Violent Victimization Across the Life Course

Abstract

The present study examines violent victimization patterns across the life course and outlines a victim careers agenda for future scholarly inquiry. I analyzed four waves of data from the National Longitudinal Study of Adolescent Health to examine whether violent victimization prevalence, onset, and persistence during earlier stages of the life course can predict violent victimization risk in adulthood, and whether these relationships are observed independent of current violent offending. Violent victimization in adolescence was significantly related to subsequent risk in adulthood. Even when current violent offending is controlled, those who report early and persistent violent victimization during prior stages of the life course appear particularly vulnerable to subsequent victimization. The findings demonstrate the importance of moving forward with a victim careers agenda and the present study outlines numerous theoretical and empirical avenues for victimization scholars to pursue.

Keywords

violent victimization life course victim careers

Research on repeat victimization consistently shows that victimization is associated with an increased risk for future victimization (Farrell, 1995). Although most repeat victimization studies focus on property victimization, there is research that suggests such a relationship exists for violent victimization as well (Lauritsen & Quinet, 1995; Ousey, Wilcox, & Brummel, 2008). Repeat victimization research, however, tends to examine victimization experiences over a few months or years; it is unclear whether prior violent victimization is associated with future risk over longer periods of time. Research demonstrates that events occurring in adolescence can have enduring impacts in adulthood (Macmillan, 2001), and in fact, the criminal career paradigm that considers “the longitudinal sequence of crimes committed by an individual offender” (Blumstein, Cohen, Roth, & Visher, 1986, p. 12) has become central to contemporary criminological theory and research (Moffitt, 1993; Sampson & Laub, 1993). Scholars, for example, have noted the importance of early and frequent offending to the continuance of offending across the life course (e.g., Farrington, Lambert, & West, 1998). Few studies, however, have examined violent victimization across life course developmental stages.¹ If in fact violent victimization serves as a predictor for future long-term risk, there are important theoretical and prevention implications to consider. Farrell, Tseloni, Wiersma, and Pease (2001) have argued that research examining “victim careers,” or victimization over the life course, is important to theory and practice and propose that we borrow from the criminal career paradigm to guide this endeavor (see also Averdijk, 2010).

A victim careers perspective should not, however, be explored in isolation from offending. The correlation between victimization and offending, or the victim–offender overlap, is a well-established empirical finding (Berg, Stewart, Schreck, & Simons, 2012; Jennings, Higgins, Tewksbury, Gover, & Piquero, 2010; Jennings, Piquero, & Reingle, 2012; Lauritsen & Laub, 2007; Lauritsen, Sampson, & Laub, 1991; Schreck, Stewart, & Osgood, 2008). It is plausible that criminal behavior might account for repeat victimization over the life course: a criminal lifestyle might repeatedly present opportunities for victimization (Cohen & Felson, 1979; Hindelang, Gottfredson, & Garofalo, 1978), or victimization and offending might have common sources (Gottfredson & Hirschi, 1990; Schreck, 1999). Many repeat victimization studies, however, do not account for criminal behavior, a known risk factor for violent victimization. It is unclear, therefore, whether the relationship between prior victimization and future risk is independent of criminal involvement.

The present study contributes to the repeat victimization literature in two ways. First, using data from four waves of the National Longitudinal Study of Adolescent Health (Add Health), this research draws on concepts from the criminal careers literature to explore violent victimization across the life course. Specifically, I examine whether violent victimization prevalence, onset, and persistence during earlier stages of the life course can predict violent victimization risk in adulthood, and whether these relationships are observed independent of current violent offending. Second, I outline a number of avenues for future scholarly inquiry to stimulate new empirical and theoretical developments in victimization across the life course.

Repeat Victimization

There is a considerable body of empirical research documenting the phenomenon of repeat victimization, with several data sources indicating that a relatively small proportion of the population endures a large proportion of all criminal victimizations (Farrell, 1992). Farrell and Pease (1993), for example, reported the distribution of all offenses in the 1992 British Crime Survey. Not only were more than 80% of all incidents repeat victimizations, but 4.3% of respondents reported five or more victimizations, thus accounting for 43.5% of all incidents. Researchers have persuasively argued that the study of repeat victimization is crucial to understanding and preventing crime. First, failing to examine the distribution of victimizations across individuals can produce misleading descriptions about the nature and risk of victimization (Farrell, Tseloni, & Pease, 2005; Planty & Strom, 2007; Ybarra & Lohr, 2002). Second, because victimization is a good predictor of future victimization, policies directed at the prevention of repeat victimization represent a more effective and defensible allocation of crime prevention resources (Farrell, 1995; Farrell & Pease, 1993; Pease, 1998; Trickett, Osborn, Seymour, & Pease, 1992). In short, the study of repeats is crucial to both understanding and preventing victimization.

Hindelang et al. (1978) were among the first to explore victim “proneness,” or the propensity to be repeatedly victimized (see also Fienberg, 1980; Reiss, 1980; Sparks, 1981, for early works). Since then, explanations of repeat victimization generally fall into one of two broad categories: risk heterogeneity and state dependence (Farrell, Phillips, & Pease, 1995; Lauritsen & Quinet, 1995; Spelman, 1995; Tseloni & Pease, 2004). Risk heterogeneity, also known as “flag theory,” suggests that victims have some enduring characteristics or behaviors that repeatedly place them at risk for victimization (Farrell et al., 1995). Some scholars, for example, use the routine activity perspective (Cohen & Felson, 1979; Cohen, Kluegel, & Land, 1981) to explain repeat victimization over time. Eck (2001) argued that repeat victimization problems stem from the actions of targets and their guardians, stating that “lifestyles, travel routes, job situations, or other stable living patterns” can repeatedly put some individuals and their property at risk (p. 257). Similarly, Wittebrood and Nieuwbeerta (2000) hypothesized that patterns of routine activities—including proximity to high crime areas, exposure to criminal opportunities, target attractiveness, and guardianship—may be useful for explaining criminal victimization during one’s life course.

