Direct and indirect effects of neighborhood characteristics on the perpetration of dating violence across adolescence.

J Youth Adolescence (2015) 44:727–744 DOI 10.1007/s10964-014-0190-z

EMPIRICAL RESEARCH

Direct and Indirect Effects of Neighborhood Characteristics on the Perpetration of Dating Violence Across Adolescence Ling-Yin Chang • Vangie A. Foshee • Heathe Luz McNaughton Reyes • Susan T. Ennett Carolyn T. Halpern

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Received: 19 June 2014 / Accepted: 17 September 2014 / Published online: 28 September 2014 Ó Springer Science+Business Media New York 2014

Abstract Neighborhood context plays a role in the development of adolescent health risk behaviors, but few studies have investigated the influence of neighborhoods on the perpetration of dating violence. This longitudinal study examined the direct effects of risky neighborhood structural and physical characteristics on trajectories of the perpetration of dating violence, tested whether collective efficacy mediated these relationships, and determined if the

L.-Y. Chang (&) Department of Health Behavior, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7400, USA e-mail: [email protected] V. A. Foshee Department of Health Behavior, 319B Rosenau Hall CB# 7440, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7400, USA H. L. M. Reyes Department of Health Behavior, 319G Rosenau Hall CB# 7440, Gillings School of Global Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7400, USA S. T. Ennett Department of Health Behavior, 358A Rosenau Hall CB# 7440, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7400, USA C. T. Halpern Department of Maternal and Child Health, 407A Rosenau Hall CB# 8120, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7400, USA

effects varied by the sex of the adolescent. Adolescent data are from a multi-wave longitudinal study from grades 8 to 12; neighborhood data were collected from parents’ interviews and U.S. Census data. Multilevel growth curve models were conducted with 3,218 students; the sample was 50 % male, 41 % White, 50 % Black, and 9 % other race/ethnicity. In models examining risky neighborhood variables one at a time, and controlling for potential individual-level confounders, the sex of the adolescent interacted with economic disadvantage, residential instability, and physical disorder; these risky neighborhood characteristics increased risk for girls’ but not boys’ perpetrating of dating violence. In full models with all of the risky neighborhood variables, the sex of the adolescent continued to interact with neighborhood economic disadvantage; living in economically disadvantaged neighborhoods increased girls’ but not boys’ risk for dating violence across all ages. No other risky neighborhood effects were found for boys or girls. Collective efficacy did not mediate the relationships between other neighborhood characteristics and the outcome. These findings suggest that dating violence prevention strategies for girls should consider the contexts in which they live rather than only targeting changes in their individual characteristics. Keywords Adolescent dating violence Neighborhood effects Multilevel model Developmental trajectory

Introduction Research has shown that neighborhoods play a role in the etiology of adolescent aggression and violence (KarrikerJaffe et al. 2012; Vanfossen et al. 2010), and in adult intimate partner violence (IPV) (Benson et al. 2003; Wright

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and Benson 2011). Only recently, however, have studies begun to examine associations between neighborhood characteristics and the perpetration of dating violence by adolescents (Banyard and Modecki 2006; Champion et al. 2008; Jain et al. 2010; Reed et al. 2011; Rothman et al. 2011; Schnurr and Lohman 2013)—a prevalent health-risk behavior that has serious and long-term consequences for its victims including an increased likelihood of being involved in adult IPV (Exner-Cortens et al. 2013).With one exception (Banyard and Modecki 2006), each of these studies found significant associations between various aspects of the neighborhood and the perpetration of adolescent dating violence (Champion et al. 2008; Jain et al. 2010; Reed et al. 2011; Rothman et al. 2011; Schnurr and Lohman 2013). However, methodological and theoretical limitations of the studies interfere with the interpretation of the findings. For example, adolescent self-reports were often used to measure both neighborhood characteristics and the perpetration of dating violence (Banyard and Modecki 2006; Champion et al. 2008; Reed et al. 2011; Rothman et al. 2011), which could have produced common-method variance and inflated or deflated associations (Williams and Brown 1994). Many of the studies did not account for clustering by neighborhood (Banyard and Modecki 2006; Champion et al. 2008; Reed et al. 2011) or control for individual-level socioeconomic status that could confound neighborhood and dating violence associations (Banyard and Modecki 2006; Champion et al. 2008; Reed et al. 2011; Rothman et al. 2011). Also, most were crosssectional, and thus could not examine neighborhood influences on the development of dating violence over time (Banyard and Modecki 2006; Champion et al. 2008; Reed et al. 2011; Rothman et al. 2011). Yet, developmental perspectives suggest that neighborhood influences may increase over time because adolescents spend more unsupervised time in neighborhoods as autonomy increases during adolescence (Ingoldsby and Shaw 2002; Steinberg and Silverberg 1986). Additionally, few studies have tested hypotheses based on theoretical perspectives that have guided research examining neighborhood effects on other adolescent risk behaviors and adult IPV. The purpose of the current study is to examine the influence of neighborhood characteristics on the perpetration of adolescent dating violence. Prior methodological and theoretical limitations are addressed by examining the influence of neighborhood characteristics on trajectories of the perpetration of dating violence across adolescence, controlling for neighborhood clustering and individuallevel demographic characteristics, not relying on adolescent self-reports of neighborhood characteristics, and testing hypotheses on the nature of influence that are informed by key theoretical perspectives on neighborhood influences.

