Feminist Criminology
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An Exploration of Gender Differences in Measurement of Fear of Crime
Valerie J. Callanan and Brent Teasdale
Feminist Criminology 2009; 4; 359 originally published online Sep 16, 2009;
DOI: 10.1177/1557085109345462
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An Exploration of Gender
Differences in Measurement
of Fear of Crime
Feminist Criminology
4(4) 359­–376
© The Author(s) 2009
Reprints and permission: http://www.
sagepub.com/journalsPermissions.nav
DOI: 10.1177/1557085109345462
http://fc.sagepub.com
Valerie J. Callanan1 and Brent Teasdale2
Abstract
Most studies that investigate differences in fear of crime between men and women
assume measurement invariance. This study explores gender differences in two
different scales that measure fear of crime—a four-item factor from a survey of 1,918
southern Californians and an eight-item factor from a statewide survey of 4,245
Californians. Measurement invariance of these factors is assessed with two structural
equations modeling techniques—a two-groups confirmatory factor analysis and
a test for differential item function. Significant gender differences are found in the
measurement of fear of crime. These are explained by the presence of items that
measure fear of crimes with the potential for physical harm, particularly sexual assault.
The findings suggest that gender differences in fear of crime may be overestimated by
a factor of two if differential item function is ignored.
Keywords
fear of crime; gender; measurement invariance
Introduction
For more than 25 years, research on fear of crime has been criticized for poor definition and operationalization of its concepts. In spite of years of debate, many issues
remain unresolved, and “research practice has hardly benefited from these debates”
(Bilsky & Wetzels, 1997, p. 310). A key criticism of earlier studies was the propensity
to use a single-item (usually dichotomous) measure of fear. For example, most studies
used either the General Social Survey (GSS) question, “Is there any area right around
here—that is, within a mile—where you would be afraid to walk alone at night?” or an
1
University of Akron, OH
Georgia State University, Atlanta
2
Corresponding Author:
Valerie J. Callanan, Department of Sociology, University of Akron, OH 44325-1905
Email: [email protected]
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Feminist Criminology 4(4)
item from the early version of the National Crime Survey (NCS), “How safe do you
feel or would you feel being out alone in your neighborhood at night?” (with a parallel
question for day). As critics pointed out, neither question distinguished between perceptions of risk and actual fear of crime nor explicitly asked the respondent about being
afraid of being a victim of crime. The questions also suggested a situation—walking
alone at night—that may be an unlikely event for many, thus leading respondents
to assess a situation they rarely experience (Ferraro & LaGrange, 1987).
Many of the studies using single-item measures uncovered the so-called victimizationfear paradox, namely, women and the elderly reported higher levels of fear but lower
levels of criminal victimization. For example, surveys in the 1970s found that the
elderly were so fearful of crime that they became “prisoners in their own homes,” and
this claim was widely reported by the mass media (Ziegler & Mitchell, 2003).
Criminologists debated whether these purportedly high levels of fear were rational or
irrational (e.g., Jaycox, 1978; Jones, 1987; Lindquist & Duke, 1982). Yet, as Ferraro
and LaGrange (1987, 1988) observed more than 20 years ago, the findings from these
early surveys were very questionable given the conceptual and methodological problems with the research. They argued that there are several dimensions to fear of crime,
which include cognitive assessments of risk, concerns about crime in general (or crime
control policies), assessments of what would happen if one was criminally victimized
(perceived vulnerability), precautionary behavior to avoid criminal victimization, and
actual fear of crime (the emotional, anxious response to potential victimization.).
Quantitative measurement has moved from using global single-item indicators to
multiple-item scales to capture the various dimensions of fear of crime. As the field
developed better measures, studies began to find that prior assumptions such as irrational fear were probably artifacts of conceptual misspecification. For example,
studies that distinguished between dimensions of fear found that the elderly felt vulnerable because they felt unable to defend themselves if physically attacked (Warr,
1984, 1987), but they had significantly lower levels of fear of crime than younger age
groups because they avoided places and activities that would increase their likelihood
of criminal victimization (e.g., Chadee & Ditton, 2003). However, research continued
to find that women had much higher levels of fear, perceived risk, and perceived vulnerability to crime than men, even in studies that employed multiple-item scales to
measure these dimensions (e.g., LaGrange & Ferraro, 1989).
