime and

reported risk of victimization. While women do experience lower rates of general

violence, they are disproportionately the victims of sexual crimes. Consequently,

womens heightened fear may arise from an increased level of personal vulnerabilitydirectly related to the sexual nature of their experienced threats (Gordon & Riger,

1989; Junger,1987; Stanko,1987,1990a,b; Warr,1984,1985).

Related arguments can be advanced that fear among the elderly must be understood

through their differential sensitivity to risk, such that similar levels of risk do not

necessarily produce similar levels of fear (Warr,1984, p. 695). Moreover, Fattah and

Sacco (1989) argue that the widespread use of global or formless measures of fear of

crime have limited our understanding of what stimulates fear of crime among the

elderly. Inquiries concerning the safety one feels in his/her neighborhood after dark

likely provide unrealistic scenarios that are largely irrelevant to elderly respondentswho are unlikely to traverse the streets after dark (Fattah & Sacco,1989). The use of

such measures may produce an inflated level of fear among the elderly. In fact,

analyses employing more crime-specific measures of fear have found age to be a poor

predictor of fear of crime (LaGrange & Ferraro,1989).

Disorder Model

The disorder model originates from Shaw and McKays (1942) work on social

disorganization, wherein facilitators of fear were grounded in perceptions of localsurroundings, specifically signs of physical and social disorder (Skogan, 1990). The

basic assumption of this model is that neighborhood incivilities are the manifes-

tations of disorder that threaten individual residents even more than the actual

experience of crime. The physical decay and deterioration of a neighborhood

signifies a lack of local concern and the absence of informal social controls, leading

to citizen perceptions of neighborhood disorder.

Researchers have divided incivilities into two conceptual categoriessocial and

physical incivility (Burby & Rohe, 1989; LaGrange et al., 1992). Social incivility

refers to disruptive behaviors such as loiterers, inconsiderate neighbors, loose dogs,

unsupervised and/or unruly teenagers, gangs, beggars, and public drinking. Physical

incivilities refer to disorderly surroundings such as abandoned cars, vandalized

property, trash, vacant houses, and deteriorated homes. Neighborhood residents who

perceive disordered social and physical local surroundings are more likely to exhibit

higher levels of fear (Gates & Rohe, 1987; LaGrange et al.,1992; Lewis & Salem,

1986; Skogan,1990; Skogan & Maxfield,1981).

Furthermore, residents may perceive themselves to be at increased risk of

victimization in areas in which there are visible signs of community disorder

(Covington & Taylor, 1991; Lewis & Salem,1986). Perceptions of disorder likely

translate into environmental uncertainty and perceived threats to personal safety(Kennedy & Silverman, 1985). Skogan and Maxfield (1981) and Lewis and

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Kellings (1982) broken windows theory, which posits a strong connection

between disorderly surroundings and fear of crime.

Social Integration Model

Shifting from facilitators of fear to inhibitors of fear, the social integration model

purports that those who are socially integrated within their neighborhoods experience

lower levels of fear of crime than those who are not as well integrated (Hartnagel,

1979; Lewis & Salem,1986; Riger, LeBailly, & Gordon,1981; Rountree & Land,

1996b). Social integration has been defined as a persons sense of belonging to their

local surroundings as well as their attachment to the community (Adams, 1992;

Kasarda & Janowitz,1974; Keyes,1998). Prior research has operationalized social

integration as the ability to identify strangers in the area and the degree to which

neighbors feel they are a part of the neighborhood (Hunter & Baumer,1982). Otherresearchers have defined social integration as possessing personal investment in the

neighborhood, having social ties to neighbors, and feeling emotional attachment to the

community (Kanan & Pruitt, 2002). Additional social integration measures have

included participation in formal organizations (Austin, Woolever, & Baba, 1994),

involvement in neighborhood activities, engaging in neighborhood information

sharing, the perception of similarities among residents, and the presence of friends or

relatives living in the neighborhood (Bursik & Grasmick, 1993). In sum, residents who

become familiar with their neighbors and develop connectedness to their neighbor-

hood should report lower levels of fear than those who do not.Empirical research testing the social integration model has produced somewhat

mixed results, though substantial evidence appears to suggest an inverse relationship

between levels of social integration and fear of crime (Austin et al., 1994; Baba &