Conversely, state dependence, also known as “boost theory,” suggests that previous victimization may increase the risk of future victimization (Farrell et al., 1995). Thus, the observed relationship between victimization experiences is causal, as previous victimization impacts the likelihood of subsequent victimization. Farrell et al. (1995) applied a rational choice theory of offender decision making to explain how the interaction between the target/victim and the offender in an initial victimization can impact the likelihood of future victimizations. They argue that the previous victimization experience provides the offender with information that shapes perceptions about the risks and rewards associated with future victimizations. Polvi et al. (1991), for example, found that there is an elevated risk of repeat victimization following an initial burglary for a short period of time. They offer several potential explanations for this finding, including that the same offenders return to the residence, due to known opportunities or in anticipation of replacement of goods (Polvi et al., 1991).

A great deal of research examining repeat victimization has focused on property crime, and burglary in particular (e.g., Bowers & Johnson, 2005; Forrester, Chatterton, & Pease, 1988; Forrester et al., 1990; Osborn, Ellingworth, Hope, & Trickett, 1996; Osborn & Tseloni, 1998; Sagovsky & Johnson, 2007), but there is also mounting evidence that concentration exists across other crime types, including the sexual victimization of college women (Daigle, Fisher, & Cullen, 2008), the poly-victimization of children (Finkelhor, Omrod, Turner, & Holt, 2009), domestic violence (Lloyd, Farrell, & Pease, 1994), and fraud (Titus & Gover, 2001). In addition, a few studies have examined repeat violent victimization specifically.² Lauritsen and Quinet (1995), for example, analyzed repeat assault and robbery victimization using the first five waves of the National Youth Survey. Their findings reveal that victimization is disproportionately concentrated among relatively few victims, individual risk for victimization is correlated across years, and there is evidence that both state dependence and risk heterogeneity processes contribute to this observed correlation. Ousey et al. (2008) examined school-based repeat simple assault using four waves of data collected from Kentucky students during 7th, 8th, 9th, and 10th grades. They find that both population heterogeneity and state dependence processes contribute to repeat assault victimization, but that the magnitude of the impact of prior victimization (i.e., state dependence) is more modest than prior research suggested. These studies demonstrate that prior violent victimization is related to future risk—be it via risk heterogeneity or state dependence—over relatively short time periods. Neither risk heterogeneity nor state dependence explanations for repeat victimization, however, suggest that repeat victimization would necessarily be limited to the few months or years following a victimization experience.

Toward a “Victim Careers” Perspective

Farrell and colleagues (2001) argued that the natural extension of repeat victimization research is the study of victimization from a life course perspective, stating that such research offers potential benefits for both theory and prevention (see also Averdijk, 2010; Lauritsen & Laub, 2007; Ousey et al., 2008). Although such a body of work exists regarding criminal careers across the life course (e.g., Piquero, Farrington, & Blumstein, 2003; Piquero, Jennings, & Reingle, 2010), relatively little is known about victimization from a longitudinal perspective (Farrell et al., 2001). Research from the criminal career paradigm has produced an important body of findings related to prevalence, frequency, onset, specialization, and desistance across the life course that have been used to inform contemporary criminological theory (Piquero et al., 2003; Piquero et al., 2010). Sampson and Laub’s (1993) age-graded theory of informal social control, for example, provides a state dependence explanation to account for the observed continuity and change in individual offending patterns over the life course (see also Laub, Sampson, & Sweeten, 2006). Similarly, Moffitt’s (1993) dual developmental taxonomy aims to explain two seemingly contradictory empirical observations about antisocial behavior: “it shows impressive continuity over age, but . . . its prevalence changes dramatically over age, increasing almost 10-fold temporarily during adolescence” (p. 674). The aforementioned “life course” theories were thoughtfully constructed to account for empirical findings regarding offending across the life course and have had tremendous impacts on the field of criminology. Theoretical developments in the area of victimization can be hastened by empirical research that explores the patterning of victimization across the life course.

To date, there has been relatively little research examining the relationship of prior victimization and future risk at later stages in the life course. Menard’s (2000) descriptive study, which uses nine waves of data from the National Youth Survey that span 17 years, suggests that there are long-term patterns of violent victimization and that there is an increasing concentration of violent victimization from adolescence to adulthood. Menard (2000) reported that victimization is typically an intermittent experience, which “suggests that even longitudinal data, if the time span is short, will fail to detect some chronic victims as repeat victims; instead such data will classify them as one-timers, or will fail altogether to detect them as victims” (p. 568). Beyond this descriptive study, there are a limited number of studies that have examined the relationship between prior violent victimization and future risk during subsequent stages of the life course. Wittebrood and Nieuwbeerta (2000) used retrospective data collected from a nationally representative sample of the Dutch population age 15 and older. At the bivariate level, they found that prior victimization is significantly related to later assault victimization. Once they controlled for lifestyles/routine activity measures—including the respondent’s own offending—the effect of prior victimization on assault victimization was nonsignificant. Murphy (2008), using data from the first three waves of the National Longitudinal Study of Adolescent Health, reported a consistent pattern of violent victimization across the stages of the life course. In particular, she found that those who reported physical abuse before Grade 6 and violent victimization during adolescence were significantly more likely to report both general violent victimization and intimate partner violence in early adulthood. The analysis, however, did not control for offending, making it unclear whether the observed patterns of violent victimization apply to victims who are not themselves involved in criminal behavior.