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J Youth Adolescence (2015) 44:727–744

Theoretical Perspectives Much of the research on the influence of neighborhood characteristics on violence and aggression has been guided by social disorganization theory (Sampson et al. 1997; Shaw and McKay 1942), collective socialization models (Jencks and Mayers 1990), and broken window theory (Wilson and Kelling 1989). These theoretical perspectives specify how three aspects of the neighborhood—structural characteristics, the physical environment, and social organization—inter-relate to influence individual behaviors. Neighborhood structural characteristics refer to the population makeup of a neighborhood (Leventhal et al. 2009); the neighborhood physical environment refers to the appearance of the neighborhood, for example, whether buildings and streets are neat and manicured, or dilapidated and run down (Cohen et al. 2003); and social organization refers to how people in a neighborhood behave and bring about change in a phenomenon of interest. Social disorganization theory and collective socialization models suggest that the structural characteristics of risky neighborhoods, such as high levels of economic disadvantage, ethnic heterogeneity, and resident instability, lead to disruption of neighborhood social organization, reducing the ability of residents to establish norms and values and maintain effective social control, and resulting in increased rates of crime and violence. Broken windows theory (Wilson and Kelling 1989) suggests that the neighborhood physical environment affects individual behavior through influences on social organization. This theoretical perspective suggests that neighborhood physical disorder signals to residents that the neighborhood is unsafe, and as a result, residents start to withdraw from the public life of the neighborhood, leading to a breakdown of informal social control, and in turn to more serious crime (Gault and Silver 2008; Kelling and Coles 1996). Each of these perspectives proposes that social disorganization mediates the associations between risky structural and physical aspects of the neighborhood and individual behavior. Although there are exceptions (Duncan et al. 2003; Karriker-Jaffe et al. 2009; Warner and Rountree 1997), a number of empirical studies have supported these theoretical premises. In testing social disorganization theory, Sampson et al. (1997) focused on the mediating role of a particular aspect of social organization, collective efficacy, defined as the linkage of social cohesion and mutual trust among neighbors and their willingness to intervene for the common good of the neighborhood. Sampson et al. (1997) found significant positive associations between risky structural aspects of the neighborhood (economic disadvantage, immigrant concentration, and residential instability) and neighborhood violence and crime, even when controlling for important individual level covariates.


However, when collective efficacy was added to the model, these associations were significantly attenuated, and Sampson et al. (1997) concluded that this was evidence of mediation. In a formal statistical test of mediation, Elliott et al. (1996) found that neighborhood disadvantage decreased the development of informal social control (one aspect of social organization), and in turn increased the neighborhood rates of adolescent problem behaviors. Others have found that aspects of social organization mediated associations between neighborhood physical disorder and individuals’ mental health (Gapen et al. 2011; Kruger et al. 2007).