Austin,1989; Hunter & Baumer,1982; Kanan & Pruitt, 2002; Krannich, Berry, &

Greider, 1989; McGarrell, Giacomazzi, & Thurman, 1997; Rountree & Land,

1996b). Bursik and Grasmick (1993) and Gibson et al. (2002) argue, however, that

prior measures of social integration lack methodological consistency, thus making

subjective cross-study comparisons more difficult. More specifically, when

researchers employ different measures of social integration and reach dissimilarconclusions concerning its effect on fear of crime, it is not readily apparent if these

differences are attributable to the differing methodologies or are, in fact, real

differences in how social integration is operating from one study to the next. This

limitation makes it more difficult to draw solid conclusions about the effect of social

integration on fear of crime, although the evidence is generally supportive of the

social integration model (e.g., Hale, 1996).

Data and Methodology

Individual-level data for the current analysis were derived from the 2003 Eastern

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of Washington. The survey respondents were clustered within 21 cities, of which 5

can be classified as metropolitan areas and 16 as rural areas.4 City-level data were

derived from two sources. First, basic demographic information was taken from the

2000 U.S. Census for each city included in the analysis. Second, official crime

statistics for each location were provided by the Crime in Washington 2004 AnnualReport produced by the Washington Association of Sheriffs and Police Chiefs

(WASPC). Due to randomly missing individual-level data, the final sample for

statistical analysis includes 2,599 residents located within 21 cities.

Dependent Variables

Perceived Risk

The first dependent variable is a global measure of perceived risk and represents the

cognitive dimension of fear. It is a multiple-item index based on two questions

originating from the NCVS.5 Specifically, respondents were asked How safe

would you feel walking alone during the day [night] in the area where you live?

Responses were summed to create a scale ranging from 2 (very unsafe) to 10 (very

safe), and reliability tests indicated acceptable internal consistency (Cronbachs

alpha =.72). As previously discussed, utilizing such a measure has received ample

criticism (see Ferraro & LaGrange, 1987), particularly when the desired outcome

measure is emotional fear as opposed to a cognitive perception of risk. Despite the

limitations of a global measure, perceived risk is included in the current analysis toallow for a comparison across the cognitive and affective dimensions of fear.

Additionally, it provides a baseline for comparison across studies, as multiple

researchers have incorporated a similar measure.

Worry of Victimization

The second dependent variable represents the affective dimension of fear and is

based on a seven-item index capturing respondents frequency of worry aboutbecoming the victim of specific crime scenarios (e.g., being burglarized while

someone is at home). Respondents were asked How much do you worry about

each of the following situations? Responses were summed to create a scale ranging

from 7 (never) to 28 (very frequently), and reliability of the measure demonstrated

strong internal consistency (Cronbachs alpha = .89).

The resulting operational definition offers at least two benefits to the current

analysis. First, perceived risk is general in nature and requires respondents to

speculate about how safe they would feel (hypothetical) in a particular situation,

4 Survey instruments were mailed to a random sample of household addresses (extracted from localt l h di t i ) ithi h f th iti i l d d i th l i t d h F th 8 836

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whereas worry of victimization is more specific and taps into the amount of worry

respondents (actually) do feel. Second, the former measure is cognitive, asking

respondents to make a judgment concerning their safety, whereas the latter measure

is arguably affective (see Ferraro & LaGrange, 1987; Rountree & Land, 1996a;

Taylor & Hale, 1986), tapping into the emotional aspect of fear.

Individual-Level Independent Variables

Vulnerability

Race, age, sex, education, and income are included in the analysis as proxy

measures of individual vulnerability to criminal victimization.6 Due to the

minimal presence of minorities in the sample, race is a dichotomous variable

coded as 0 (other) and 1 (White). Age was captured by converting the year ofbirth to the respondents age at the completion of the survey. Sex was coded as 0

(female) and 1 (male). Educational achievement was captured on a scale of 1 (less

than high school) to 7 (graduate degree). Finally, annual income was measured on

a scale of 1 (less than $10,000) to 10 (more than $90,000). Although past

victimization has been argued to influence fear of crime through increased

feelings of vulnerability to future victimization, a reliable measure of previous

victimization was not available in the present data. Despite this apparent

shortcoming, several studies have called into question the strength of the

association between actual victimization and fear of crime (e.g., Baumer, 1985;Hindelang, Gottfredson, & Garofalo, 1978; McGarrell et al., 1997). Researchers

questioning the direct relationship between victimization and fear of crime have

pointed to empirical evidence demonstrating either a weak or nonexistent

correlation (e.g., Gibson et al., 2002). Given these findings, the absence of a

previous victimization measure should not pose significant shortcomings to the

current study. Table1 displays descriptive statistics for all variables included in

the analysis.

