Victimization experience does matter: Testing the effect of different types of victimization on fear of crime among adolescents

Abstract

The aim of the present study is to thoroughly examine the relationship between adolescent fear of crime and a wide variety of offences which commonly affect children. The analysed data comes from the Urban Youth Victimization Survey conducted among 9^th grade students in the Czech Republic. The results unequivocally demonstrate that victimization experience, when measured properly, substantially affects adolescent fear of crime. All analysed types of victimization are associated with fear of crime, though the strongest effects were found for cyber-victimization and bullying rather than conventional violent and property crimes. Furthermore, a poly-victimization scale was revealed to be a highly effective tool for capturing overall victimization by using a single summary measure.

Keywords

adolescence fear of crime victimization poly-victimization

Introduction

Since fear of crime has become an important topic in criminological research, a number of studies have been devised to address the relationship between this phenomenon and various individual and social factors. Initially, it was assumed that fear of crime is the natural result of an individual's real experience with crime. Victimization thus became one of the most obvious predictors of fear of crime in earlier studies (e.g. Garofalo, 1979; Hale, 1996; Skogan, 1987). Further research, however, has shown that the relationship between victimization and fear of crime is far from consistent, both in studies conducted on adults (see e.g. Box et al., 1988; Chadee and Ditton, 2003; LaGrange et al., 1992) and adolescents (e.g. Cops, 2010, 2013; Cops and Pleysier, 2011; May, 2001; May et al., 2015; May and Dunaway, 2000; Schreck and Miller, 2003).

Drawing inspiration from Hale (1996), who attempted to summarize the reasons behind the inconsistent relationship between victimization and fear of crime in adults, we have identified three fundamental issues which contribute to the mixed findings of previous research and which obscure this relationship in adolescents as well: the inadequacy of employed victimization measures; inconsistencies in the measurement of fear of crime; and differences in the variables controlled for in analysis. Whereas the latter two issues are sometimes addressed in research (see e.g. Baron, 2011; May et al., 2002; Melde, 2009; Zani et al., 2001), the shortcomings posed by commonly employed victimization measures are mostly overlooked. Capturing a child's victimization experience is challenging as it covers many diverse incidents which occur in different contexts and are perpetrated by different types of offenders (see e.g. Finkelhor et al., 2005). Nevertheless, the victimization indicators used in the majority of studies on adolescent fear of crime are very basic and generally only include a limited selection of conventional violent and/or property offences (Cops, 2010; May, 2001; May et al., 2002). On the other hand, the few studies that employ more sophisticated victimization measures have largely revealed a strong association between victimization and adolescent fear of crime (De Groof, 2008; Swartz et al., 2011; Wallace and May, 2005).

We assume that victimization experience is likely to substantially impact the levels of adolescent fear of crime. The prevalence of youth victimization is very high, multiple victimization is frequent and the consequences of victimization can be severe (Cyr et al., 2013; Finkelhor et al., 2011; Finkelhor et al., 2013; Turner et al., 2017). Moreover, children have only limited autonomy as they are dependent on their caregivers and obliged to attend school. Avoiding victimization might prove challenging as a result. Overall, children are highly vulnerable and it is particularly their own experience with crime and antisocial behaviour which can considerably increase their awareness of how vulnerable they are to future victimization and, consequently, increase their fear of crime (Baron, 2011; Melde, 2009). The main advantage of the present study is that it allows for a thorough evaluation of the relationship between victimization experience and adolescent fear of crime, as it considers a variety of offences which children are typically victimized by, including bullying as well as victimization in cyberspace.

Inconsistent relationship between victimization and fear of crime

The most notable outcome generated by previous research on the relationship between personal victimization experience and fear of crime is the large variability of findings. Although a number of studies among both adults and adolescents have pointed to a significant relationship between victimization and fear of crime (e.g. Collins, 2016; Farrall et al., 2009; Swartz et al., 2011; Tseloni and Zarafonitou, 2008; Wallace and May, 2005; Wilcox et al., 2005), many authors found it to be weak or even non-existent (Cops, 2010, 2013; Ferraro, 1995; LaGrange et al., 1992; Liska et al., 1988; May et al., 2015; May and Dunaway, 2000; Schreck and Miller, 2003).

Possible reasons behind the high inconsistency in the relationship between victimization and fear of crime have already been outlined in a review study by Hale (1996; see also Agnew, 1985). First, it is attributed to the use of various indicators of fear of crime, especially the preference given to simple “global” measures over batteries of questions targeting worries about specific crimes. Second, he points to differences in victimization measures, with only a limited number of studies focusing on victimization by different types of crime and its incidence. Finally, it is likely that victimization per se represents only one of many factors which influence fear of crime and that the relationship between these phenomena is also shaped by other variables considered in analyses.