This latter point is particularly important given the body of research documenting the correlation between victimization and offending, or the “victim–offender” overlap (Berg et al., 2012; Jennings et al., 2010; Jennings et al., 2012; Lauritsen & Laub, 2007; Lauritsen et al., 1991; Schreck et al., 2008). This overlap has also been observed for violence in particular (see Mustaine & Tewksbury, 2000; Silver, Piquero, Jennings, Piquero, & Leiber, 2011). From the lifestyles-routine activity perspectives, criminal behavior might contribute to victimization risk over the life course by repeatedly exposing individuals to risky situations that present opportunities for criminal victimization (Cohen & Felson, 1979; Hindelang et al., 1978). Chen (2010), for example, examined the association between deviant lifestyles and victimization using 4 years of data collected from public school students as part of the Gang Resistance Education and Training program. She found that victimization and deviant lifestyles change over time, and changes in deviant lifestyle patterns lead to changes in victimization patterns over time (Chen, 2010). It is also plausible that victimization and offending might have common sources, such as low self-control (Gottfredson & Hirschi, 1990; Schreck, 1999; Schreck, Wright, & Miller, 2002) or genetic factors. A bivariate genetic analysis by Vaske, Boisvert, and Wright (2012), for example, indicates that genetic factors explain 39% of the covariance between violent victimization and delinquency in adolescence and 20% in early adulthood.

Lauritsen and Laub (2007) contended that the victim–offender overlap is more than an interesting criminological phenomenon; rather, it should be central to future crime prevention research. They argue that policies intended to prevent victimization should be more explicitly tied to policies aimed at reducing offending to reduce rates of both offending and victimization. Given that violent offending is a well-established correlate of violent victimization and the associated implications for victimization prevention, it is important to consider whether the relationship between prior violent victimization and future risk exists for all victims, or if it is only observed for those who are themselves involved in violent offending.

In sum, the study of victimization from a “victim careers” perspective is appealing for theory development. Furthermore, understanding the relationship between prior victimization and future risk may prove to be crucial for improving and measuring the effectiveness of prevention efforts. If prior victimization does in fact impact long-term risk for victimization, this reaffirms the importance of focusing resources on victims (Farrell & Pease, 1993; Farrell et al., 2001). In addition, determining whether this relationship exists independent of one’s own involvement in offending has implications for whether delinquency prevention programs that effectively reduce one’s offending behavior are sufficient for addressing chronic violent victimization.

Conceptually, there is some existing work that demonstrates the viability of a life course approach to victimization. Finkelhor and colleagues (e.g., Finkelhor, 1995; Finkelhor & Dziuba-Leatherman, 1994; Finkelhor, Omrod, & Turner, 2007; Finkelhor, Omrod, Turner, & Hamby, 2005) have approached the victimization of children from a developmental perspective that explores the developmental changes that influence children’s risk for and reactions to different types of victimizations. Macmillan’s (2001) comprehensive review of the life course consequences of violent victimization highlights that the risk is highest in adolescence, a period during which many life course trajectories are formed. Daigle, Beaver, and Hartman (2008) applied Sampson and Laub’s (1993) age-graded theory of informal social control to victimization and found that life course transitions assumed to form social bonds—marriage and employment—are associated with reduced victimization. The present study, which examines long-term violent victimization patterns from a “victim careers” perspective in a nationally representative longitudinal sample, contributes to the repeat victimization literature through research that aims to stimulate subsequent conceptual work on victimization across the life course.

The Present Study

In recent decades, research on offending over the life course has informed theoretical developments, with scholars seeking to explain empirical findings related to individual offending patterns across the life course (Moffitt, 1993; Sampson & Laub, 1993). In a separate vein, there is a considerable body of work documenting the phenomenon of repeat victimization, though most research in this area examines victimization over a relatively short time period. The development of victimization theories and prevention strategies from a life course perspective can be hastened by research examining criminal victimization across the stages of the life course. To this end, the present study aims to contribute to the repeat victimization literature through a series of exploratory analyses examining the relationship between violent victimization risk in adulthood and prior victimization during earlier stages in the life course. Given the theoretical and empirical linkages between offending and victimization, I also examine whether the nature of one’s victimization career impacts future victimization independent of current offending. Specifically, the present study addresses the following three research questions:

Research Question 1: Is violent victimization prevalence in earlier stages of the life course related to violent victimization risk in adulthood? If so, can violent offending during adulthood account for the observed relationship?

Research Question 2: Is early onset of violent victimization in adolescence related to violent victimization risk in adulthood? If so, can violent offending during adulthood account for the observed relationship?

Research Question 3: Is persistence of violent victimization across previous stages of the life course related to violent victimization risk in adulthood? If so, can violent offending during adulthood account for the observed relationship?

Data

The present study uses four waves of public-use data from the National Longitudinal Study of Adolescent Health (Add Health). The Add Health data were collected from a sample of students enrolled in middle and high schools located in the United States (Harris et al., 2009). Systematic sampling and stratification techniques were used to select a sample of 80 high schools and 52 middle schools. Students in Grades 7 through 12 completed the Wave 1 in-school self-report survey during the 1994-1995 school year. A sample of these students was selected for in-home interviews using stratified random sampling, and additional data were collected from this subsample during Wave 1. Wave 2 data were collected during follow-up in-home interviews during April through August of 1996. Wave 3 data were collected between August 2001 and April 2002 when the respondents were between 18 and 26 years old. Finally, Wave 4 data were collected using in-home interviews in 2008, when the respondents were between 24 and 32 years of age. Wave 1 of the public-use Add Health data includes information on 6,504 adolescents.³ Of these cases, 3,342 participants were interviewed during Waves 2, 3, and 4. Listwise deletion of cases based on missing data for study variables resulted in 2,779 cases for analysis.⁴

Measures

Violent victimization in Wave 4 is a dichotomous dependent variable created from three survey items. Respondents were asked whether the following things had happened to them in the past 12 months: “Someone pulled a knife or gun on you?” “Someone shot or stabbed you?” and “You were beaten up?” (Cronbach’s α = .72). If respondents answered “yes” to any of these three victimizations, they were coded as 1.⁵ All others were coded as 0 to indicate that they did not report violent victimization during Wave 4.