Disorder

Perceived disorder or incivility was measured by summing the responses to eight

questions regarding the seriousness of neighborhood problems. The resulting scale

ranged from 8 (no problem) to 32 (a serious problem) and demonstrated acceptable

internal consistency (Cronbachs alpha = .83). Although previous research has

distinguished between perceptions of physical disorder (e.g., trash, abandoned

buildings, vandalism) and perceptions of social disorder (e.g., public drunkenness,

6 In the absence of more direct measures of vulnerability to criminal victimization, race, age, sex,

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Table 1 Descriptive statistics

Note: Total sample size is 2,599citizens and 21 cities

Variables Mean Standard deviation

Dependent variables (N=2,599)

Perceived risk 3.48 1.44

Worry of victimization 14.95 4.02

Individual-level variables (N =2,599)

Race

Non-White (0) (6.5%)

White (1) (93.5%)

Gender

Female (0) (36.1%)

Male (1) (63.9%)

Age 57.99 15.64

Income 6.12 2.03

Less than $10,000 (1) (1.4%)

$10,000$19,999 (2) (3.0%)

$20,000$29,999 (3) (5.7%)

$30,000$39,999 (4) (9.2%)

$40,000$49,999 (5) (8.8%)

$50,000$59,999 (6) (34.5%)

$60,000$69,999 (7) (16.3%)

$70,000$79,999 (8) (5.3%)

$80,000$89,999 (9) (3.5%)

More than $90,000 (10) (10.4%)

Education 4.12 1.88

Less than high school (1) (4.7%)

High school graduate (2) (18.0%)

Some college (3) (25.7%)

Associate degree (4) (8.4%)

Bachelor degree (5) (17.2%)

Some graduate coursework (6) (6.9%)

Graduate degree (7) (19.0%)

Disorder 12.58 4.45

Social integration -.01 2.95

City-level variables (N =21)

Violent crime rate 3.10 1.76

Property crime rate 55.08 20.11

Unemployment 6.05 1.76

Urbanism

Rural (0) (76.2%)

Urban (1) (23.8%)

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in which measures of social and physical disorder were also found to represent a

single underlying construct (Ross & Mirowsky,1999).

Social Integration

Social integration was captured through responses to four questions that were

derived from prior literature (Gibson et al.,2002; McGarrell et al.,1997) examining

the link between social integration and fear of crime: (1) Would you describe the

area where you live as a place where people help one another or a place where

people mostly go their own way? (2) Do you feel the area where you live is more

of a real home or more like just a place to live? (3) How often do you talk with

your neighbors? and (4) When you do a favor for a neighbor, can you trust the

neighbor to return the favor? Individuals scoring higher on the resulting scale

demonstrated higher overall levels of social integration. To account for the differingmetrics in which the questions were measured, responses were standardized across

items. Reliability tests indicated acceptable internal consistency (Cronbachs

alpha = .71).

City-Level Independent Variables

Several city-level variables were included in the analysis to control for potential

contextual effects on perceived risk and worry of victimization.7 Past research has

suggested that community-level characteristics, particularly those related to socialdisorganization and the breakdown of informal social control, may lead to increased

perceptions of risk and fear of crime (Lee & Ulmer, 2000). For this reason, the

present study includes measures of the violent crime rate, property crime rate,

unemployment rate, and urbanism, all of which have been linked to social

disorganization (Bursik & Grasmick, 1993; Sampson & Groves, 1989; Skogan &

Maxfield,1981). Specifically, the violent and property crime rates were based on the

average rate of reported crimes per 1,000 persons for the 3 years prior to the

collection of survey data (20002002). Unemployment rates for each of the cities

were obtained from the 2000 U.S. Census. Finally, areas identified by the 2000 U.S.Census as metropolitan locations were considered to be urbanized and were coded 0