One area seldom investigated is the concept of measurement invariance. Scales
such as those constructed to measure fear of crime are made up of multiple items, and
comparisons between groups rely on the assumption that the scale is measuring the
same trait in all of the groups. If that assumption is correct, meaningful interpretations
of group differences (such as gender) make sense and result in significant interpretations; however, if this assumption is violated then the results may distort reported
differences. In other words, it is critical that a set of indicators measure the same construct with equivalency across groups. In general, if models have not been tested for
measurement equivalence, any differences found between groups cannot be assumed
to reflect real variation, as these may be an artifact of measurement error alone. This
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361
issue is particularly important for fear of crime research, which relies on selfdeveloped measures. Thus, in the absence of standardized measurement, researchers
should test for measurement invariance before comparing group differences. To
explore this issue, this study will use two different data sets to estimate the measurement equivalence of fear of crime for men and women.
Gender Differences in Fear of Crime
The vast majority of studies, especially quantitative, consistently found that women
have much higher levels of fear of crime than do men (see Hale, 1996, for review),
although these findings have been debated on a number of grounds. First, some
researchers questioned the reliance on survey-based data, which they argued, often
masks nuances in fear of crime between men and women by asking for quantitative
assessments of responses to potential crime, often conflating emotional responses
with cognitive assessments of crime risk (e.g., Chadee, Austen, & Ditton, 2007). In
contrast, qualitative studies found that fearful men and women were much more similar than dissimilar (e.g., Gilchrist, Bannister, Ditton, & Farrall, 1998).
Feminist criminologists argue that men and women’s relation to crime victimization is socially constructed. The gendered reactions to potential crime risk mesh with
traditional and well-established cultural narratives about men and women, namely,
men are strong and aggressive, like offenders, and women are weak and passive, as are
victims (Goodey, 1997; Hollander, 2001; Madriz, 1997; Stanko, 1992). For example,
not only do both men and women believe that men are far more dangerous than women
(Hollander, 2001), but they also believe men are far less susceptible to criminal victimization. The social construction of gender differences in vulnerability to criminal
victimization begins in childhood (Goodey, 1997; Valentine, 1997) and is reinforced
through mass media where women are far more likely than men to be portrayed as
victims (e.g., Chermak, 1995), even though the most likely victims of crime are men.
From early on, boys learn to internalize fear “in order to retain some semblance of
control and power in relation to others” (Goodey, 1997, p. 410), but girls are socialized
to express emotions. As a consequence, men may be more reticent than women to
disclose fear and often downplay what they do admit to (Smith & Torstensson, 1997).
Newburn and Stanko (1994), however, argued that the assumption that men are reluctant to discuss weakness or vulnerability is largely untested, especially in fear of crime
research. In fact, they stated that studies find men who have been criminally victimized “do admit to being profoundly affected by crime” (p. 160), although the proportion
of men who do so is smaller than that of women.
The tendency for men to minimize feeling fearful has important implications for
fear of crime research. For example, a recent study analyzed gender differences in the
effect of social desirability on survey responses about fear of crime and found that
respondents’ tendencies to give socially desirable answers were negatively correlated
with reported fear of crime, but only among men (Sutton & Farrall, 2005). There was
no relationship between giving socially desirable answers and fear of crime among
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Feminist Criminology 4(4)
women. Importantly, when the tendency to give socially desirable answers was taken
into account, men actually reported higher fear of crime than women.
As provocative as these findings may be, there are several reasons to believe that
women actually have higher fear of crime than men. First, laboratory-based research
found that women report higher levels of fear and manifest more physiological symptoms when presented with fear-inducing stimuli than do men (Bradley, Codispoti,
Sabatinelli, & Lang, 2001; Grossman & Wood, 1993). However, these studies
assessed reactions to general fear, not fear of crime per se. Moreover, psychological
research found that fear of criminal victimization is a transitory reaction to a threatening stimulus that dissipates once the stimulus disappears (Gabriel & Greve, 2003).
Therefore, even if women display more physiological reactions to stimuli than men,
researchers cannot explain why women consistently report higher fear of crime in
survey-based research.
Second, feelings of vulnerability—defined as a person’s perception of their “openness to attack, powerlessness to resist attack, and exposure to traumatic physical (and
probably emotional) consequences if attacked” (Skogan & Maxfield, 1981, p. 69)—is
higher among women than men. Feelings of vulnerability result from the perception
that one would suffer serious consequences if criminally victimized (Garofalo, 1979;
Killias, 1990). The logical derivation is that one who feels vulnerable to personal victimization should have higher fear of crime than someone who feels less vulnerable,
all else being equal. Consequently, women, by virtue of their smaller physical stature
vis-à-vis men, are reported to have higher fear of crime because they feel more physically vulnerable. The problem is that because some studies of gender variation in fear
of crime do not control for physical vulnerability, they may be overestimating the difference in emotive fear of crime between women and men.