Several variables were created using the Wave 4 data to measure respondent sociodemographic characteristics that prior research demonstrates are correlated with risk for violent victimization (e.g., Kennedy & Forde, 1990; Lauritsen, 2001; Lauritsen & Carbone-Lopez, 2011; Lauritsen & Quinet, 1995; Miethe & Meier, 1990; Sampson, 1987; Tillyer, Fisher, & Wilcox, 2011; Truman, 2011). Male (0 = no, 1 = yes) measures the respondent’s gender. Three dummy variables measure race and ethnicity—Black, Hispanic, and Other, with White Non-Hispanic being the omitted reference category. Age is the respondent’s age in years at Wave 4. Education is an ordinal variable that measures the educational attainment of the respondent (1 = less than high school diploma, 2 = high school graduate, 3 = some college or vocational/technical training, 4 = college degree). Employed is a dichotomous variable that measures whether the respondent works for pay for a minimum of 10 hr per week (0 = no, 1 = yes). Household income measures the respondent’s total household income before taxes and deductions (1 = less than US$5,000 to 12 = US$150,000 or more). Married is a dichotomous variable that measures whether the respondent is currently married and living with his or her spouse (0 = no, 1 = yes).

I created several variables to capture the variation in violent victimization experiences prior to Wave 4. The first set of these variables measures violent victimization prevalence at each of the prior waves. Each variable measures whether the respondent reported any violent victimization in the prior 12 months.⁶ Note that these variables are not mutually exclusive, as a respondent could experience victimization during more than one wave. The second set of these variables is designed to capture the onset of violent victimization and measures when, if ever, respondents reported their first violent victimization during their Add Health in-home interviews. Three dummy variables measure whether the first violent victimization was reported in Wave 1, Wave 2, or Wave 3, with no prior violent victimization as the omitted reference category in the analyses. Finally, I measured the respondent’s violent victimization persistence across earlier stages of the life course. This variable captures the number of waves prior to Wave 4 during which the respondent reported violent victimization, with values ranging from zero to three.

Given the established theoretical and empirical literature demonstrating the relationship between offending and victimization, the present study also measures violent offending during Wave 4. Violent offending is a dichotomous variable that measures whether the respondent reports any violent behavior in the past 12 months. The items used to create this variable specifically asked respondents whether they used or threatened to use a weapon to get something from someone, pulled a knife or gun on someone, hurt someone badly enough in a physical fight that he or she needed care from a doctor or nurse, and/or shot or stabbed someone (Cronbach’s α = .83). Descriptive statistics for all study variables are available in Table 1.⁷

Table 1:

Descriptive Statistics (N = 2,779)^a

	Minimum	Maximum	M	SD
Dependent variable
Violent victimization in W4	0.00	1.00	0.08	0.27
Independent variables
Male	0.00	1.00	0.50	0.50
Black	0.00	1.00	0.13	0.34
Hispanic	0.00	1.00	0.11	0.31
Other	0.00	1.00	0.07	0.26
Age	24.00	33.00	27.88	1.66
Education	1.00	4.00	3.01	0.91
Employed	0.00	1.00	0.84	0.37
Household income	1.00	12.00	8.01	2.60
Married	0.00	1.00	0.40	0.49
Violent victimization prevalence
VV prevalence in W1	0.00	1.00	0.19	0.39
VV prevalence in W2	0.00	1.00	0.15	0.35
VV prevalence in W3	0.00	1.00	0.09	0.28
Violent victimization onset
First VV in W1	0.00	1.00	0.19	0.39
First VV in W2	0.00	1.00	0.06	0.23
First VV in W3	0.00	1.00	0.04	0.20
Violent victimization persistence	0.00	3.00	0.42	0.75
Violent offender in W4	0.00	1.00	0.05	0.22

Note. VV = Violent victimization.

The descriptive statistics presented above are based on the weighted data.

Results

I began by exploring whether the violent victimization prevalence reported across survey waves was nonrandomly distributed across study participants (see Hindelang et al., 1978; Sparks, 1981). First, I calculated a simple Poisson model of the expected frequencies of participants to report victimization prevalence across a total of 0, 1, 2, 3, and 4 waves, respectively (Expected: 0 = 1,733.76; 1 = 817.99; 2 = 192.96; 3 = 30.35; 4 = 3.58). I then tested whether the observed frequencies were significantly different from the Poisson-expected frequencies (Observed: 0 = 1,928; 1 = 507; 2 = 243; 3 = 86; 4 = 15). Consistent with prior research on the distribution of victimization, the observed frequencies were significantly different from those expected by chance (χ² = 291.405, p < .001).

To explore the relationship between prior violent victimization history and current risk, I estimated a series of logistic regression models that examine the influence of victimization prevalence, onset, and persistence, while also accounting for the respondents’ self-reported violent offending.⁸ Table 2 examines the relationship between violent victimization prevalence during Waves 1, 2, and 3 and violent victimization risk during Wave 4, net of sociodemographic controls. Consistent with prior research, many of the control variables were significantly associated with violent victimization risk. Males were more likely to report violent victimization during Wave 4 compared with females. Black and Hispanic respondents were more likely to experience violent victimization, relative to White Non-Hispanics. Conversely, education, employment, and household income were negatively associated with violent victimization risk.