(rural) or 1 (urbanized).8

7 Ideally neighborhood-level contextual factors would be used in the current analysis, but suchinformation was unavailable. Thus, city-level contextual factors were included in their place. This raisesconcern over the relationship between city-level factors (e.g., crime rate) and fear experienced withinsmaller special regions (e.g., perceived safety in the area where respondents live). Hypothetically, one

could live in an affluent, safe neighborhood nested within a dangerous city, attenuating the influence ofcity-level factors. In the current analysis, however, the majority of the cities are rural towns with

populations of less than 10,000 residents, arguably creating a situation where city-level factors,particularly crime rates, have widespread influence over fear of crime, despite location within the town.8

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Analytic Strategy

To determine the explanatory power of the vulnerability, disorder, and social

integration models across the cognitive and affective dimensions of fearwhile

controlling for city-level contextual effectsmultilevel modeling was employed.Hierarchical modeling has become the standard method used to estimate the effects

of community-, county-, and city-level factors on individual outcomes, particularly

when the data contain a substantial amount of clustering within cities, as in the

present study (Bryk & Raudenbush, 2002). These models not only provide an

efficient illustration of the degree to which a given individual-level outcome varies

across geographic areas, but also formally adjust for the non-independence of

sample members living within the same city. The failure to model this type of non-

independence can result in estimated standard errors that are biased downward;

consequently, conclusions regarding the statistical and substantive importance ofeither individual- or city-level factors may be misleading (Baumer, Messner, &

Felson,2000; DiPrete & Forristal, 1994; Snijders & Bosker, 1999).

The analysis reported here uses hierarchical linear random-intercept models to

evaluate the degree to which perceived risk and worry of victimization vary across

the cities included in the data (for detailed explanations of these models, see Bryk &

Raudenbush, 2002). First, intercept-only models are estimated to determine the

baseline variation in the dependent variables. Second, each of the three models of

fearvulnerability, disorder, and social integrationare independently specified to

determine the variation explained by each model separately. Third, random-intercept models simultaneously estimating the effects of the vulnerability, disorder,

and social integration models are specified. Finally, full random-intercept models

are estimated to determine the influence of the vulnerability, disorder, and social

integration models net of city-level contextual factors and to determine the degree to

which both level-one (individual) and level-two (city) variables account for

variation in the dependent variables across cities.9

Results

Assessing Baseline Variation

Prior to assessing the influence of individual- and city-level characteristics on the

cognitive and affective dimensions of fear, it was first necessary to evaluate the

degree to which the dependent measures actually vary across the cities included in

the analysis. Table2displays the results of two baseline hierarchical linear models

presenting the intercept (which describes the mean level of each dependent variable)

and variance components (which describes the amount of variation across cities) for

perceived risk and worry of victimization. Illustrating the necessity for subsequent

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analyses, the random effects variance components and corresponding test statistics

indicate significant variation in both dimensions of fear across the 21 cities

(Perceived Risk: s00= .102, v2 = 457; Worry of Victimization: s00 = 1.066,

v2 = 3,137).

Assessing the Conceptual Models

Table3 separately compares the effects of the vulnerability, disorder, and social

integration models on respondents perceived risk of victimization. The results from

this portion of the analysis help illustrate which of the three models best explains the

observed variation in the dependent variable. Consistent with the vulnerability

model, race, gender, age, income, and education significantly influenced respon-

dents perception of risk. More specifically, minorities, women, those who are older,

those with lower incomes, and those with lower levels of education were more

likely to report higher levels of risk as compared to their counterparts. Although thevulnerability measures were significant and directionally accurate, the random

effects portion of the table indicates that the vulnerability model accounts for a

modest proportion of the variation in respondents perceived risk across cities. More

Table 2 Intercept-onlyhierarchical models forperceived risk and worry ofvictimization

Note: Standard errors aredisplayed in parentheses

* p\ .05

Perceived risk Worry of victimization

Fixed effects

Intercept 3.31* (.08) 14.42* (.23)

Random effects

Variance component .10 1.07

Chi-square 457.10* 3137.41*

Table 3 Individual-leveltheoretical models explainingperceived risk

Vulnerability Disorder Socialintegration

Intercept 4.67* (.15) 1.72* (.08) 3.35* (.07)

Fixed effects

Race -.35* (.08)

Gender -.80* (.04)

Age .01* (.00)

Income -.09* (.01)

Education -.09* (.01)

Disorder .13* (.00)

Socialintegration

-.15* (.01)

Random effects

Intercept

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specifically, the variables associated with the vulnerability model were only able to

reduce the variance component by 12%.