Third, as Stanko (1992) and others argued, women’s fear of crime is a rational
response to the high levels of violence perpetrated by men against women, most of
which is interpersonal in nature and not reported to the police. In short, although
feminist criminologists may take issue with women’s fear of crime being labeled as
irrational, they do not dispute that women’s fear is higher than men’s. This line of
reasoning argues that in the private realm women are much more likely to be victimized by their intimate partners than are men. Indeed, studies using more sensitive
survey items and/or qualitative techniques document that women are victimized with
greater frequency than previously suspected (e.g., Bilsky & Wetzels, 1997).1 In the
public realm, women are regularly subjected to sexual harassment and other forms of
degradation, in addition to the very real danger of sexual violence, which heightens
their fear of other types of criminal victimization (Ferraro, 1996; Junger, 1987).
A number of qualitative and quantitative studies have found that women’s fear
appears to be driven by their fear of rape and/or sexual assault (e.g., Ferraro, 1996;
Fisher & Sloan, 2003; LaGrange & Ferraro, 1989; Madriz, 1997; Meyer & Post, 2006;
Stanko, 1992). For example, Ferraro (1996) found that fear of sexual assault significantly impacted women’s fear of other types of crime; in fact, this effect was larger
than the effect of perceived risk of violent crime victimization. However, because his
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363
analysis was not broken out by gender, the extent to which the findings would be different for men and women was not assessed. Furthermore, a handful of studies have
found that women’s fear of nonsexual crimes is much higher than men’s when it
involves face-to-face confrontation with an offender. These studies suggest that women’s fear of these types of crime centers on the potential for sexual victimization;
(Fisher & Sloan, 2003; Williams & Konrad, 2004)—what Ferraro (1996) referred to
as the shadow of sexual assault hypothesis.
Yet, as Pain (1997) noted, “harassment and violence are very different experiences,
and most women draw a distinct division between them” (p. 300). She asked women to
identify their perceptions of the most likely rapist “in general,” and the most likely
person to rape them. Although a large percentage of women correctly identified the
most likely rapist as a friend or acquaintance, fewer than 10% believed that one of their
friends, acquaintances, or family members could rape them. She argued that women’s
fear is only associated with certain public spaces and men who are strangers. Most of
the women she interviewed felt confident in their ability to avoid criminal victimization
from strangers by avoiding potentially dangerous places and/or situations and by being
able to quickly assess the character of new men who they encountered.
Similarly, Hollander’s (2001) analysis of discussions of fear of crime among focus
group members found that women routinely engage in precautionary behaviors to
avoid criminal victimization in their daily life, but men seldom take such actions.
Other qualitative studies also found that women regularly assess their environment for
potential victimization in their daily activities (Stanko, 1990). Thus, studies may be
confounding cognitive assessments of risk with actual fear and consequently overestimating women’s fear relative to men’s.
Certainly, there are many reasons to believe that women’s fear of crime is actually
higher than men’s, but there are also many reasons to suspect that this difference may be
overestimated. Although quantitative studies on fear of crime now routinely employ
multiple items to measure the concept, very little research has examined measurement
invariance. Underlying quantitative measurement scaling techniques is the assumption
that the observed items (such as answers to survey questions) used to capture an unobserved concept (such as fear of crime) are in fact doing so equally across groups in a
sample. If group differences are found, they could be due to actual differences on the
latent, unobserved construct or due to systematic error stemming from the way different
groups respond to scale items. Thus, establishing measurement invariance is necessary
for valid interpretation of group differences. Indeed, studies that assume measurement
invariance without testing for it may be confounding real group differences with measurement error, thus potentially overestimating (or underestimating) the real magnitude
of differences between the groups. We argue that establishing measurement invariance
is a necessary first step in any quantitative analyses that compare group differences, but
this is lacking in the literature on gender differences in fear of crime.
To date, only one study has examined measurement invariance of fear of crime
between men and women. Pleysier, Vervaeke, and Goethals (2004) examined the
factor loadings of a four-item scale that measured fear of crime among Belgian
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Feminist Criminology 4(4)
respondents and found no difference between men and women. However, three of the
four items used tapped into feelings of safety and vulnerability, not emotive fear per
se. Specifically, respondents were asked to assess how frequently they felt unsafe in
their neighborhood, felt safe in their home, and how frequently “the idea that something could happen to me makes me feel limited in my freedom of movement.” Only
one item directly referred to fear (“I’m afraid of being robbed in the streets”), and this
was the only item that involved the potential for face-to-face confrontation with an
offender. Moreover, the fear of crime scale did not include a fear of sexual assault
item, which is a critical omission as so many studies of gender differences in fear of
crime suggest that women’s fear is largely driven by their fear of sexual assault (e.g.,
Ferrarro, 1996; Stanko, 1992; Warr, 1984).