Table 2:

Adult Violent Victimization Risk and Victimization Prevalence in Earlier Waves^a

	Model 1			Model 2
	b	SE	Exp(B)	b	SE	Exp(B)
Constant	−1.48	1.28	—	−1.32	1.39	—
Male	0.90**	0.17	2.46	0.69**	0.18	2.00
Black	0.69**	0.19	1.99	0.58**	0.21	1.79
Hispanic	0.54**	0.22	1.72	0.51*	0.23	1.67
Other	−0.45	0.36	—	−0.70	0.40	—
Age	0.01	0.05	—	0.00	0.05	—
Education	−0.29**	0.08	1.33	−0.26**	0.09	1.30
Employed	−0.66**	0.18	1.93	−0.63**	0.19	1.89
Household income	−0.11**	0.03	1.12	−0.13**	0.03	1.14
Married	−0.10	0.17	—	−0.14	0.18	—
VV prevalence in W1	0.37*	0.18	1.45	0.25	0.20	—
VV prevalence in W2	0.45*	0.19	1.56	0.28	0.21	—
VV prevalence in W3	0.38	0.21	—	−0.07	0.24	—
Violent offender in W4	—	—	—	2.67**	0.22	14.44
Nagelkerke R²	0.16			0.28
Model x²	198.31**			343.66**

Note. VV = violent victimization.

The logistic regression results presented above are based on the weighted data. Odds ratios for negative coefficients are inverted for ease of interpretation.

p ≤ .05. **p ≤ .01.

With respect to the influence of prior victimization on future risk, Model 1 in Table 2 indicates that prevalence in Wave 1 and prevalence in Wave 2 are associated with a significant increase in violent victimization risk during Wave 4 (the positive association between Wave 3 prevalence and risk in Wave 4 did not reach statistical significance, with p = .07). Model 2, however, demonstrates that the influence of victimization prevalence in earlier waves on future risk is not observed once violent offending is controlled. Consistent with prior research on the victim–offender overlap, an individual’s current violent offending maintains a strong and statistically significant relationship with violent victimization risk, with an odds ratio of 14.44.

Table 3 examines whether onset—measured as the first time the respondent reported violent victimization during their Add Health interviews—is associated with violent victimization risk in adulthood. Model 3 indicates that those who reported victimization in adolescence—in Waves 1 or 2—were more likely to report violent victimization in Wave 4. Once violent offending is introduced in Model 4, however, only Wave 1 onset is significantly associated with an increased risk for violent victimization in Wave 4.

Table 3:

Adult Violent Victimization Risk and Victimization Onset in Earlier Waves^a

	Model 3			Model 4
	b	SE	Exp(B)	b	SE	Exp(B)
Constant	−1.55	1.28	—	−1.51**	1.39	—
Male	0.93**	0.17	2.52	0.67**	0.18	1.95
Black	0.69**	0.19	2.00	0.58*	0.21	1.79
Hispanic	0.53*	0.22	1.69	0.52	0.23	—
Other	−0.43	0.35	—	−0.71	0.39	—
Age	0.01	0.05	—	0.01	0.05	—
Education	−0.28**	0.08	1.33	−0.25**	0.09	1.28
Employed	−0.67**	0.18	1.95	−0.64**	0.19	1.90
Household income	−0.11**	0.03	1.11	−0.13**	0.03	1.14
Married	−0.10	0.17	—	−0.14	0.18	—
First VV in W1	0.75**	0.17	2.11	0.46*	0.19	1.58
First VV in W2	0.70**	0.27	2.01	0.44	0.30	—
First VV in W3	0.51	0.33	—	0.35	0.35	—
Violent offender in W4	—	—	—	2.65**	0.21	14.15
Nagelkerke R²	0.16			0.28
Model x²	196.52**			344.56**

Note. VV = violent victimization.

The logistic regression results presented above are based on the weighted data. Odds ratios for negative coefficients are inverted for ease of interpretation.

p ≤ .05. **p ≤ .01.

Finally, Table 4 explores the relationship between violent victimization persistence—measured as the number of waves prior to Wave 4 during which the respondent reported violent victimization—and violent victimization risk in Wave 4. Violent victimization across prior waves was significantly associated with risk during Wave 4; though this relationship is somewhat weakened when current violent offending is controlled, it is still statistically significant, with an odds ratio of 1.21.

Table 4:

Adult Violent Victimization Risk and Victimization Persistence in Earlier Waves^a

	Model 5			Model 6
	b	SE	Exp(B)	b	SE	Exp(B)
Constant	−1.50	1.27	—	−1.48	1.38	—
Male	0.90**	0.17	2.45	0.69**	0.18	1.98
Black	0.69**	0.19	1.99	0.59**	0.21	1.81
Hispanic	0.54**	0.22	1.72	0.53*	0.23	1.69
Other	−0.45	0.36	—	−0.72	0.40	—
Age	0.01	0.05	—	0.01	0.05	—
Education	−0.29**	0.08	1.33	−0.26**	0.09	1.30
Employed	−0.66**	0.18	1.93	−0.64**	0.19	1.89
Household income	−0.11**	0.03	1.12	−0.13**	0.03	1.16
Married	−0.10	0.17	—	−0.15	0.18	—
VV persistence	0.40**	0.08	1.50	0.20*	0.09	1.21
Violent offender in W4	—	—	—	2.64**	0.22	14.03
Nagelkerke R²	0.16			0.27
Model x²	198.24**			342.23**

Note. VV = violent victimization.