Measurement of fear of crime

Fear of crime is a social phenomenon which is not easy to define or measure. According to some researchers (Farrall et al., 1997; Gray et al., 2011), it is a tenuous concept whose operationalization has long been the subject of numerous scientific debates. The main indicator of fear of crime, incorporated in early research in the 1970s, has now become what is generally known as the “global” question about feeling of safety in a neighbourhood (in various permutations). However, this indicator has also become a subject of scathing criticism due to its abstract nature and limited ability to distinguish between fear of crime as an emotional concept and perceived victimization risk as a cognitive concept (Ferraro and LaGrange, 1987; Garofalo, 1979). Subsequently, under the influence of Ferraro and LaGrange (Ferraro, 1995: 23; Ferraro and LaGrange, 1987: 72), fear of crime has begun to be defined as ‘a negative emotional reaction to crime or the symbols associated with crime’ and more complex batteries of questions have been developed, taking into consideration fear of a wide range of crimes, such as assault, theft, burglary, robbery, etc. (Ferraro and LaGrange, 1992; LaGrange and Ferraro, 1989), as well as the difference between its intensity and frequency (see Farrall et al., 1997; Farrall and Gadd, 2004; Yang and Hinkle, 2012). On the other hand, feeling of safety and risk perception—defined as judgments about the likelihood of criminal victimization—have been considered proximal determinants of fear of crime (Ferraro, 1995).

At the same time, some researchers pointed to the importance of the behavioural dimension of fear of crime (Doran and Burgess, 2011; Liska et al., 1988) as it can generate both protective and avoiding behavior. Drawing on these considerations, a different approach—from that introduced by Ferraro and LaGrange—was later advocated by Rader (2004) and Caro and Navarro (2017). These authors suggested that—based on cross-sectional studies, which predominate in fear of crime research—we cannot determine the causal direction of fear of crime, risk perception, neither constraint behaviour and that there is a reciprocal relationship between all these phenomena.

In this study, however, we follow the approach introduced by Ferraro and LaGrange (Ferraro, 1995; Ferraro and LaGrange, 1992; LaGrange and Ferraro, 1989) as the majority of studies on adolescent fear of crime is based on this approach and thus relies on a composite indicator measuring worries about being victimized by specific offences. Nevertheless, the sets of offences that are included are quite diverse across these studies. Some authors consider conventional violent and property offences only (e.g. Baron, 2011; Melde, 2009; Sacco, 1993), whereas others also include offences typical of children, such as harassment by peers (Lane, 2006; Zani et al., 2001). Moreover, many researchers limit their focus on fear of crime at school (e.g. Schreck and Miller, 2003; Swartz et al., 2011; Wilcox et al., 2005). Consequently, different conceptualizations of fear of crime may result in different research findings (e.g. Collins, 2016; May et al., 2002).

Measurement of victimization

Victimization experience can be very heterogeneous in terms of offence types, frequency, as well as severity, rendering it unrealistic to capture its full diversity. Nevertheless, research on fear of crime among adults comprises almost exclusively victimization by violent and property crime and often has a limited focus on a few selected offences. Furthermore, a simple indicator of the prevalence of victimization during the previous year(s) is employed in the majority of recent studies (e.g. Hummelsheim et al., 2011; Vauclair and Bratanova, 2017; Visser et al., 2013). Studies which do examine specific types of victimization are rare, often use summation indices of property and violent crime victimization and fail to produce consistent findings either (e.g. Miethe and Lee, 1984; Skogan, 1987; Skogan and Maxfield, 1981).

Whereas victimization rates among adults are not particularly high—at least in terms of the conventional violent and property offences commonly captured in fear of crime studies—victim surveys conducted on children demonstrate a very high prevalence of overall victimization and a relatively high concurrence of victimization by different types of offences (e.g. Cyr et al., 2013; Finkelhor et al., 2011). For instance, a national survey from the U.S. revealed that 69% of children were victimized during the previous year and that the mean number of reported offence types was 2.4 (Finkelhor et al., 2009). Despite the large extent and variety of youth victimization, research on adolescent fear of crime—similarly to adult surveys—often uses a simple binary measure indicating the prevalence of victimization in the previous year, usually comprising conventional violent or property crimes exclusively (e.g. Cops, 2010; Cops and Pleysier, 2011; Wilcox et al., 2005). Only a few studies have employed victimization measures that attempt to capture previous year incidence for a wider variety of offences, sometimes also including “non-conventional” offences such as verbal aggression or bullying (De Groof, 2008; Swartz et al., 2011; Wallace and May, 2005). Such studies have revealed a substantial association between victimization and fear of crime. The importance of including victimization by non-conventional offences—a common issue among adolescents—is further confirmed by studies which exclusively analyse the relationship between bullying or cyber-victimization and fear of crime, generally revealing a positive association between these phenomena (e.g. Baek et al., 2019; Keith, 2018; Vidourek et al., 2016; Virtanen, 2017).