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neighborhoods characterized by risky structural and physical characteristics and low social organization may increase the likelihood of IPV by producing stress which may increase the likelihood of using violence against partners, creating cynicism of the justice system resulting in a decreased likelihood that victims of IPV will seek help and thus increasing the likelihood that the perpetration will continue, producing isolation among neighbors which decreases the likelihood that neighbors will intervene to help stop the IPV, and producing isolation of the neighborhood from other areas, which decreases diffusion into the neighborhood of mainstream cultural norms and beliefs that disapprove of IPV (Pinchevsky and Wright 2012).

Neighborhoods and Adult IPV Numerous studies have examined associations between the key neighborhood characteristics of these theories and adult IPV. Structural characteristics of the neighborhood (e.g., economic disadvantage, ethnic heterogeneity and residential instability) have been associated with adult IPV, with the strongest and most consistent associations being the positive association found between economic disadvantage and IPV (see Pinchevsky and Wright 2012 for a review). Fewer studies have examined associations between the physical environment and adult IPV, but those that have, found that neighborhood physical disorder was positively associated with adult IPV (Cunradi 2007a, b). And collective efficacy has been found to be protective against adult IPV in a number of studies (Browning 2002; Caetano et al. 2010; Dekeseredy et al. 2003; Sabol et al. 2004; Wright and Benson 2011; Wu 2009). Few studies, however, have tested whether collective efficacy mediates associations between risky neighborhood characteristics and IPV. Those that have done so, report mixed findings. Wright and Benson (2010) found that, although neighborhood economic disadvantage was strongly associated with IPV, the association was not mediated by collective efficacy. Caetano et al. (2010) found that both structural characteristics and social organization were associated with IPV in expected directions, but that social organization did not mediate associations between the risky structural characteristics of the neighborhood and IPV. However, Browning (2002), found that neighborhood structural characteristics operated indirectly through collective efficacy to influence IPV: highly disadvantaged neighborhoods had lower collective efficacy, which increased the risk of IPV. Although the evidence for collective efficacy as a mediator of associations between risky aspects of neighborhoods and adult IPV is inconclusive, neighborhoods clearly play a role in adult IPV. A number of explanations have been given for why neighborhoods may influence adult IPV (Pinchevsky and Wright 2012). Living in

Neighborhoods and the Perpetration of Adolescent Dating Violence Some of these explanations may also explain why neighborhoods may influence adolescent dating violence. Increased stress from living in risky neighborhoods and the increased tolerance for violence, and specifically for violence against partners, could increase the likelihood of perpetrating dating violence, as could the decreased willingness of neighbors to intervene to stop an adolescent from abusing their partner. Evidence suggests that adolescents who live in structurally and physically disordered neighborhoods with low social organization are monitored less effectively by parents (Byrnes et al. 2011) and neighbors (Elliott et al. 1996), and decreased monitoring has been found to predict the perpetration of dating violence (Schnurr and Lohman 2013). Additionally, adolescents from such neighborhoods have a higher probability of affiliating with a violent peer group than those from less risky neighborhoods (Brody et al. 2001; Rankin and Quane 2002); affiliating with a violent peer group has been found to predict the perpetration of dating violence (Miller et al. 2009). Also, as noted above, IPV is more prevalent in these kinds of neighborhoods, providing adolescents with more role models of partner violence, further increasing their acceptability of the use of violence, and specifically the use of violence against partners. Also, the pervasiveness of aggressive peer and adult role models in risky neighborhoods provides few opportunities for adolescents to learn constructive responses to conflict (Bukowski and Sandberg 1999), which have been found to be protective against dating violence (Wolfe et al. 2003). And finally, evidence suggests that parents who live in neighborhoods of low collective efficacy relay messages to their adolescents that are tolerant of the use of violence (Johnson et al. 2011). Adolescents who receive such messages from parents have been found to be more likely to handle conflictual situations with violence (Kliewer et al. 2006).