The logistic regression results presented above are based on the weighted data. Odds ratios for negative coefficients are inverted for ease of interpretation.

p ≤ .05. **p ≤ .01.

Discussion

There are three broad findings to highlight from the analyses. First, although victimization prevalence during adolescence was significantly related to risk in Wave 4, the effects of violent victimization prevalence in Waves 1 and 2 were rendered nonsignificant once current violent offending was controlled. Second, and related, respondents’ current violent offending is by far the most important predictor of their violent victimization risk in adulthood. Third, even when current violent offending is controlled, those who report early onset of violent victimization and persistence of violent victimization across waves are at an elevated risk for violent victimization in Wave 4.

It is interesting that early and persistent victimization is predictive of violent victimization in Wave 4, while the more recent Wave 3 victimization is not. Perhaps Wave 3 victimizations are the result of short-term risk that subsides by Wave 4. This short-term risk may be a function of the lifestyles and/or routine activities that mark this developmental stage of the life course (the mean age of the sample at Wave 3 was between 21 and 22 years of age); for many individuals, this is a time when they no longer live with their parents, they are unmarried, and they are old enough to frequent bars, nightclubs, and parties. Conversely, victimizations occurring earlier and across multiple waves may be the result of an enduring set of characteristics, behaviors, and/or structural conditions that are more stable and thus are associated with violent victimization risk at Wave 4.

As noted above, there are few empirical investigations of violent victimization across the life course. This is likely due, at least in part, to lack of available data. Many longitudinal victimization surveys do not collect data over longer periods of time, and surveys such as the National Crime Victimization Survey (NCVS) do not ask respondents about their own criminal behavior (Lauritsen & Laub, 2007), making it impossible to determine whether the observed patterns of repeat victimization are simply a function of criminal involvement. The Add Health data offer several benefits and present a few limitations that warrant further discussion. In terms of benefits, the Add Health is a nationally representative longitudinal data set that measures violent victimization across stages of the life course. Respondents are questioned about victimization experiences occurring in the past 12 months, thus minimizing the recall error that is likely present in studies that retrospectively measure lifetime victimization. In addition, the Add Health includes measures of offending, thus allowing researchers to account for the victim–offender overlap.

That being said, using the Add Health to capture the “victim career” presents several limitations. First, as discussed earlier (see Footnote 3), sample attrition disproportionately affects high-risk individuals; those most at risk for experiencing multiple victimizations are more likely to drop out of the study and therefore are not represented in the estimates of violent victimization reported here. Second, because the reporting of violent victimization during Waves 1, 2, and 3 only include experiences that happened within 12 months, victimizations that occurred outside these time frames are not recorded in the Add Health. Third, victimization is captured using ordinal measures in Waves 1 and 2 and dichotomous measures in Wave 3. Ideally, the present study would have explored whether the number of prior victimizations (in addition to persistence of victimization across waves) was predictive of future risk. The measurement level of the survey items made this impossible. Even with the ordinal measurement in Waves 1 and 2, the present study could not distinguish with certainty between one-time and repeat victims within Waves 1 or 2, because the data do not include incident-level information about the victimization experiences. For example, if respondents reported that someone pulled a weapon on them in Wave 1 and they were jumped in Wave 1, it is unclear whether they are reporting a single or multiple incidents. Finally, the Add Health to date only includes data through young adulthood. It is unclear if early and persistent victimization in adolescence is associated with an increased risk for violent victimization throughout the life course, or if this relationship is weakened with age; additional research is needed to observe how violent victimization risk is patterned across later stages of the life course. In short, the repeat violent victimization estimates reported in the present study are incredibly conservative; it is likely that there is considerable repeat violent victimization—both within and across years—that the present study is unable to capture.

In addition, violent offending and violent victimization are both measured during Wave 4, making the time order of these variables uncertain. Although Wave 3 does include measures of offending, a 6-year lag is not likely a reliable indicator of ongoing criminal behavior that puts respondents at risk for violent victimization. The purpose of the present study was to observe how one’s past victimization experiences may predict future risk, independent of current violent offending. The relationship between one’s criminal career and victim career is complex and warrants considerable theoretical and empirical attention beyond the scope of the present study. Future research should explore how victimization and offending patterns overlap across the life course and the nature of the relationships, be it recursive, reciprocal, or spurious.

Moving a “Victim Careers” Agenda Forward

Developing a body of empirical findings regarding individual victimization patterns across the life course has the potential to stimulate theoretical developments and guide improvements for crime prevention. Findings from the present study suggest that although the victim–offender overlap is important for understanding victimization across the life course, offending is not a sufficient explanation for long-term violent victimization patterns, nor will effective delinquency prevention efforts alone eliminate chronic violent victimization. Those who report early and persistent violent victimization appear particularly vulnerable to subsequent victimization. Below I outline several ideas to direct future scholarly inquiry regarding repeat violent victimization across the life course.

From a risk heterogeneity, or “flag theory” perspective, it is plausible that some individuals have enduring characteristics and behaviors that repeatedly place them at risk for victimization across developmental stages of the life course. In other words, the observed correlation in victimization over time is spurious, not causal. Lifestyles-routine activity and low self-control are prominent explanations of victimization that could be used to explain continuity in victimization across the life course, or “long-term” repeat victimization (Cohen & Felson, 1979; Eck, 2001; Hindelang et al., 1978; Schreck, 1999). To be clear, identifying behaviors and characteristics that place individuals at repeated risk for victimization should not be viewed as “victim blaming.” As Schreck and colleagues (2002) argued, no one deserves to be victim of a crime; however, some individuals are disproportionately victimized. Identifying the factors that place them at risk is important for developing policies to reduce that risk.