If, as much research suggests, women’s fear of sexual assault spills over to fear of
other crimes with the potential for physical harm, but this spill-over process does not
occur among men, then we would expect the measurement models to differ by gender.
This hypothesis suggests that the pattern of correlations between fear of sexual assault
and fear of other violent crime items will be higher for women than men, which has
important implications for measurement models that are based on interitem correlations. Of concern is that surveys may not uncover men’s fear of crime because the
predominant indicators used to measure the concept focus on fear of potential physical
harm, which overestimates women’s fears and underestimates men’s. In sum, if the
pattern of correlations of fear of crime items systematically varies by gender, this may
result in measurement variance, thus potentially rendering statistical interpretations of
scales based on such data invalid.
This study will test for differences in the factor loadings of fear of crime items
between men and women. Using structural equations modeling, we compare two
methods of testing measurement invariance on two samples of respondents. We use
a standard two-groups structural equation model (SEM) for testing measurement
invariance and introduce a novel method for testing measurement invariance based
on SEMs. This latter approach will allow us to estimate the size of the effect of
measurement invariance on the estimation of group differences. Specifically, we can
compare the gender effect on the latent outcome (fear of crime) by allowing for
measurement invariance across groups and forcing measurement models to be
equivalent across groups. A comparison of these two models will give us some idea
about the nature and size of the effect of measurement invariance on estimates of
gender differences in fear of crime. In addition, the fear of crime factor used in each
sample varies in the number of items, item wording, and forced choice response
categories, which allows us to compare the utility of these different measurement
strategies.
Method and Data
The data used were from two surveys of two different samples, both of which were
collected by academic survey research institutes for two different studies; the first was
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Callanan, Teasdale
365
conducted in 1991 and the second in 1999. Using a computer-assisted telephone interviewing system and random digit dialing, households were contacted and one adult
respondent willing to be interviewed was randomly selected. The first study surveyed
respondents from a random sample of households in four southern California counties
about fear of crime and related topics and was administered in English, Spanish, or
Vietnamese; 1,916 interviews were completed for an overall response rate of 71%
(which is an above average response rate for telephone surveys; Weisberg, Krosnick,
& Brown, 1989).2 The second source of data came from a statewide area probability
sample of California households; respondents were surveyed about their perceptions
of crime and opinions of several crime control policies. Interviews were administered
in English and Spanish; 4,245 completed interviews were collected, for a response rate
of 69.9%.3
Table 1 presents the sociodemographic characteristics of the men and women in
each of the two samples. The southern California sample had more women (57.6%)
than men (42.4%), as did the statewide sample (57.8% and 42.2%, respectively).
There were more Whites in the southern California sample collected in 1991 (approximately 70%) than there were in the statewide sample collected in 1999 (approximately
59%). This finding is primarily accounted for by the stratified sampling procedures
used for the statewide sample designed to capture more Hispanic and African
American respondents, but it probably also reflects the growth of ethnic diversity in
the state. The mean age of men and women in both samples was in the early 40s. The
educational attainment did not significantly differ across gender in either sample. Of
interest, there were more respondents with bachelor’s degrees in 1999 than in 1991,
reflective of national trends. Household incomes were lower for women than for men
in both years; modal and median levels of household income were higher in 1999
than in 1991.4
Measurement of Fear of Crime
The southern California sample measured fear of crime with four questions: (a) “I
worry a great deal about my personal safety from crime and criminals,” (b) “I worry a
great deal about the safety of my loved ones from crime and criminals,” (c) “When I
am away from home, I worry about the safety of my property,” and (d) “There is
reason to be afraid of becoming a victim of crime in my community.” All items were
measured on a 6-point Likert-type scale, ranging from 1 (strongly disagree) to 6
(strongly agree). The statewide California sample measured fear of crime with questions that asked how fearful a respondent was of eight different property and violent
crimes on a scale of 0 (not at all fearful) to 10 (very fearful). Specifically, respondents
were asked how fearful they were of (a) being burglarized while at home, (b) being
burglarized while away, (c) being attacked with a weapon, (d) auto theft, (e) being
robbed or mugged, (f) being sexually assaulted or raped, and (g) having their property
vandalized. The eighth question asked respondents how fearful they were of their
loved ones becoming a victim of crime.