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Despite reasons to believe that neighborhood characteristics could influence adolescent dating violence, few empirical studies have examined such associations. Those that have done so, focused primarily on examining the protective effects of social organization, but methodological issues make it difficult to draw conclusions from these studies. For example, Rothman et al. (2011) found that aspects of social organization such as collective efficacy and social control were protective against the perpetration of dating violence when they used aggregated adolescent reports of the neighborhood; this effect, however, could have been due to common-method variance since the effects were not found when using aggregated parent reports of the neighborhood characteristics. Banyard and Modecki (2006) found that two aspects of neighborhood social organization (monitoring and support) were not associated with the perpetration of dating violence. However, the questions used to assess these constructs referred to the adolescents’ particular town and only three towns were represented in the study. Champion et al. (2008) found that neighborhood organization was protective against the perpetration of dating violence, but neighborhood organization was operationalized as community crime and violence and physical aspects of the community, such as empty buildings and graffiti, and thus was conceptually ambiguous. Only two studies have examined associations between neighborhood structural characteristics and the perpetration of dating violence (Jain et al. 2010; Schnurr and Lohman 2013), and neither found that neighborhood structural characteristics influenced the perpetration of dating violence. However, one study was conducted with high-risk adolescents living in extremely poor neighborhoods, and thus there was little variation in neighborhood structural characteristics (Schnurr and Lohman 2013). In the other, collective efficacy was included in the model and that could have attenuated the association between the structural variables and the perpetration of dating violence (Jain et al. 2010), evidence which would be consistent with collective efficacy being a mediator of the association between the risky neighborhood characterisctis and dating violence. No study has looked at whether the physical aspects of a neighborhood are associated with the perpetration of adolescent dating violence and no study has statistically tested whether collective efficacy mediates the relationships between the structural and physical aspects of the neighborhood and adolescent dating violence. Also, urban settings have been the dominant focus for the study of neighborhood influences on adolescent dating violence (Jain et al. 2010; Reed et al. 2011; Schnurr and Lohman 2013). Although social disorganization theory, collective socialization models, and broken window theory grew out of research conducted in urban areas, some

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evidence suggests that they are also applicable to rural areas. Structurally and physical risky neighborhood characteristics and neighborhood social disorganization are present in rural areas (Bouffard and Muftic´ 2006;Osgood and Chambers 2000; Pinchevsky and Wright 2012). Also, some theorists suggest that, because of the importance in rural communities of strong ties with others, the protective effects of social organization may be even greater in rural than urban areas (Websdale 1998; Wilkinson 1984). Additionally, key neighborhood constructs of these theories have been found to be associated with adult IPV among those living in rural areas (Browning 2002; O’ Campo et al. 1995; Wright and Benson 2010, 2011), and there is some evidence indicating that in rural samples, collective efficacy (Browning 2002) and other aspects of social organization (Wright and Benson 2010) mediate associations between structural disadvantage and IPV. However, it is important to note that some studies have found mixed support for these theories in rural areas (Bouffard and Muftic´ 2006; Osgood and Chambers 2000). A substantial number of adolescents live in rural areas in the United States (Gesler and Ricketts 1992) and dating violence is prevelent among rural adoelscnets (Foshee et al. 1996). It is important to determine if the theoretical perspectives put forth by social disorganization theory, collective socialization models, and broken window theory hold when considering the perpetration of dating violence by rural adolescents. Also most of the studies of neighborhoods and dating violence have been cross-sectional, not allowing for an assessment of how neighborhood influences change across development. There is evidence that contributions of and experiences in the neighborhood vary across developmental stages (Ingoldsby and Shaw 2002). During adolescence, the need for autonomy grows (Steinberg and Silverberg 1986) and parental monitoring decreases (Dishion and McMahon 1998). Thus, adolescents may spend more time with peers, including dates, in their neighborhood, and the neighborhood becomes an important social context for adolescents’ out-of-home time. Social environments have been found to exert increasing influence on development across adolescence (Steinberg and Morris 2001). And finally, few of the studies examined whether associations between the neighborhood characteristics and dating violence varied by the sex of the adolescent. However, many studies have found that neighborhood effects on other adolescent health risk behaviors vary by the sex of the adolescent, with most reporting stronger effects for boys than girls (Kim 2010; Pabayo et al. 2011; RamirezValles et al. 2002). The few studies of dating violence to examine sex differences also found stronger associations for boys than girls (Jain et al. 2010; Schnurr and Lohman 2013). The stronger associations for boys have been


attributed primarily to noted differences in parental monitoring of their girls and boys. Parents monitor their sons less closely than their daughters and they encourage their sons, but not their daughters, to be out in the neighborhood (Kupersmidt et al. 1995). Thus, boys who have increased exposure to a risky neighborhood may be more negatively influenced by it, and conversely, boys who have increased exposure to a socially organized neighborhood may confer more protection from it. Thus, positive and negative aspects of the neighborhood may have a stronger influence on the perpetration of dating violence by boys than by girls.