Wittebrood and Nieuwbeerta’s (2000) findings support the idea that heterogeneity within the population with respect to routine activities partially explains repeat victimization across the life course. A recent study by Averdijk (2011) on the reciprocal of effects of victimization and routine activities using data from the NCVS suggests that the influence of victimization on routine activities is limited. Therefore, routine activities—even for those who are victimized—may remain relatively stable, thus accounting for the observed continuity in victimization risk over time. Similarly, numerous studies have observed a relationship between low self-control and victimization (e.g., Daigle et al., 2008; Schreck, 1999). Gottfredson and Hirschi (1990) argued that self-control is a stable construct once established, and research to date generally supports their assertion (see, for example, Hay & Forrest, 2006). Stable traits such as low self-control may account, at least in part, for repeat victimization across the life course.

It is also plausible that repeat victimization is a function of ecological factors. Much victimization theory and research in recent years are grounded in multilevel frameworks that draw from the social disorganization tradition to consider how contextual factors directly influence victimization risk, as well as how they might moderate the influence of individual factors on victimization (e.g., Sampson & Wooldredge, 1987; Wilcox, Land, & Hunt, 2003). Multicontextual opportunity theory, for example, has been used to explain variation in burglary victimization (Wilcox, Madensen, & Tillyer, 2007). It is unknown, however, whether it can explain intraindividual variation over time in victimization risk. It is possible that repeat victimization patterns observed at the individual level might be explained, in part, not just by one’s own traits or risky activities but also by ecological characteristics of the community in which one is embedded. The extent to which individuals are cognizant of these high-risk environments, and are willing and able to remove themselves from dangerous contexts may be important to understanding repeat violent victimization patterns across the life course. Admittedly, testing such hypotheses may prove to be challenging, as getting accurate dynamic measures of contextual and individual risk factors for victimization across the life course is difficult to say the least.

From a state dependence, or “boost theory” perspective, it is plausible that the experience of being violently victimized causally increases future risk. One potential mechanism by which this occurs is through a victim labeling process (Lauritsen & Quinet, 1995; Ousey et al., 2008); potential offenders view those who have already been victimized as particularly vulnerable targets. When we apply such logic to repeat violent victimization across developmental stages of the life course, this obviously begs the question of how long such labels stick. For example, the idea of victim labeling in a school context seems reasonable, particularly in the case of bullying. When we extend victim labeling, however, to consider how a label formed in early adolescence influences serious violent victimization risk more than a decade later, the process is less clear. Qualitative research on chronic victims may be needed to explore if and how such processes operate across the life course.

Another state dependence explanation is individual behavioral changes in response to the initial victimization experience that increase future risk. There is some indirect evidence from the victimization literature that lends support to the idea that individuals make incorrect assumptions about how to reduce victimization risk. Recent research on victimization and perceived risk suggests that some individuals do not accurately assess the factors that put them at risk for violent victimization. Tillyer et al. (2011), for example, reported that associating with delinquent peers decreases students’ perceptions of risk for violent victimization in school, despite the fact that it increases actual risk. Similarly, Melde (2009) found that involvement in a delinquent lifestyle reduces perceptions of victimization risk, despite the fact that it increases actual risk. Stewart, Schreck, and Simons (2006) reported similar findings with respect to adopting a “street code” that requires one to “maintain the respect of others through a violent and tough identity, and a willingness to exact retribution in the event of disrespect” (p. 428). Although Anderson (1999) hypothesized that adopting the street code increases respect by peers and potential offenders, thus reducing victimization risk, Stewart et al. found that adopting the street code not only increases victimization risk, but it exacerbates it beyond that associated with living in a high crime, disorganized community. In short, it may be that some individuals, in an attempt to reduce risk, actually respond to victimization in ways that increase risk—by becoming more aggressive, associating with delinquent peers, carrying weapons, joining gangs, and so on.⁹

Beyond the explicit risk heterogeneity versus state dependence issues, there are additional avenues to consider. Past progress in victimization scholarship has been stimulated by criminological theories designed to explain empirical facts about crime (Gottfredson & Hirschi, 1990; Schreck, 1999), and the life course theories put forth by Sampson and Laub (1993) and Moffitt (1993) offer similar opportunities. These scholars were adept in identifying the types of explanations needed to account for observed individual patterns in offending across the life course. I am not suggesting that the specific constructs explicated in their theories are relevant (or not) to understanding victimization across the life course, but rather that the structure of their theories offers guidance for the study of victimization across the life course.

For example, one purpose of Sampson and Laub’s (1993) theory is to account for intraindividual change in offending across the life course. Victimization scholars should consider the same issue with respect to victimization. One promising avenue is to apply Paternoster and Pogarsky’s (2009) recent theoretical developments on “thoughtfully reflective decision making” (TRDM) to individual victimization patterns. TRDM is

the tendency of persons to collect information relevant to a problem or decision they must make, to think deliberately, carefully, and thoughtfully about possible solutions to the problem, apply reason to the examination of alternative solutions, and reflect back upon both the process and the outcome of the choice in order to assess what went right and what went wrong. (Paternoster & Pogarsky, 2009, pp. 104-105)

Paternoster, Pogarsky, and Zimmerman (2011) argued that capital—human, social, and cultural—mediates the relationship between TRDM and later life outcomes, and their hypothesis tests support their assertions. Furthermore, the authors assert that

TRDM is a trait that varies across individuals (some are better decision makers than others), within-individuals over time (the ability to make good decisions is, in part, a function of age), and across decision making contexts (not all decisions are made thoughtfully and reflectively). (Paternoster et al., 2011, p. 3)

TRDM presents exciting opportunities to explore in victimization theory and research. On its face, this framework is compatible with the lifestyles-routine activity perspectives that have guided much of the victimization research in recent years, and offers the potential to explain both between- and within-individual variation by considering how individuals perceive and respond to victimization. As discussed above, research on adolescent victimization suggests that risk perceptions are often incongruent with actual risk factors (Melde, 2009; Tillyer et al., 2011). Perhaps a victim’s ability (or lack thereof) to consider, accurately identify, and address the factors that put them at risk for the victimization may prove to be important in understanding chronic victimization across the life course.