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Feminist Criminology 4(4)
Table 1. Sociodemographic Characteristics of the Samples by Gender
Southern California
sample (collected in 1991)
Women
(n = 1,079)
Men
(n = 794)
Statewide sample
(collected in 1999)
Women
(n = 2,454)
Race/ethnicity
White
72.7%
68.8%
59.3%
Latino
13.2%
15.6%
19.1%
African American
7.3%
6.2%
10.3%
Asian
4.1%
6.0%
6.3%
Other
1.7%
2.5%
2.5%
Age (M)
43.4
40.7
45.9
Education
Not a high school
8.6%
7.5%
8.8%
graduate
High school graduate
20.1%
15.2%
21.3%
Some college or
43.9%
40.7%
36.6%
associate’s degree
Bachelor’s degree
15.6%
18.9%
21.5%
Advanced education
11.7%
17.4%
11.3%
or degree
Household income
Modal category
Less than
US$25,000-
US$35,000-
US$25,000
US$39,999
US$49,999
Median category
US$25,000-
US$40,000-
US$35,000-
US$39,999
US$59,999
US$49,999
Language of interview
English
94.5%
96.5%
99.0%
Spanish
0.9%
1.1%
5.5%
0.1%
None
None
Other
Men
(n = 1,791)
58.3%
17.3%
10.2%
8.8%
2.7%
44.7
6.5%
17.3%
34.8%
25.3%
15.2%
US$50,000US$74,999
US$35,000US$49,999
98.9%
3.5%
None
Data Analyses
We began by comparing difference in means between men and women on the observed
fear of crime items. Next, we conducted exploratory factor analyses to ascertain that
the items measured only one latent construct (fear of crime). We then estimated a twogroups confirmatory factor analysis to test for measurement invariance of the items
across gender. Last, we tested for differential item function using SEMs, an approach
that has not been used before in the fear of crime research.
To test for differential item function, we estimated a SEM where the independent
variable (gender) was allowed to predict our latent dependent variable (fear of
crime). This is represented in Figure 1 as the path labeled “a.” We also allowed
gender to predict the observed items that measure the latent construct. These paths
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Callanan, Teasdale
367
are represented by the paths labeled “b” in Figure 1. Note that Path “a” represents
the effect of gender on fear of crime, whereas all of the paths labeled “b” represent
differential item functioning. That is, where Path “b” is significant, the item has a
different factor loading for men than women. Note that in Figure 1, some paths
between gender and observed items are omitted. In this case, the factor loading for
that item is constrained equal across gender. This differs from conventional twogroup approaches in that we now get an estimate of the effect of gender on the latent
construct, whereas the two-group model is stratified by gender; thus, a gender effect
must be computed based on the intercepts. In addition, a comparison of models that
allow paths labeled “b” and a model that omits all of the paths labeled “b” permitted
us to estimate the size of the effect of measurement invariance on our substantive
relationship of interest (the relationship between gender and fear of crime). This
approach allowed us to estimate whether failure to take into account measurement
invariance overestimates or underestimates gender differences. We argue this is an
important contribution of the current investigation, as substantive researchers can
gauge how much the gender difference in fear of crime is over- or underestimated
by not accounting for measurement invariance.
Results
For ease of interpretation, the results are presented separately for each sample. We
begin with the southern California sample.
Southern California Sample
Presented in Table 2, mean responses to the fear items ranged from a low of 3.14 to a
high of 4.07 (on a scale of 1 to 6). Women ranked worry about the safety of their property lowest, whereas men worried least about their personal safety. Of the four items,
only one (worry about personal safety) was statistically significantly different for men
and women (t value = 3.77).
The exploratory factor analysis suggested a single factor fit the data. The eigenvalue was 2.310 for the first factor and 0.664 for the second. Consequently, we
proceeded with our two-groups analyses using a single factor to measure fear of crime.
As seen in Table 3, the two-groups model fit the data well as demonstrated by the
fit indices.5 To set the metric of the factor loadings, we used the item that measured
worry about safety of property when the respondent was away from home. This item
was selected because preliminary analyses suggested no gender differences in the
factor loadings on the latent fear of crime construct for this item. Thus, it could reasonably be constrained equal. As long as a single item can be constrained equal across the
groups, models such as those presented in the current study are estimable using structural equation modeling approaches. That is, a reference item must be measured
equivalently across the groups under investigation. This item serves as a point of deviation by which the differences in measurement in the remaining items can be estimated
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Feminist Criminology 4(4)
Figure 1. Structural equation model testing for differential item functioning
Fear of Crime
a
Gender
b
b
b
Note: Paths labeled “b” represent factor loadings for items that are allowed to vary by gender. Note that
some observed items do not have direct paths from gender. In these cases, the factor loading for the
observed item is constrained equal across gender. The path labeled “a” represents the effect of gender
on the latent fear of crime variable.