Apart from direct victimization, indirect experience with crime is sometimes also considered in fear of crime research. Studies focusing on adults suggest that vicarious experience with crime through people with whom an individual is in frequent contact, that is family members, friends, neighbours etc. (Skogan and Maxfield, 1981; Taylor and Hale, 1986), or through the media (Callanan, 2012; Gerbner and Gross, 1976; Hale, 1996), might increase fear of crime. With respect to children, indirect experience with crime is mostly reflected in terms of witnessing violence, either at school, in the community, or at home (see e.g. Finkelhor et al., 2005). However, such indicators are scarcely considered in studies on adolescent fear of crime (see e.g. Baron, 2011; Hartless et al., 1995).

Overall, the measurement of victimization in adolescent fear of crime research is in large part simplistic and rarely attempts to reflect the diversity of victimization experiences. Nevertheless, employing multiple indicators in order to capture different victimization types poses a challenge, as victimization types are considerably intercorrelated (see e.g. Finkelhor et al., 2015) and outcomes of multivariate analyses are thus affected by multicollinearity. One possible solution is to employ a variety index that counts the number of different offences by which the person has been victimized (i.e. poly-victimization). This approach has already proven fruitful outside of the fear of crime field, for instance when studying psychological distress as a consequence of victimization (e.g. Finkelhor et al., 2007b; Finkelhor et al., 2009). Moreover, there is evidence that children with a high degree of poly-victimization exhibit even more trauma than those who experience repeat episodes of the same type of victimization (Finkelhor et al., 2007a; Turner et al., 2010). However, studies employing the concept of poly-victimization as a determinant of adolescent fear of crime are very limited. To our knowledge, Deakin (2006) has been the only one to use an extensive poly-victimization measure and her analysis revealed a substantial correlation with fear of crime.

Determinants of fear of crime

Previous research on fear of crime has generated various theoretical explanations for differential fear of crime levels, taking diverse individual, social, and situational factors into account. Studies focusing on adults frequently examine—in addition to victimization experience—the influence of gender, age, socio-economic status as well as neighbourhood characteristics (Farrall et al., 2009; Ferraro, 1995; Gainey et al., 2011; Skogan and Maxfield, 1981; Tseloni and Zarafonitou, 2008). According to vulnerability theory (Killias, 1990), people differ in their sensitivity to risk, with women, the elderly and those with lower socio-economic status being more vulnerable than their counterparts. While some studies present rather inconsistent results with respect to the latter two social groups (see e.g. Box et al., 1988; Chadee and Ditton, 2003, Ferraro, 1995), gender proves to be the most consistent determinant of fear of crime. Whereas women do not consider themselves strong enough to resist an attack and assess the consequences of potential victimization to be more serious (Warr, 1984), men often underestimate the risk of being victimized or do not admit their fear (Agnew, 1985).

In addition to factors stemming from the vulnerability approach, the effect of an individual's immediate environment is often also taken into account. Incivility theory (Lewis and Salem, 1986) postulates that fear of crime is a result of social disruption in a given area rather than an individual's experience with crime. A higher incidence of physical (vandalism, abandoned cars, trash and litter) and social (beggars, rowdy youth, inconsiderate neighbours) incivilities is thus found to be associated with higher fear of crime (LaGrange et al., 1992).

Researchers dealing with adolescents often extend their models and also consider other factors relevant to this specific age group, particularly the parenting style of caregivers, leisure patterns and school environment. Existing studies have confirmed that parental concerns can, to a certain degree, be transmitted to their offspring (De Vaus and Wise, 1996). On the other hand, parents who promote their children's engagement with meaningful and autonomous leisure activities may partly inhibit their fears (e.g. Cops, 2010, 2013; De Groof, 2008; May et al., 2015, 2002; May and Dunaway, 2000; Podaná and Krulichová, 2018; Wallace and May, 2005; Zani et al., 2001).

The majority of fear of crime research stems from the sociological tradition and does not aspire to explain the psychological mechanisms responsible for generating fear of crime (e.g. Cops et al., 2012; Jackson, 2011; Van der Wurff et al., 1989). A notable exception is an individual's subjective perception of victimization risk, which was even equated with fear of crime in some early studies (e.g. Garofalo, 1979; Liska et al., 1982). Nevertheless, as the distinction between these two concepts has been emphasized and widely accepted (Ferraro and LaGrange, 1987), many studies have revealed that people mainly fear crime because they consider the probability of victimization to be relatively high; consequently, risk perception has been established as a strong proximal factor of fear of crime (Farrall et al., 2009; Ferraro, 1995; Warr, 1984; Warr and Stafford, 1983). Moreover, Ferraro (1995), in his seminal work, suggested that perceived victimization risk can mediate the effects of many individual and social factors, including victimization, onto fear of crime—something which has been subsequently confirmed by other studies (e.g. Farrall et al., 2009; Gainey et al., 2011).