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with increasing grade), and slower desistance from the perpetration of dating violence than those who lived in less structurally and physically risky neighborhoods. Our second hypothesis was that the relationship between structurally and physically risky neighborhoods and adolescent dating violence trajectories would be mediated by collective efficacy. Finally, we hypothesized that the direct and indirect effects of risky neighborhood characteristics on trajectories of dating violence would be stronger for boys than for girls.

Methods The Current Study The current study used longitudinal data to determine whether structural (economic disadvantage, residential instability, ethnic heterogeneity) and physical (physical disorder) aspects of the neighborhood influenced trajectories of the perpetration of dating violence from grades 8 to 12, and whether collective efficacy mediated these relationships. Having a trajectory as the outcome made it possible to determine whether neighborhood influences on dating violence varied across adolescence. We also examined whether the direct and indirect effects of neighborhood characteristics on trajectories of the perpetration of dating violence differed for boys and girls, given the evidence that neighborhood influences have been found to vary by the sex of the adolescent. Additionally, the study examined neighborhood influences on dating violence by adolescents living primarily in rural areas, thus contributing to expanding the scholarship on the influence of neighborhoods on adolescents living in rural areas. Prior research has shown that the trajectory of the perpetration of dating violence follows a curvilinear pattern, with the perpetration increasing from grade 8 to around grade 10.5 when it starts to decline (Foshee et al. 2009; Reyes et al. 2011). Thus, in the current study, we examined the influence of neighborhood characteristics on the level of the perpetration of dating violence at the starting point of the trajectory (the intercept), the rate of change in the perpetration of dating violence over time (slope) and desistance from the perpetration of dating violence. Our first hypothesis was that, after controlling for individuallevel characteristics, adolescents living in structurally risky neighborhoods (high economic disadvantage, residential instability, ethnic heterogeneity) and physically risky neighborhoods (high physical disorder) would have higher initial levels of the perpetration of dating violence at grade 8, steeper slopes of the perpetration of dating violence across grades (indicating stronger neighborhood influences

Data are from the Context Study, a multi-wave longitudinal study of adolescent health risk behaviors (Ennett et al. 2006; Foshee et al. 2011). As part of this study, seven waves of data were collected in school from adolescents in two public school systems located in two predominantly rural counties. Adolescents were in 6th, 7th, and 8th grade at wave 1 and in 10th, 11th, and 12th grade at wave 7. Data were collected every six months for the first six waves and there was a one-year interval between waves 6 and 7. At each wave, students with sufficient English language reading skills and who were not in special education programs or out of school due to long-term suspension were eligible for the study. Response rates ranged from 88.8 % for wave 1 to 72.8 % for wave 7. Parents could refuse consent for their child’s participation by returning a written form or by informing the study investigators via a toll-free telephone number. Also as part of the Context Study, a random sample of parents was selected for telephone interviews from those whose child completed a wave one survey. Three parent interviews were conducted and coincided with wave 1, 3 and 5 of adolescent data collection. Parental response rates ranged from 78.9 % at wave 1 to 73.1 % at wave 5. Interviews were conducted by trained data collectors and lasted approximately 25 min. Additionally, all participant addresses at each wave of data collection were sent to a commercial geocoding firm to be matched with U.S. Census geographies. The geocoding process has been described elsewhere (Karriker-Jaffe et al. 2009). The current study used adolescent data from waves 4 through 7 because those were the waves when the perpetration of dating violence was measured. Neighborhood was defined using U.S. Census block groups based on participants’ geocoded addresses. Neighborhood measures were created using Wave 3 data from parents on their perceptions of their neighborhood, aggregated by block group, and 2000 U.S. Census data (U.S. Census Bureau 2002).