Table 2. Comparisons of Means Between Females and Males for Fear of Crime Items
Fear items
Females
Males
Southern California sample
Safety of property
3.14
3.24
Personal safety***
3.40
3.14
Safety of loved ones
4.07
4.07
Afraid in neighborhood
3.58
3.49
Statewide California sample
Home broken into while away***
3.92
3.43
Home broken into while at home***
2.94
1.90
Attacked with weapon***
3.74
2.71
Raped or sexually assaulted***
3.56
0.93
Car stolen***
3.67
3.19
3.68
2.55
Robbed or mugged***
Property vandalized***
3.49
3.06
Safety of loved ones***
5.57
4.74
t value
1.41
3.77
0.04
1.21
5.28
12.00
11.06
30.32
4.93
12.51
4.47
7.75
***p ≤ .001.
across groups. Without such an item that can be constrained across the groups, these
models are not estimable using SEMs.
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Callanan, Teasdale
Table 3. Two-Groups Measurement Models for Men and Women
Standardized factor loadings
Women
Men
Southern California sample
Safety of property
.627
.674
Personal safety
.798
.682
.740
.619
Safety of loved ones
Afraid in neighborhood
.525
.588
Goodness of fit: CFI = .98; TLI = .97
Statewide California sample
Home broken into while away
.789
.757
Home broken into while at home
.835
.751
Attacked with weapon
.898
.818
Raped/sexually assaulted
.851
.525
Car stolen
.731
.718
Robbed or mugged
.899
.848
Property vandalized
.762
.746
.710
.644
Safety of loved ones
Goodness of fit: CFI = .88; TLI = .86
χ2 difference
(reference)
3.210**
3.227**
0.204
0.505
7.049***
5.261***
19.971***
0.011
4.590***
(reference)
0.692
Note: CFI = comparative fit index; TLI = Tucker–Lewis index.
**p < .01. ***p < .001.
Of the four items used to measure fear of crime, two could not be constrained equal
between men and women.6 These were worry about personal safety and worry about
the safety of loved ones.
Finally, we estimated two models to test for differential item functioning. The first
is a model that does not allow for differential item functioning across gender. That is,
we excluded all paths labeled “b” in Figure 1. Next, we estimated a model that
includes all paths labeled “b” and trimmed the insignificant paths to produce a final
model that allowed some items to have differential item functioning and constrained
others equal. As seen in Table 4, the final model revealed only one item that operated
differently for men and women, which was the item that asked about worries pertaining to personal safety.
A comparison of the fit indices for the model that allows for differential item function
and the model that does not yields a slightly better fit for the model that includes the
paths for differential item functioning. Moreover, the gender effect changes direction
once we take into account the different measurement models for men and women
(note the change in sign between Model 1 and Model 2 of Table 4). Most importantly,
the gender effect was not significantly different from zero in either case.
Statewide California Sample
As Table 2 demonstrates, mean responses across all subjects to the fear items ranged
from a low of 2.44 for sexual assault to a high of 5.22 for the safety of loved ones.
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Table 4. Results From Final Models of Tests for Differential Item Function by Gender
Effects of gender on observed
items and latent fear of crime
Dependent variable
Model 1
b
SE
Model 2
t
b
SE
t
Southern California sample
Safety of property
—
—
Personal safety
—
–0.282*** 0.060 –4.737
Safety of loved ones
—
—
Afraid in neighborhood
—
—
Fear of crime
–0.079
0.052
–1.533
0.014
0.055
0.807
Goodness of fit: CLI = .979; Goodness of fit: CLI = .991;
TLI = .957 TLI = .977
Statewide California
sample
Home broken into
—
—
while away
—
–0.521*** 0.062 –8.395
Home broken into
while at home
Attacked with weapon
—
–0.428*** 0.061 –6.973
Raped/sexually assaulted
—
–2.131*** 0.065 –32.783
Car stolen
—
—
Robbed or mugged
—
–0.536*** 0.058 –9.252
Property vandalized
—
—
Safety of loved ones
—
—
Fear of crime
–0.944*** 0.070 –13.440 –0.513*** 0.076 –6.765
Goodness of fit: CLI = .898; Goodness of fit: CLI = .939;
TLI = .865 TLI = .905
Note: CFI = comparative fit index; TLI = Tucker–Lewis index. The dashes (—) indicate that the paths were
set to zero in our structural equation model. Note that in Model 1, all effects of gender on the observed
items are constrained to zero. In Model 2, only significant effects are presented. As an interim step, we
estimated a model with all of the effects (except our reference category) estimated and then trimmed
nonsignificant paths to produce Model 2.