As predicted by the disorder and social integration models, the associated measures

included in the analysis were found to affect citizens levels of perceived risk.

Respondents who viewed their neighborhoods as characterized by high levels ofdisorder reported significantly higher levels of perceived risk. Moreover, those who

viewed their neighborhoods as less socially integrated also reported significantly

higher levels of perceived risk. Examination of the random effects portion of the table

for these two models indicates that respondents perception of their neighborhoods

level of disorder and social integration explained a much larger portion of the variation

in the dependent variable as compared to the vulnerability measures. Specifically, the

disorder model accounts for the largest portion of the variation in perceived risk across

cities (44%), followed by the social integration model (25%).

Table4separately assesses the same three modelsvulnerability, disorder, andsocial integrationwith worry of victimization as the dependent variable. Results

indicated that only two of the five vulnerability measures significantly affected

respondents worry of victimization. Women and younger individuals reported

higher levels of worry of victimization as compared to men and older individuals.

While these results provide strong support for our expectation that women are more

likely than men to worry about victimization (due to their physical vulnerability),

the negative age effect contradicts the vulnerability-derived assumption that elderly

individuals possess greater worries of victimization. Finally, the random effects

portion of the table indicates that the vulnerability model does not account for muchvariation (about 1%) in respondents worry of victimization across cities.

The remaining two models included in Table4suggest that both the disorder and

social integration measures influenced respondents worry of criminal victimization.

Table 4 Individual-leveltheoretical models explainingworry of victimization

Vulnerability Disorder Social

integration

Intercept 16.13* (.26) 10.75* (.19) 14.47* (.22)

Fixed effects

Race .02 (.08)

Gender -.92* (.04)

Age -.02* (.00)

Income .01 (.01)

Education -.02 (.01)

Disorder .30* (.00)

Socialintegration

-.14* (.01)

Random effectsIntercept

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Specifically, citizens who perceived their neighborhoods as characterized by higher

levels of disorder, as well as lower levels of social integration, were significantly

more likely to report worry of victimization. The random effects portion of the table

indicates that the disorder and social integration models account for 36% and 8% of

the variation in worry of victimization, respectively. As with the analysis ofperceived risk, the disorder measure appears to be the most powerful predictor of

worry of victimization across cities.

To further assess whether one theoretical framework has greater explanatory

power than the others, additional tests were conducted. Table 5presents the results

of two models that assess, simultaneously, the effects of the vulnerability, disorder,

and social integration models to determine whether or not the effects of one model

attenuates the effects of the other(s). The results suggest that including the

vulnerability, disorder, and social integration measures in a single model helps to

further explain the city-level variation in both perceived risk and worry ofvictimization.10 None of the previously statistically significant variables became

non-significant in the presence of the additional individual-level predictors. In fact,

three of the vulnerability measuresrace, income, and educationbecame

significant predictors of worry of victimization when simultaneously assessed with

the disorder and social integration measures. Thus, inclusion of the disorder and

social integration variables actually increased the influence of several vulnerability-

related variables on worry of victimization, although in the opposite directions

predicted by the vulnerability model.11

The simultaneous analysis of the vulnerability, disorder, and social integrationvariables explained significantly more city-level variation in the dependent variables

when compared to the separate analyses of each model. The random effects portion

of Table5indicates that when all measures were considered in a single model, the

variance component was reduced by 63% for perceived risk and 42% for worry of

victimization. Despite the relatively large portion of variation explained by the

theoretical models, the variance components for both perceived risk and worry of

victimization remained significant; this indicates that additional unaccounted for

factors were influencing the dependent variables across cities.