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732 Table 1 Mean and prevalence of the perpetration of dating violence by wave and the sex of the adolescent


Wave

Mean (SD)

n (%)

Boys

Girls

Total

Boys

Girls

Total

1

0.37 (2.38)

0.78 (2.36)

0.59 (2.38)

83 (5.17)

241 (22.18)

324 (14.23)

2

0.97 (4.60)

0.89 (3.20)

0.92 (3.88)

93 (7.68)

188 (20.36)

281 (14.74)

3

0.76 (3.95)

0.77 (2.54)

0.77 (3.24)

80 (6.91)

167 (20.35)

247 (14.44)

4

0.30 (1.82)

0.99 (2.94)

0.69 (2.54)

66 (5.85)

181 (24.47)

247 (16.42)

Analysis Sample The analysis sample (N = 3,218) included students who completed at least one of the last four waves of data collection, except for those who did not have geocode information (n = 20), those who were the only respondent from their neighborhoods (n = 20), those who did not have neighborhood measures (n = 5), those whose grade was out of range of 8–12 (n = 11), and those who were missing data on sex across all waves (n = 138). Of those in the analysis sample, 42.2 % participated at all 4 waves and 21.2, 22.1 and 14.6 % participated only in three, two and one wave(s), respectively. There were no significant differences on demographic covariates, neighborhood characteristics, or the perpetration of dating violence between those in and out of the analysis sample. The analysis sample was nested in a total of 63 block groups in the twocounty areas. Measures The Perpetration of Physical Dating Violence Six items from the Safe Dates Physical Perpetration Scale (Foshee et al. 1996) were used to access the perpetration of physical dating violence. The Safe Dates Physical Perpetration Scale has high internal consistency (a = .94) and is widely used for assessing dating violence among adolescents (Centers for Disease Control and Prevention 2006).Adolescents were asked, ‘‘During the past three months, how many times have you done the following things to a person that you had a date with? Don’t count it if you did it in self-defense or play.’’ The six items were: ‘‘slapped or scratched them,’’ ‘‘physically twisted their arm or bent back their finger,’’ ‘‘pushed, grabbed, shoved, or kicked them,’’ ‘‘hit them with their fist or with something else hard,’’ ‘‘beat them up,’’ and ‘‘assaulted them with a knife or gun.’’ All responses were on a five-point scale ranging from 0 for ‘‘never’’ to 3 for ‘‘10 or more times.’’ Items were summed to a total score at each wave (average Cronbach’s a = .93) and then log- transformed after adding a constant to adjust for skewness. The physical perpetration of dating violence scores and the prevalence of

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perpetrating any dating violence at each wave in the total sample and stratified by the sex of the adolescent are presented in Table 1 for descriptive purposes. Neighborhood Characteristics All neighborhood measures were time-invariant and grandmean centered (Raudenbush and Bryk 2002). Descriptive statistics are presented before grand-mean centering. Neighborhood Economic Disadvantage Neighborhood economic disadvantage was a composite of four variables: the proportion below poverty, the proportion unemployed, the proportion of residents receiving public assistance, and the proportion of female-headed households (Beyers et al. 2003). A mean disadvantage score was calculated for each neighborhood (M = 0.12, SD = 0.06, Range 0.03–0.28). Residential Instability This variable was measured by summing scores for percentage of people who have lived in the neighborhood for less than 5 years and percentage of renter-occupied homes (Sampson et al. 1997) (M = 0.73, SD = 0.22, Range 0.30–1.75). Ethnic Heterogeneity This variable was measured by an index developed by Blau (1977) to capture the range of ethnic heterogeneity. This index ranged from 0 to 1 and was calculated by summing the squared proportions of each racial/ethnic group in the sample and subtracting that from one, with higher number indicating higher level of ethnic heterogeneity (M = 0.42, SD = 0.11, range 0–0.59). Neighborhood Collective Efficacy This variable was created based on parents’ responses of neighborhood social bonding and informal social control (Sampson et al. 1997). For neighborhood social bonding, parents were asked, ‘‘In the last 3 months, how often have you done the following?’’ Four items were included: socialized with a neighbor, asked a neighbor for help, talked to a neighbor about personal problems, and gone out for a social evening with a neighbor. Responses options ranged from 1 for ‘‘never’’ to 4 for ‘‘four or more times.’’ Informal social control was measured by six items. Parents were asked how likely it was that neighbors would step in and do something if teens were damaging property, showing disrespect to an adult, fighting in front of someone’s house, hanging out and