Apart from these findings, there are also approaches that consider the relationship between fear of crime and risk perception as reciprocal (Caro and Navarro, 2017; Liska et al., 1988; Rader, 2004). Nevertheless, we follow the argumentation advocated by Ferraro (1995) and subsequently applied by studies on adolescent fear of crime. Although the mediating role of perceived victimization risk in adolescent samples has only been considered by a few researchers (e.g. Henson et al., 2013; May, 2001; May et al., 2002), in the studies that do not rely on simplistic victimization indicators, the relationship between fear of crime and victimization was always partially or even fully mediated by risk perception (Baron, 2011; Krulichová and Podana, 2019; Melde, 2009).

Current study

The present study strives to thoroughly analyse the relationship between victimization experience and adolescent fear of crime, both from a bivariate and multivariate perspective that takes into consideration other relevant predictors of fear of crime as well. The advantage of this study is that it employs several measures of victimization by different offence types—specifically, violent and property crime, bullying at school, cyber-victimization, and indirect victimization—while also examining the utility of a single poly-victimization measure. Furthermore, two variants of the fear of crime indicator are considered. The first focuses on worries about conventional crimes that typically appear in studies on adults, whereas the second also considers harassment by peers and through modern technology, that is offences common among adolescents. The analysis aims at answering the following research questions:

What is the overall strength of the association between victimization and fear of crime?

Which victimization types have the strongest association with fear of crime?

Can the concept of poly-victimization be effectively employed in fear of crime research?

Is the association between victimization and fear of crime affected by specific offence types that are considered in the fear of crime measure?

Methods

Data

The data used in this study comes from the Urban Youth Victimization Survey (UYVS) conducted in the Czech Republic in 2015. It was a school-based self-report study that surveyed 9^th grade children from schools located in the country's four largest cities. Some sections of the questionnaire were a replication of previous surveys, specifically the International Self-Report Delinquency Study 3 (Enzmann et al., 2018; ISRD3 Working Group, 2013) and a victimization study conducted in Pilsen in 1999, which was a part of a comparative project comprising several European cities.

A random sample of school classes stratified by city and school type was drawn and the survey was administered in class during a lecture. The response rate reached 64% at a school level and 78% at an individual level, which was mainly attributed to the absence of students during the day of data collection. Overall, the sample included 1546¹ children from 85 classes across 69 schools (54 middle schools and 15 grammar schools). The average age of the children was 15.0 and females comprised 50% of the sample.

Measures

Fear of crime and risk perception

Fear of crime was measured by asking how often adolescents feared that each of the seven offences could happen to them (for the exact phrasing of items, see the Supplement).² In the subsequent analyses, two indicators of fear of crime are utilized: the fear of crime indicator is counted as the mean of all seven items, whereas fear of conventional crime excludes humiliation by peers and cyber-bullying and corresponds to indicators typically used for adult populations. Perceived risk of victimization was evaluated using the same seven items as for fear of crime, and required respondents to estimate the probability of being victimized by such offences within the following 12 months (scale range: 1 = 5%, improbable; 5 = 95%, probable). Similarly to fear of crime measures, two variants of the risk perception indicator were constructed: one considering all seven items and the other reduced to five conventional crime items. The reliability of all four fear of crime and risk perception indices is good (Cronbach α > .75). A logarithmic transformation was applied to adjust for the high skewness of distributions.

Victimization

Direct victimization was measured using the frequency of recent experiences with 13 offences which can be divided into four victimization types (for details, see the Supplement). Victimization by conventional crime, that is violent offences and property offences, were counted as sums of the respective offences. Furthermore, the frequency of victimization by bullying at school and cyber-victimization were measured using 5-point scales and the outcome variables were computed as a mean of the respective items. Indirect victimization was calculated as a mean of three scales, evaluating the frequency of witnessing violence in three different contexts: 1) intimate partner violence among parents at home; 2) bullying at school; 3) violence in neighbourhood (for details, see the Supplement). A logarithmic transformation was applied to adjust for the high skewness of all five victimization measures. Finally, a poly-victimization variable was constructed by dichotomizing all 16 individual direct and indirect victimization items (0 = no experience; 1 = some experience) and summing them up.

Control variables

The control variables include 11 individual, social, and environmental characteristics often viewed as factors which affect levels of adolescent fear of crime (e.g. Krulichová and Podana, 2019; May et al., 2015, 2002; May and Dunaway, 2000; Podaná and Krulichová, 2018; Wallace and May, 2005). Gender and minority status are both binary indicators, with “male” and “ethnic minority” coded as 1 and “female” and “ethnic majority” as 0. Subjective family income captures the family's financial situation when compared to other families the child knows, measured on a 7-point scale (1 = much worse; 7 = much better). School type distinguishes between standard middle schools (coded 1) and academically oriented grammar schools (coded 0). School disorder is a scale constructed out of four items indicating the occurrence of crime (stealing, fighting, vandalism) and drug use at school and neighbourhood disorder was designed using six items capturing crime, drug selling, fighting and disorder (abandoned buildings, littering, and graffiti) in the child's neighbourhood (all original items are measured on 4-point scales). Higher values mark higher disorder. The reliability of both scales is good (α > .74).