The results presented thus far indicate that individual-level factors have

important influences on both the cognitive (perceived risk) and affective (worry

of victimization) dimensions of fear of crime. These variables also help to account

for the variation in responses across cities. However, it is possible that individual-

level factorsspecifically our measures of vulnerability, disorder, and social

integrationmay be affected by contextual factors that vary across cities. That is,

respondents in cities characterized by certain features (e.g., high crime rates, high

10 It should also be noted that multicollinearity diagnostics were examined for both individual- and city-level variables. Tolerances ranged from .54 to .98 and Variance Inflation Factors ranged from 1.0 to 1.8,

indicating that multicollinearity was not a concern in the present analysis. Moreover, bivariatecorrelations for all independent variables are presented in Appendix B.11

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levels of unemployment, urbanization) may report higher or lower levels of

perceived risk or worry of victimization despite the effects of individual-level

characteristics. Thus, Table6 provides the results of the final analyses which

address three main issues: (1) whether key features of respondents location

influence reported levels of perceived risk or worry of victimization; (2) whether the

inclusion of city-level variables influences the effects of individual-level charac-teristics on perceived risk or worry of victimization; and (3) whether the inclusion of

both individual- and city-level factors help to explain a larger portion of the

variation in reported levels of perceived risk or worry of victimization.12

The results presented in the first column of Table 6indicate that the inclusion of

city-level variables does not substantially alter the effects of individual-level

characteristics on perceived risk as reported in Table5. Virtually all of the

individual-level factors exert the same effect on perceived risk when violent and

property crime rates, unemployment rates, and urbanism of the respondents cities

are included in the analysis. However, one of the city-level variablesurbanismdoes exert a statistically significant positive effect on respondents perceived risk.

Specifically, citizens living in urban locations reported higher levels of perceived

risk as compared to those living in rural locations. Moreover, the random effects for

this model indicate that the inclusion of both individual- and city-level variables

accounts for nearly all of the variation in perceived risk across the cities included in

the analysis (96%).

The results presented in the second column of Table6 also indicate that

including city-level variables does not influence the effects of individual-level

Table 5 Combined individual-level models explainingperceived risk and worry ofvictimization


* p\ .05

Perceived risk Worry of Victimization

Intercept 2.37* (.16) 10.66* (.23)

Fixed effects

Race -.17* (.08) .31* (.08)

Gender -.72* (.04) -.73* (.04)

Age .01* (.00) -.01* (.00)

Income -.03* (.01) .11* (.01)

Education -.06* (.01) .03* (.01)

Disorder .11* (.01) .29* (.01)

Social integration -.11* (.01) -.04* (.01)

Random effects

Intercept

Variance component .04 .62

Chi-square 194.07* 1917.53*

12 Due to the small number of level-two groups available for analysis (N= 21) and the consequent

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characteristics on worry of victimization, as observed in earlier analyses (see

Table5). The random effects for this model indicate that the inclusion of both

individual- and city-level variables accounted for a significant portion of the

variation in reported worry of victimization across the 21 cities (53%). Despite this

observed explanatory power, the variance component for the worry of victimization

model remained statistically significant (v2 = 725.39), indicating the presence of

unexplained variation in the worry of victimization measure across cities.

Discussion

The primary objective of this analysis was to assess the explanatory power of three

conceptual models with respect to two separate dimensions of the fear of crime. The

analysis revealed that the disorder model accounts for the greatest proportion of

variation in both dimensions of fear of crimecognitive and affectiveat the city

level. In other words, individual perceptions of neighborhood disorder, such as

noise, traffic problems, and youth gangs, appeared to be the most powerful

determinant of fear of crime. Levels of individual social integration also appeared tobe an important determinant, but significantly less so than perceptions of

Table 6 Complete hierarchicalmodels explaining perceivedrisk and worry of victimization


* p\ .05

Perceived risk Worry of victimization

Fixed effects

Intercept 1.86* (.23) 10.08* (.67)

Individual-level variables

Race -.16* (.08) .31* (.08)

Gender -.72* (.04) -.73* (.04)

Age .01* (.00) -.01* (.00)

Income -.03* (.01) .11* (.01)

Education -.06* (.01) .03* (.01)

Disorder .11* (.01) .29* (.01)

Social integration -.10* (.01) -.04* (.01)

City-level variables

Violent crime .03 (.03) .20 (.13)

Property crime .00 (.00) .00 (.01)

Unemployment .02 (.02) -.08 (.10)

Urbanism .25* (.08) .24 (.41)

Random effects

Intercept

Variance component .01 .50

Chi-square 30.42* 725.39*

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Although the vulnerability, disorder, and social integration models were

assessed as separate or distinct models in the present analysis, a particular

limitation of this approach should be noted. Specifically, it is plausible that the

three models overlap to some degree with one another, rather than existing as

purely distinct frameworks. For example, individuals who are more sociallyintegrated into their neighborhoods may feel less vulnerable to victimization due

to the availability of social support from their neighbors. Socially integrated

individuals may also feel an increased ability to cope with troublesome situations

due to a sense of belonging or community (see Hale, 1996). Along these same

lines, socially integrated residents who participate within the community may

become familiarized with signs of social and physical incivilities, consequently

reducing the influence of disorder on fear of crime (Riger, LeBailly, & Gordon,

1981). Despite this potential overlap or interaction between vulnerability, social

integration, and perceived disorder, each of the three theoretical models predictedsignificant direct effects on fear of crime.