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Table 2 Descriptive statistics for study measures at Wave 1 (N = 3,218) N (%) Race/ethnicity White

1,267 (41.49)

Black

1,544 (50.19)

Other

265 (8.62)

Family structure Single-parent household Two-parent household Parental education Less than high school

260 (11.77) 1,949 (88.23) 182 (9.49)

Graduated from high school

578 (30.15)

Some college, community college, or technical school

362 (18.88)

Graduated from community college or technical school

294 (15.34)

Graduated from college

379 (19.77)

Graduate or professional school after college Failed school year Yes No

122 (6.36) 170 (5.28) 3,048 (94.72)

Moving status Yes No

381 (14.63) 2,223 (85.37)

Dating status Yes No

1,486 (62.83) 879 (37.17)

Sex Male

1,623 (50.44)

Female

1,595 (49.66)

Numbers do not sum up to 3,218 because of missing values

smoking cigarettes, hanging out and drinking alcohol, and hanging out and smoking marijuana. Response options ranged from 1 for ‘‘very unlikely’’ to 4 for ‘‘very likely.’’ The four social bonding items and six informal social control items were averaged to reflect parents’ perception of collective efficacy (individual level Cronbach’s a = 0.85). Neighborhood-level collective efficacy was then calculated by averaging collective efficacy scores reported by parents living in the same neighborhood (block group) (M = 2.77, SD = 0.15, range 1.89–3.47). Neighborhood Physical Disorder This construct was assessed by three questions asking: how strongly the parent agreed or disagreed that people in their neighborhood take good care of their home, their neighborhood is clean, and people leave a lot of junk in their yards. After appropriate reverse coding, response options ranged from 1 for ‘‘strongly disagree’’ to 4 for ‘‘strongly agree.’’ Items were summed and averaged to represent individual reports of

neighborhood physical disorder. The Cronbach’s a at the individual level is 0.74. Neighborhood physical disorder was then created by aggregating parents’ responses of physical disorder in the same neighborhood (block group) (M = 1.51, SD = 0.10, range 1.13–2.27). Control Variables All analyses controlled for individual-level covariates that could potentially confound associations between neighborhood characteristics and dating violence, including race/ethnicity, family structure, and parent education as a proxy for socioeconomic status (Winkleby et al. 1992). Additionally, failed school year, moving status, and dating status were controlled. The sex of the adolescent was conceptualized as a moderator variable. Race/ethnicity, family structure, failed school year, moving status, and the sex of the adolescent were time invariant and thus were grand-mean centered. Parent education and dating status were time-varying and personmean centered (Raudenbush and Bryk 2002). Grade level was used as the primary metric of time and centered at grade 8. Table 2 presents the distributions on each control variable. Race/Ethnicity Race/ethnicity was dummy coded to include White (reference group), Black/African-American, and other race/ethnicity. Family Structure Family structure was a dichotomous variable representing residence in a two-parent versus single-parent household. Seventy percent of the single parent households were headed by the mother. Parents’ Education This variable ranged from less than high school (0) to graduate school or more (5), and was based on the adolescent’s report of the highest level of education achieved by either parent at each wave. Failed School Year This variable was coded such that 0 = no failed school year over the developmental period and 1 = at least one failed school year. Moving Status This variable was coded as 0 = never moved and 1 = ever moved. Dating Status Dating status was measured at each wave (0 = adolescent had never dated and 1 = they had dated). The Sex of the Adolescent This variable was coded as male = 1 and female = 0.