Furthermore, research on fear of crime specifically targeted towards the adolescent population often stresses the influence of parents and leisure time activities (e.g. Cops, 2010, 2013; De Groof, 2008; Zani et al., 2001). Indices of parental supervision and autonomy support from parents are thus included. The former variable consists of six items capturing the degree to which parents are aware of their children's whereabouts when they go out, whether or not they set a curfew etc.; and the latter four items indicating the extent to which parents trust their children, respect their opinions, include them in the decision-making process and treat them like adults. All items are measured using 5-point scales with higher values indicating higher supervision and higher support of autonomy (α > .72). Finally, three aspects of leisure time are considered. The amount of time spent on unstructured activities in public without supervision is evaluated using three items focusing on the frequency of the following leisure time activities: going to bars, pubs or music clubs; hanging out in the streets, parks, or the neighbourhood just for fun; hanging out in shopping centres just for fun; all are measured using a 5-point scale (1 = never; 5 = very often). The extent of daily internet use is measured using a 7-point scale (1 = no/almost no time; 7 = more than 5 h). Lastly, social isolation is a binary indicator identifying adolescents who spend their leisure time alone (coded 1) in contrast to those who spend their leisure time in the company of friends or parents (coded 0).

Analytic strategy

The analysis proceeds in three stages. First, bivariate correlations between fear of crime, risk perception and all victimization variables are examined. Second, a hierarchical OLS regression is employed to evaluate the overall contribution of all five types of victimization—violent offences, property offences, bullying, cyber-victimization, and witnessing violence—in order to explain the variance in fear of crime. The first step entails estimating a model including eleven control variables, that is factors usually researched in relation to fear of crime. The next step involves incorporating the set of victimization variables. This allows for an evaluation of the overall net effect of victimization on fear of crime based on the increase in R² and also allows us to examine the direct effects of different victimization types; nevertheless, they must be interpreted cautiously due to their expected high intercorrelation. The last step involves incorporating the risk perception variable as a proximal factor which affects fear of crime and is likely to mediate the effects of the other variables (Baron, 2011; Ferraro, 1995; Melde, 2009). Third, the same set of hierarchical regression models is estimated, though the block of five victimization variables is replaced with a single poly-victimization index and the corresponding models are then compared. All of the outlined multivariate analyses are conducted separately for fear of crime and fear of conventional crime variables. The statistical software STATA and its survey data analysis procedures are used, taking the clustered sampling design into account.

Results

Univariate and bivariate analysis

Descriptive statistics for all variables are presented in table 1. The bivariate correlations between fear of crime, risk perception and victimization variables reported in table 2 show a strong association between both fear of crime scales as well as both risk perception scales (r > .91); furthermore, there is a considerable correlation between the corresponding fear of crime and risk perception scales (r = .62 for the full scales and r = .58 for conventional offences). All victimization variables are significantly associated with fear of crime: both cyber-victimization and poly-victimization exhibit substantial correlations (r > .41), though the other victimization variables show a weaker association (ranging from r = .28 for bullying at school, to r = .17 for property offences). The association with fear of conventional crime reveals a similar pattern; though the strength of the association is somewhat weaker, especially in the case of bullying, cyber-victimization, and poly-victimization. With respect to the association between victimization variables and risk perception scales, the correlations are often somewhat stronger compared to those associated with fear of crime, though a similar pattern is apparent regardless. Lastly, all five specific victimization types are intercorrelated and also exhibit a strong association with poly-victimization.

Table 1.

Descriptive statistics.

	Mean	SD	Min	Max	valid N
Fear of crime	.51	.30	0	1.31	1544
Fear of conventional crime	.47	.31	0	1.39	1544
Risk perception	.40	.33	0	1.52	1533
Risk perception (conventional crime)	.33	.33	0	1.61	1531
Witnessing violence	.42	.28	0	1.61	1531
Victimization – violent crime	.19	.47	0	3.99	1536
Victimization – property crime	.27	.48	0	3.66	1529
Victimization – bullying at school	.29	.33	0	1.61	1533
Cyber-victimization	.37	.37	0	1.61	1534
Poly-victimization	4.10	2.53	0	15	1526
Gender	.50	.50	0	1	1536
Minority status	.10	.30	0	1	1530
Family income	4.68	1.16	1	7	1528
School type	.74	.44	0	1	1546
School disorder	1.80	.62	1	4	1535
Neighbourhood disorder	1.68	.63	1	4	1546
Unstructured activities in public	2.06	.76	1	5	1521
Internet use	4.33	1.62	1	7	1518
Social isolation	.18	.38	0	1	1500
Parental supervision	3.96	.80	1	5	1532
Autonomy support from parents	3.73	.79	1	5	1513

Abbreviations: SD = standard deviation; Min = minimum; Max = maximum.