While the disorder and social integration models behaved similarly across

measures of perceived risk and worry of victimization, variables associated with the

vulnerability model displayed directional changes across the two dependent

variables. When examining the effects of the vulnerability-related measures on

the cognitive dimension of fear (perceived risk), directional accuracy was observed.

As expected, minorities, females, the elderly, and those with lower levels of income

and education reported higher levels of perceived risk. The directional accuracy,

however, was diminished when examining the effects of these variables on theaffective dimension of fear (worry of victimization). Specifically, the effects of race,

age, income, and education were significant, but they were in the opposite directions

predicted by the vulnerability model. Thus, it appears that variables associated with

the vulnerability model operate differently across the dimensions of fear analyzed in

the present analysis.

It should be noted that previous research has discovered similar findings. For

example, LaGrange et al. (1992) found age to negatively influence the affective

dimension of fear of crime while positively influencing the cognitive dimension.

Moreover, Rountree and Land (1996a) reported that the effects of socio-

demographic variables, such as age and income, varied significantly across the

two measures of fear of crime. Consistent with LaGrange et al. (1992), age was

found to negatively impact the affective dimension of fear (operationalized as

burglary-specific fear), while its influence on the cognitive dimension was not

statistically significant. Further, income was found to affect perceptions of risk

negatively but to positively affect burglary-specific fear. While these findings are

contrary to much of the earlier literature in support of the vulnerability model,

Rountree and Land (1996a) point out that many earlier analyses have been limited

to cognitive assessments of perceived risk. This observation is supported by the

current analyses, which suggest that the vulnerability model maintains directional

accuracy when predicting levels of perceived risk but not when predicting levels of

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be the case. One possible explanation stems from LaGrange and Ferraros (1989)

work. These researchers contend that global measures of fear, such as the measure

of perceived risk in the current analysis, lack relevance to the everyday lives of

many people. For instance, they inquire about feelings of safety when walking alone

on the streets at night; this event is probably infrequent for many neighborhoodresidents. Moreover, global measures forgo specificity, allowing unwanted

flexibility in respondents interpretation of the items. By contrast, concrete

measures of fearbased on specific crime scenariosleave little room for variation

in respondents interpretations.

Another plausible explanation concerns the process of desensitization potentially

experienced by minorities and those with lower levels of income and education.

Such individuals disproportionately live in crime-prone neighborhoods, where

levels of risk are relatively high. Citizens rooted in these neighborhoods or those

who have spent lengthy periods of time in high-crime locations may begin to viewtheir surroundings as normal and, thus, experience lower levels of fear of crime as a

consequence. As they become desensitized to their surroundings, it may be possible

for them to maintain an understanding of their higher-risk level while experiencing

lower levels of actual worry or emotional fear of crime.

The statistically nonsignificant influences of violent and property crime rates on

perceived risk and worry of victimization may be a result of the small sample size at

level two of the analysis, resulting in reduced power to reveal significant findings.

The nonsignificant findings may also be a byproduct of the measures of fear of

crime utilized.

13

Perceived risk is a non-specific global measure, and worry ofvictimization is based on a multiple-item scale, which means that the effects

of violent and property crime on specific types of fear of crime could be masked.

For example, Rountree (1998) found neighborhood-level property crime to have a

statistically significant effect on burglary-specific fear but not on fear of violent

crime. Thus, it is safe to conclude that while violent and property crime rates did not

affect our multiple-item measures of fear of crime, they may be important for

understanding fear of specific violent and property crimes separately.

Aside from property and violent crime rates, two other city-level factors were

included as controls in the analysisnamely, unemployment and urbanism. Rates

of unemployment displayed no significant effect on levels of perceived risk or worry

of victimization; urbanism, however, did exhibit a strong positive effect on levels of

perceived risk. This finding is congruent with past research positing increased levels

of risk in urban areas (e.g., Baumer1978; Belyea & Zingraff, 1988; Sacco,1985)

and is not surprising considering the higher crime rates and lower levels of social

integration associated with inner-city or urbanized living.