***p < .001.
There were, however, large gender differences in responses to these items; all were
statistically significant. For example, the mean level of fear of sexual assault among
women was 3.56, but for men it was only 0.93. In general, women’s fear of violent
crimes with the potential for physical harm was much higher than men’s, but the
gender difference was not as great for fear of property crimes.
The exploratory factor analysis suggested a single factor fit the data. The eigenvalue was 5.208 for the first factor and 0.687 for the second. Consequently, we
proceeded with our two-groups analyses using a single factor to measure fear of crime.
Table 3 shows that the fit indices for the two-groups model revealed a relatively
modest fit, which is expected given the large sample size and that only half of the
factor loadings could be constrained equal. Paralleling the analysis of the southern
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Callanan, Teasdale
371
California data, we used the item that measured fear of property being vandalized as a
reference item. This item was selected because preliminary analyses suggested no
gender differences in the factor loadings for this item. Only the items about home
burglaries, car theft, property vandalism, and the safety of loved ones could be constrained equal across men and women. The items that could not be constrained equal
were those that implied potential physical harm—fear of sexual assault, fear of robbery or mugging, fear of assault with a weapon, and fear of home invasion robbery.
Not surprisingly, the item with the largest Wald statistic was the item measuring fear
of sexual assault. Moreover, the standardized loading for the sexual assault item in the
male group is marginal (.525), whereas the loading for the female group is substantially higher (.851).
Finally, we estimated two models to test for differential item functioning. Presented
in Table 4, the first is a model that does not allow for differential item functioning
across gender. That is, we excluded all paths labeled “b” in Figure 1. Next, we estimated a model that included all paths labeled “b” and trimmed the insignificant paths
to produce a final model that allowed some items to have differential item functioning
and constrained others equal. The final model revealed significant differential item
function for the four items that involved fear of violence or potential violence (fear of
sexual assault, fear of robbery or mugging, fear of assault with a weapon, and fear of
having one’s home broken into while at home).
A comparison of the fit indices for the model that allows for differential item function to the model that does not yields a better fit for the model that includes the paths
for differential item functioning. Moreover, the gender effect is significantly reduced
once we take into account the different measurement models for men and women.
Note that the coefficient for gender is reduced by about half—from b = -.944 in the
model with equality constraints (Model 1 of Table 4) to b = -.513 in the model that
allows for differential item functioning (Model 2 of Table 4). This finding implies that
the gender effect on fear of crime may be substantially overestimated (our data indicate by a factor of two) if differential item functioning is not taken into account.
Discussion
This study illustrates not only significant measurement variance between men and
women on fear of crime items but also pinpoints where these differences are. In short,
women’s fear of crime appears significantly heightened with items that measure fear
of crimes with the potential for physical harm. These findings mirror prior substantive
research that suggests women’s fear of crime stems from fear of sexual assault and
other crimes that could involve physical violence. The analyses also suggest that without accounting for measurement variance between men and women, studies may be
significantly overestimating the actual gender difference in fear of crime.
Consistent with the gender and fear of crime literature, our models suggest that women’s fear of crime is greater than men’s; however, our models (presented in Table 4)
suggest that it is the magnitude, not the direction, of this relationship that is at issue.
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Feminist Criminology 4(4)
As we argued previously, the gender difference on the latent fear of crime construct
could be over- or underestimated, when not taking into account differential item function, not that the gender effect would reverse sign (or change direction). Indeed,
consistent with this argument, we found that the gender effect is overestimated by a
multiple of two, when differential item function is ignored.
As previously discussed in the literature review, several factors may account for the
measurement difference reported between men and women. First, men may underreport
their fear of crime, as Sutton and Farrall (2005) have recently found. Second, women may
be more aware of their physical environment and of strangers to avoid potential victimization (e.g., Meyer & Post, 2006), which in turn may drive up their level of fear. Finally,
sensitivity to risk may heighten women’s fear of crime relative to men’s. Warr (1987)
found fear of crime is not only dependent on one’s perception of risk (i.e., the likelihood
of criminal victimization), but it is also dependent on one’s sensitivity to risk (i.e., one’s
perception of the probable physical harm they would suffer as a result of criminal victimization). He found that although sensitivity to risk increases with the perceived seriousness
of the type of criminal victimization, it does not necessarily produce fear of crime if one’s
perception of being at risk for the criminal victimization is low. There are a number of
offenses that people view as being likely to occur together in a single criminal event (such
as burglary leading to assault), which Warr termed contemporaneous offenses. Of relevance, he found that most women (and men) perceive that rape is a contemporaneous
offense for women who are burglarized, robbed, or assaulted. Given his research and
prior studies that support Ferraro’s shadow of sexual assault hypothesis (e.g., Fisher &
Sloan, 2003; Hughes, Marshall, & Sherrill, 2003; Madriz, 1997), women’s fear of criminal victimization likely is driven by their fear of sexual assault.