Table 2.

Bivariate Pearson correlations (N = 1546).

Variable	1	2	3	4	5	6	7	8	9	10
(1) Fear of crime	1
(2) Fear of conventional crime	.924	1
(3) Risk perception	.621	.548	1
(4) Risk perc. – convent. crime	.550	.579	.914	1
(5) Witnessing violence	.206	.164	.269	.234	1
(6) Violent crime	.215	.177	.239	.208	.297	1
(7) Property crime	.172	.156	.219	.210	.249	.289	1
(8) Bullying at school	.278	.193	.348	.247	.522	.329	.280	1
(9) Cyber-victimization	.415	.345	.403	.320	.352	.323	.194	.398	1
(10) Poly-victimization	.410	.333	.427	.345	.641	.516	.465	.716	.709	1

Note: All coefficients are statistically significant (p < .001, two-tailed).

Multivariate analysis

The results of the hierarchical regression analysis are presented in table 3. The baseline Model 1, which includes the individual and social factors usually associated with fear of crime, explains 19.7% of the variance in fear of crime, with gender having the greatest effect. The inclusion of five victimization variables in Model 2 is associated with a substantial increase in R² (by 11.3 percentage points); furthermore, despite the relatively high intercorrelation of the victimization variables (VIF values in the 1.2–1.7 range), three of them show a significant direct effect, namely cyber-victimization (β = .21), bullying (β = .17) and violent offences (β = .08). Finally, Model 3 takes perceived victimization risk into account and confirms its strong effect on fear of crime. Despite the inclusion of this variable in the model, the effects of cyber-victimization (β = .11) as well as bullying (β = .07) remain statistically significant. The corresponding set of regression models for fear of conventional crime (Models 4–6) yields generally similar results, with only a few notable deviations. First, all models exhibit lower levels of explained variance. In the case of Model 4, this is particularly attributed to the lower effect of gender, parenting-related factors and social isolation. Second, the overall contribution of victimization to the explained variance is lower as well, though it still accounts for an additional 6.5 percentage points (Model 5). The only moderate direct effect was revealed for cyber-victimization (β = .19); the effects of the other direct victimization variables, though significant (including property offences in this case), are weak. Last, cyber-victimization is the only variable whose direct effect remained significant when risk perception was controlled for in Model 6.

Table 3.

OLS regression models for fear of crime (models 1–3) and fear of conventional crime (models 4–6).

Variable	Fear of crime			Fear of conventional crime
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Gender	–.323***	–.279***	–.220***	–.284***	–.242***	–.186***
Minority status	–.050*	–.060**	–.055**	–.035	–.044	–.055**
Family income	–.071**	–.080**	–.061**	–.055*	–.061*	–.055**
School type	–.046	–.047	–.051*	–.064*	–.063*	–.059*
School disorder	.100**	.023	.011	.114***	.059*	.024
Neighbourhood disorder	.041	.015	.011	.039	.017	.006
Unstructured activities in public	.073**	–.007	–.010	.079**	.016	–.004
Internet use	.074**	.017	.004	.060*	.017	.012
Social isolation	.099***	.068**	.035	.064*	.042	.023
Parental supervision	.102**	.097***	.068**	.084**	.080**	.057*
Autonomy support from parents	–.119***	–.068*	–.032	–.076*	–.040	–.016
Witnessing violence		.020	.001		.012	–.009
Violent offences		.081**	.046		.060*	.031
Property offences		.047	.008		.060*	.010
Bullying at school		.165***	.068**		.083*	.035
Cyber-victimization		.212***	.110***		.185***	.110***
Risk perception^a			.482***			.496***
R ²	.197	.310	.482	.149	.214	.417
BIC^b	330.7	156.1	−234.0	564.8	492.3	86.9
N	1383	1383	1383	1381	1381	1381

Note: Standardized coefficients displayed (unstandardized coefficients and their standard errors are reported in online supplemental table S1).

The variable restricted to 5 conventional crimes is used in Model 6.

BIC computed for models which do not control for clustering.

*p < .05; **p < .01; ***p < .001 (two-tailed).