The most obvious implication of the current research concerns the influence of

perceived neighborhood disorder on fear of crime. Neighborhood disorder appears

to be the most powerful predictor of fear of crime, whether measured as perceived

13 It should also be noted that separate bivariate analyses of crime trends (operationalized as the

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risk or measured as worry of victimization. Moreover, the disorder effect remained

stable across the models, despite various controls at the individual and city levels.

The obvious implication of this finding is the need to reduce levels of perceived

neighborhood disorder which should, in turn, reduce fear of crime. Toward this end,

community-oriented approaches may be quite successful, because they allowresidents to address disorder-related problems in conjunction with police and

various social agencies. Practitioners must be cautioned, however, that perceptions

of disorder are not necessarily grounded in reality. For example, Reisig and Parks

(2000) discovered that citizens living in the same locationand, thus, experiencing

similar neighborhood conditionsperceived very different levels of physical and

social disorder. Thus, effective measures will likely depend on future research

aimed to identify the underlying correlates and causes of perceived disorder.

While the current analysis allowed us to compare the vulnerability, disorder, and

social integration models on both cognitive and affective dimensions of fear, futureresearch should devote additional attention to the behavioral dimension of fear.

Arguably, this is the most important dimension of the fear of crime, capturing

actual changes in human behavior. The behavioral dimension of fear illustrates the

overt effect of fear of crime in citizens everyday lives. Researchers should aim to

develop an appropriate measure of this behavioral dimension of fear to shed further

light on the fear-of-crime dynamic.

Acknowledgments We would like to thank Nicholas P. Lovrich, Michael J. Gaffney, and the Divisionof Governmental Studies and Services for providing the primary data analyzed herein. This research was

supported, in part, by Project Safe Neighborhoods contract F03-68303004. Please direct correspondenceto Noelle E. Fearn, Department of Sociology and Criminal Justice, Saint Louis University, 3500 LindellBlvd., 211 Fitzgerald Hall, St. Louis, MO 63103.

Appendix A: Scale Items

Perceived Risk

1. How safe would you feel walking alone during the day in the area where you

live?

(1) Very safe (2) Safe (3) Neither safe nor unsafe (4) Unsafe (5) Very unsafe

2. How safe would you feel walking alone in the area where you live at night?

(1) Very safe (2) Safe (3) Neither safe nor unsafe (4) Unsafe (5) Very unsafe

Worry of Victimization

How much do you worry about each of the following situations? Do you worry very

frequently, somewhat frequently, seldom, or neverabout:

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3. Getting mugged

4. Getting beaten up, knifed, or shot

5. Getting murdered

6. Getting burglarized while someone is at home

7. Getting burglarized while no one is at home

Neighborhood Disorder

Using the answer key below, please write the number from the Answer Key that

most accurately describes the extent of these problems in the neighborhood where

you live.Answer Key: (1) No Problem (2) Uncertain (3) A Problem (4) A Serious Problem

1. Vandalism

2. Groups of teenagers or others hanging out and harassing people

3. Garbage and litter

4. Traffic problems

5. People drinking to excess in public

6. Dogs running at large

7. Youth gangs are present

8. Noise

Social Integration

1. Would you describe the area where you live as a place where people mostly

help one another or a place where people mostly go their own way?

(1) People help one another (2) People go their own way

2. Do you feel the area where you live is more of a real home or more just a

place to live?

(1) Real home (2) Just a place to live

3. How often do you talk with your neighbors?

(1) Daily (2) 13 times a week (3) 13 times a month (4) Less than once a month

4. When you do a favor for a neighbor, can you trust the neighbor to return thefavor?

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Appendix B: Bivariate Correlation Matrixes

Individual-Level Bivariate Correlation Matrix

Race Gender Age Income Education Disorder Social

integration

Race

Gender .02

Age .06* .07*

Income .08* .01 -.16*

Education .03 .09* -.09* .31*

Disorder -.08* -.09* -.12* -.16* -.13*

Social integration .09* .05* .13* .11* .09* .24*

* p\ .05

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A Multilevel Analysis of the Vulnerability Disorder and Social Integration Models of Fear of Crime

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