To assess this possibility, we estimated a measurement model (not shown) that
excluded the fear of sexual assault item but retained all others. In the two-groups
SEM, only one item could not be constrained, which was fear of being broken into
while at home. When we estimated this measurement model and allowed for differential item functioning, two items could not be constrained equal: fear of assault with a
weapon and fear of being broken into while at home. Again the coefficient for gender
is reduced by almost half when we compare the model that includes differential item
function to the model that imposes equality constraints. Thus, women’s fear of crime
was overestimated by a factor of two, even when the fear of sexual assault was
excluded from the measurement model.
These findings support both Warr’s (1987) concept of contemporaneous offenses
and Ferraro’s (1996) shadow of sexual assault hypothesis. It appears that questions
about some crimes, namely, crimes with the potential for physical victimization, activate a differential gender response. In short, women’s fear of some crimes could be
largely driven by their fear that it may lead to physical or sexual assault.
Men and women have different interpretations of crimes that contain the potential
for physical harm. What drives this difference is beyond the scope of this article, but
it is likely linked with women’s greater sense of physical vulnerability (Moore &
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Callanan, Teasdale
373
Shepherd, 2007). Our measurement models suggest that researchers who conduct
quantitative analyses of gender differences in fear of crime cannot simply assume the
measurement models work equivalently for women and men. If studies are measuring
something different in men and women then substantive gender comparisons are
likely to be overemphasized.
We suggest that quantitative models must either control for these measurement differences or simply use measures that have demonstrated measurement invariance. Our
findings suggest that statistical analyses of gender differences in fear of crime should
use items that measure property and altruistic fear, as these items show measurement
invariance across gender in our models. Alternatively, these models are estimable if a
single item can be constrained equal across the two groups. Inclusion of some items
that can be constrained and some items that cannot is appropriate, as we have demonstrated in this article. Importantly, research should separately analyze fear of sexual
assault for men and women as this fear invokes a much different response for women
than for men. Indeed, this item showed the largest measurement variance across
gender in our statistical models (see Tables 3 and 4).
Future research should explore alternative dimensions of “fear of crime,” such as cognitive assessment of risk and behavioral responses to risk, to ascertain if these measures
are invariant across gender. If so, these measures may help us more fully understand the
role that crime salience plays in the everyday lives of both men and women. Although
qualitative research has made considerable headway in gaining insight into women’s fear
of crime, more of the same approach should be employed with male samples. Information
gleaned from such qualitative studies would help us not only more fully understand the
various dimensions in fear of crime but could also be useful in assisting researchers to
construct better quantitative measures that capture important nuances in these dimensions
that vary by gender. More attention needs to be given to issues of measurement invariance, something that largely has been ignored. The findings of the current study also
suggest that criminologists who study group differences, such as age, race, gender, and
social class, should pay particular attention to issues of measurement invariance, as the
magnitude of their effect sizes may depend on the issues raised in the current study.
Acknowledgments
The authors wish to thank the editor, Helen Eigenberg, for her careful editing and helpful comments, and the three anonymous reviewers.
Declaration of Conflicting Interests
The authors declared that they had no conflicts of interests with respect to their authorship or
the publication of this article.
Funding
The authors declared that they received no financial support for their research and/or authorship
of this article.
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Feminist Criminology 4(4)
Notes
1.
2.
3.
4.
Although some suggest these assaults are relatively minor (Smith, 1988).
For additional information about the data see Adams and Serpe (2000).
For additional information about the data see Callanan (2005).
Household income categories differed across the two samples so the mean and modal categories are presented for comparison in Table 1.
5. Analyses were conducted with Mplus software.
6. The decision to constrain a factor loading equal across gender was made based on the Wald
statistic. Significant Wald statistics indicate a significant difference in the factor loading for
an item across gender.
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Bios
Valerie J. Callanan is Assistant Professor in the Department of Sociology in the University of
Akron, OH. Her research interests are public opinion of crime, corrections, fear of crime, media
and crime etc.
Dr. Brent Teasdale is Assistant Professor in the Department of Criminal Justice in the Georgia
State University, GA. His areas of interest are criminological cheory, communities and crime etc.
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