The following analysis examines the results of the same set or hierarchical regression models and replaces the five victimization variables with a single poly-victimization index (table 4). We use the suffix “p” to denote the models in table 4 which correspond to the models from table 3 (the baseline models that do not include victimization variables and risk perception are identical in both tables). The results indicate that poly-victimization has a considerable direct effect on both fear of crime (β = .37) and fear of conventional crime (β = .28) and that these effects remain significant even when controlling for perceived victimization risk. All four models which include poly-victimization are highly comparable to the corresponding table 3 models, reaching almost identical R² values, and, based on the BIC values, can even be assessed as the superior (more parsimonious) models.

Table 4.

OLS regression models for fear of crime (models 1–3p) and fear of conventional crime (models 4–6p) including poly-victimization scale.

Variable	Fear of crime			Fear of conventional crime
	Model 1p	Model 2p	Model 3p	Model 4p	Model 5p	Model 6p
Gender	–.323***	–.286***	–.224***	–.284***	–.255***	–.197***
Minority status	–.050*	–.062**	–.056***	–.035	–.044	–.056**
Family income	–.071**	–.067**	–.056**	–.055*	–.051*	–.051*
School type	–.046	–.043	–.049*	–.064*	–.062*	–.058*
School disorder	.100**	.029	.008	.114***	.059*	.017
Neighbourhood disorder	.041	.002	.002	.039	.008	–.003
Unstructured activities in public	.073**	–.007	–.013	.079**	.017	–.007
Internet use	.074**	.023	.004	.060*	.020	.012
Social isolation	.099***	.077***	.038	.064*	.047	.024
Parental supervision	.102**	.096***	.069**	.084**	.080**	.059*
Autonomy support from parents	–.119***	–.064*	–.026	–.076*	–.034	–.010
Poly-victimization		.366***	.182***		.283***	.148***
Risk perception^a			.487***			.496***
R ²	.197	.300	.484	.149	.211	.418
BIC^b	330.7	147.3	−267.3	564.8	468.3	55.7
N	1383	1383	1383	1381	1381	1381

Note: Standardized coefficients displayed (unstandardized coefficients and their standard errors are reported in online supplemental table S2).

The variable restricted to 5 conventional crimes is used in Model 6p.

BIC computed for models which do not control for clustering.

*p < .05; **p < .01; ***p < .001 (two-tailed).

Discussion

Research on the relationship between victimization and fear of crime, both in adults and adolescents, produces considerably conflicting results. While some studies find the association to be relatively strong (Tseloni and Zarafonitou, 2008; Wallace and May, 2005), others reveal a very weak or lacking association between these concepts (e.g. Cops, 2010; Ferraro, 1995; May et al., 2015). The matter is further complicated by the fact that victimization is a complex phenomenon that comprises many diverse acts which occur in different contexts, rendering it highly challenging to measure. The great advantage of this study is that it enabled us to capture the frequency of recent victimization by 16 specific offences which were categorized using four direct victimization types—violent offences, property offences, bullying at school, and cyber-victimization—as well as one indirect victimization type, namely witnessing violence. This also allowed for the construction of a poly-victimization scale and evaluation of its utility. The results of the analysis have demonstrated that victimization experience, when measured adequately, is substantially associated with adolescent fear of crime.

All five victimization types revealed to be significantly correlated with fear of crime. However, it was neither conventional offences nor indirect victimization, but bullying at school and cyber-victimization which had the strongest association with fear of crime, both in the bivariate and multivariate analysis. Moreover, the same was true of the results for fear of conventional crime, which did not specifically include worries about these victimization types. Negative experiences in cyberspace are very frequent among children (e.g. Mitchell et al., 2007) and exhibit somewhat different characteristics compared to “ordinary” offline offences. Specifically, cyberspace lends greater anonymity to offenders and essentially provides unlimited opportunities to attack (Koops, 2010). Similarly, bullying victimization is common among children and often comprises hidden and long-lasting abuse (e.g. Smokowski and Kopasz, 2005). As both cyber-victimization and bullying are difficult to prevent by victims and can easily reoccur, their adverse impact might be more pronounced. This combination of a perceived high re-victimization risk and perceived low control over the situation, that is high perceived vulnerability to crime, was suggested to be particularly fear inducing (e.g. Jackson, 2011; Killias, 1990).

Similarly to previous research (e.g. Finkelhor et al., 2015), the five victimization types included in our study were found to be intercorrelated; furthermore, several of them revealed a significant positive direct effect on fear of crime in the multivariate analysis. This implies that the effect of victimization is likely to be cumulative and the variability of victimization experience, that is poly-victimization, might be particularly important. Our findings confirmed that poly-victimization was substantially associated with fear of crime and, moreover, the results of the analysis using the poly-victimization scale were highly comparable to those using the frequency of the five victimization types. Therefore, our study suggests that the poly-victimization scale might be effectively used as a single summary measure of overall victimization experience in fear of crime research. However, further research is needed to support this finding and examine other possible constructions of summary victimization indices.