Moffitt (1993) argued that there are two types of offenders with different etiologies: (a) life course persistent, or those whose careers are marked by continuity, in that they exhibit antisocial behavior across every stage of the life course; and (b) adolescent limited, or those whose careers are marked by change, in that they have brief criminal careers during adolescence. Although some may argue that victims (and offenders, for that matter) do not fit neatly into typologies, scholars should consider how different sources of victimization risk might produce variation in “victim careers” when examining victimization across the life course. For example, individual risky routine activity patterns—which not only peak in adolescence but also persist for a small proportion of individuals—might produce victim careers that follow the age-crime curve similar to Moffitt’s adolescent-limited offenders. Other sources of risk that are more stable over time—the cumulative disadvantage that characterizes disorganized schools and neighborhoods, or personality traits that create conflict in interpersonal relationships—may produce victim careers that persist across developmental stages of the life course. In short, some sources of risk are easier than others to alter; exploring how different sources pattern victimization across the life course is important not just for theoretical reasons, but for prevention as well.

Policy Implications

The findings from the present study suggest some policy implications for those working with victims and/or perpetrators of violence. First, the present study reaffirms policy recommendations that have been advocated by repeat victimization scholars for decades: Focusing on repeat victims is an effective and defensible allocation of limited crime prevention resources (Farrell, 1995; Farrell & Pease, 1993; Pease, 1998; Trickett et al., 1992). Such a focus on repeat victims of violence may offer an even greater return than initially believed, given the long-term patterns of violent victimization observed in the present study. Victim support services and prevention programs should prioritize the youngest and most frequent victims of violence, as the results indicate that these individuals are most at risk for subsequent violent victimization during later stages of the life course.

In particular, victims should be educated about the established correlates of violent victimization so they can take steps to reduce their risk, an endeavor that may require challenging victims’ beliefs about the risk factors for victimization. As describe above, quantitative research on the perceived risk for violent victimization indicates that individual assessments about the risk factors for violent victimization are often inaccurate, and some individuals may respond to violent victimization in ways that actually increase subsequent risk (Melde, 2009; Stewart et al., 2006; Tillyer et al., 2011). In addition, qualitative research by Anderson (1999) on violence in the inner city suggests individuals often believe that fate determines what happens to them: “risks are not seen as risks, because what will be will be” (p. 137). They can walk the streets fearlessly because what happens to them, they believe, is beyond their control. Therefore, practitioners will need to overcome existing beliefs about the inevitability of violent victimization to educate victims about how to reduce the risk.

Second, and consistent with prior research (Berg et al., 2012; Jennings et al., 2010; Jennings et al., 2012; Lauritsen & Laub, 2007; Lauritsen et al., 1991; Schreck et al., 2008), the results demonstrate that there is a considerable overlap in violent victimization and offending. Practitioners should be mindful that victims and offenders do not always comprise mutually exclusive groups. Prior research suggests that victims who are involved in criminal behaviors are less likely to report their victimization to authorities (Berg, Slocum, & Loeber, 2013), making it unlikely they will receive any sort of victim services. Offering victim support and prevention services to individuals in correctional settings is one way to reach an underserved victim population that is at high risk for subsequent victimization.

Conclusion

Repeat victimization research demonstrates that victimization is associated with increased future risk (Farrell, 1995), and there is a growing body of empirical evidence that indicates this relationship exists across various forms of victimization, including violence (e.g., Lauritsen & Quinet, 1995; Ousey et al., 2008). Contemporary criminological theories have arisen, at least in part, to explain empirical findings from criminal careers research that documents individual offending patterns across the life course (Moffitt, 1993; Sampson & Laub, 1993). Several victimization scholars have called for research that examines “victim careers” and considers repeat victimization across the life course (Farrell et al., 2001; Lauritsen & Laub, 2007; Ousey et al., 2008). Such research has the potential to stimulate theoretical developments and improve crime prevention efforts. The aim of the present study was to explore how the nature of one’s violent victimization career—as indicated by prevalence, onset, and persistence in earlier stages of the life course—was associated with risk for violent victimization in adulthood and offer specific avenues for future scholarly inquiry.

Findings from the analyses suggest that prior violent victimization during earlier stages of the life course is a risk factor for subsequent violent victimization. Even when current violent offending is controlled, those who reported early onset and persistence of violent victimization across earlier stages of the life course were at an elevated risk for violent victimization in adulthood. From a prevention standpoint, these findings reaffirm the importance of focusing crime prevention resources on victims (Farrell & Pease, 1993; Farrell et al., 2001). Theoretically, scholars should continue to explore how victim careers are formed, and consider how contemporary life course criminological theories might be useful for studying victimization across the life course.

Footnotes

Acknowledgements

I would like to thank the editor and four anonymous reviewers for their helpful comments on an earlier draft of this article.

This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by Grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (). No direct support was received from Grant P01-HD31921 for this analysis.

Notes

Marie Skubak Tillyer is an assistant professor in the Department of Criminal Justice at the University of Texas at San Antonio. Her research interests include violence, victimization, and crime prevention. Her recent research has appeared in Crime and Delinquency, Justice Quarterly, and Journal of Research in Crime and Delinquency.

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