Finally, this study has offered a unique comparison of outcomes for two fear of crime scales: the traditional scale, which focuses on conventional offences and is frequently employed in research among adults; and an extended scale, which includes fear of verbal assault by peers and in cyber-space, that is commonly occurring offences among adolescents. The results demonstrated that both scales have good reliability and the extended scale showed a somewhat stronger association with victimization as well as several other factors related to parenting style or leisure time activities—that is factors hypothesized to influence the level of adolescent fear of crime (De Groof, 2008; Wallace and May, 2005; Zani et al., 2001); consequently, the models for the extended scale explained a greater part of the variance in fear of crime. It thus seems beneficial to expand the fear of crime scale and include worries about less severe offences, as they are particularly relevant for adolescents and, in our opinion, such a scale also better captures the “adolescent fear of crime” concept.

This study has bolstered the proposition that victimization experience is substantially associated with fear of crime. Nevertheless, it entails certain limitations which should not be overlooked. Primarily, the analysed sample is limited to urban adolescents from the 9^th grade, that is children about 15 years of age. It is plausible that the nature of the relationship between victimization and fear of crime might be age-dependent, both in terms of its strength as well as significance for different victimization types. The validity of results for other age groups thus needs to be confirmed by further research. Second, our study concentrates on personal fear of crime, that is fear for oneself, and does not analyse altruistic fear for others; this concept has also been considered a relevant dimension of fear of crime by some studies (Snedker, 2006; Warr and Ellison, 2000). Third, although the survey comprised 16 major victimization types, it is not an exhaustive list and other types of victimization, for example dating violence, would be worth examining in future research. Furthermore, attention was paid only to recent victimization experiences. It is likely that less recent incidents might still influence fear of crime, especially if they were of a severe nature (see e.g. Beitchman et al., 1992). Finally, our study comprised two fear of crime indices but did not analyse fear of specific offences. A more detailed analysis focusing on the link between victimization by a specific offence and fear of revictimization by this offence and fear of crime in general should be conducted by future research.

Conclusion

This study has produced several important findings which also have implications for future fear of crime research among adolescents and possibly among adults as well. Perhaps most importantly, the study has demonstrated the significance of how victimization experience is measured and which offences are considered in research. The traditional and widely used approach, which focuses solely on conventional, that is violent and property, crime, does not seem to be adequate in adolescent fear of crime research, given that victimization by other offences also proved to be relevant and even more fear inducing. Victimization experience is a complex phenomenon and researchers should strive to capture its diversity in order to obtain valid results. Although our findings are pertinent to adolescents, it is plausible that a similar argument would also hold in the case of adults and that the consideration of other offences that are common but not conventional, such as harassment, hate crime, or cybercrime, would be beneficial.

Furthermore, our conclusion that the poly-victimization index might be effectively used as an alternative solution to the indicators measuring the frequency of victimization in order to examine the relationship between victimization and fear of crime is significant from a methodological and analytical point of view. It suggests the importance of focusing on the extent, in terms of diversity, of victimization experience and that the incidence of one particular offence might be less relevant (see e.g. Finkelhor et al., 2007a). If the utility of the poly-victimization index is confirmed by further research, victimization experience can be measured by a set of questions on the previous year prevalence of different victimization types instead of incidence, which would not only be less time consuming, but also less cognitively demanding for respondents. Moreover, capturing victimization experience using a single index is both parsimonious and convenient for advanced statistical analysis, for example when non-linear effects are examined.

Even though the primary focus of this study was victimization experience and its relevance in fear of crime research, the presented analyses have also highlighted another important issue. Alongside the recommendation to consider a wider variety of victimization types, a similar suggestion can be made with respect to offences that children might be worried about. Our results have demonstrated that it is beneficial to include potentially less severe, albeit common, offences in the adolescent fear of crime scale. Analogously, such an extension might prove useful in the case of adults as well and should be subject to future research.

Supplemental Material

sj-docx-1-euc-10.1177_14773708211053829 - Supplemental material for Victimization experience does matter: Testing the effect of different types of victimization on fear of crime among adolescents

Supplemental material, sj-docx-1-euc-10.1177_14773708211053829 for Victimization experience does matter: Testing the effect of different types of victimization on fear of crime among adolescents by Zuzana Podaná and Eva Krulichová in European Journal of Criminology

Supplemental Material

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Supplemental material, sj-docx-2-euc-10.1177_14773708211053829 for Victimization experience does matter: Testing the effect of different types of victimization on fear of crime among adolescents by Zuzana Podaná and Eva Krulichová in European Journal of Criminology

Footnotes

Acknowledgements

Z. Podaná would like to thank the Fulbright Commission for supporting her research stay at the Crimes against Children Research Center (University of New Hampshire, USA) and the Director of the Center, Professor David Finkelhor, for his invaluable feedback on her work.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Eva Krulichová

Supplemental material

Supplemental material for this article is available online.

Notes

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