Analysis Strategy Data were reorganized to take advantage of the cohort sequential design of this study such that grade level of the adolescent was used as the primary metric of time to estimate the average trajectories of the perpetration of dating violence across grades eight through twelve. Missing data were handled through multiple imputation using SAS PROC MI and PROC MIANALYZE (SAS Institute Inc. 2008). Multilevel growth curve modeling conducted

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Table 3 Percent of adolescents living in neighborhoods at different levels of risk (high, medium, and low risk) Total (N = 3,218) (%) Economic disadvantage Low

19.02

Moderate

68.86

High

12.12

Residential instability Low

15.29

Moderate High

64.26 20.45

Ethnic heterogeneity Low

19.76

Moderate

68.09

High

12.15

Collective efficacy Low

15.26

Moderate

66.94

High

17.80

the neighborhood predictor and grade2 (to assess neighborhood effects on desistance), between the neighborhood predictor and the sex of the adolescent (to assess sex differences in neighborhood effects on intercepts), and between the neighborhood predictor, time variables, and the sex of the adolescent (to assess sex differences in neighborhood effects on slopes and desistance). The multivariate Wald test was used to determine if the sets of interactions significantly contributed to the model; significant individual interactions were retained if the Wald test was significant. Next, a final combined model was estimated by including all of the structural and physical neighborhood variables, time variables, and control variables, and any interaction found to be significant in the individual neighborhood models described above. To test hypothesis 2, we estimated a series of regression equations to generate coefficients required for testing the significance of the mediated effects in a multilevel context (Krull and MacKinnon 2001). This analysis strategy is discussed in more detail in the results section.

Physical disorder Low

14.16

Moderate

69.06

High

16.78

Low = one standard deviation below the mean, high = one standard deviation above the mean, and moderate = values that fall between ± one standard deviation

using Proc Mixed in SAS version 9.2 (SAS Institute Inc. 2008) was used to test study hypotheses. The first step in the multilevel growth curve modeling was to determine the optimal unconditional model (the dating violence trajectory pattern and random effects without any covariates). We compared models that differed in functional form (i.e., flat, linear, quadratic) and specification of the random effects structure (i.e., homoscedastic, heteroscedastic, autoregressive error structure). The bestfitting unconditional model of dating violence was a quadratic model with an autoregressive error structure and included two random effects (individual random intercept and neighborhood random intercept). Study hypotheses were then tested by estimating a series of conditional multilevel models. To test hypothesis 1, we estimated, in separate models, the effect of each structural and physical neighborhood variable on the intercepts, slopes, and desistance of dating violence. These models included the neighborhood predictor (to assess neighborhood effects on intercepts), the time variables grade and grade2 (to indicate the linear and quadratic slopes respectively), the sex of the adolescent, all control variables, and the interactions between the neighborhood predictor and grade (to assess neighborhood effects on slopes), between

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Results Descriptive Statistics For descriptive purposes the continuous neighborhood variables were categorized into low, medium, and high risk and the percentage of adolescents living in each type of neighborhood are presented in Table 3. Most of the study samples (around 80 %) lived in low- to moderate-risk neighborhoods. The bivariate correlations between all of the study variables averaging across all grades stratified by the sex of the adolescent are presented in Table 4. For girls, neighborhood economic disadvantage, residential instability, and physical disorder were positively associated with the perpetration of dating violence, and neighborhood collective efficacy was negatively associated with the perpetration of dating violence. Ethnic heterogeneity was not associated with the perpetration of dating violence for girls. For boys, none of the neighborhood variables was significantly associated with the perpetration of dating violence. Unconditional Model Results of the unconditional model (Table 5) indicate that the average developmental trajectory for the perpetration of physical dating violence was quadratic, which increased from grade 8 to grade 10 (positive liner slope, b = 0.08, p \ 0.001) and then slightly decreased from grade 10.5 onward (negative quadratic slope, b = -0.02, p \ 0.001). This trajectory pattern is consistent with that found in a

– -0.75**

-0.52* -0.75** 0.47** – 0.06** 0.52**

-0.57**

0.04** 0.61**

-0.64**

Coefficient

Intercept

0.150***

0.020

Linear change rate (grade)

0.082***

0.018

-0.015***

0.004

Level 1 (temporal change)

0.208**

0.006

Level 2 (student within neighborhoods) Individual initial status

0.144**

0.008

0.004**

0.002

Quadratic change rate (grade2) Random effect

Level 3 (between neighborhoods)

0.10**

-0.01

0.03*

0.34**

-0.02

Neighborhood initial status

(9)

SE

Fixed effect

-0.53*

-0.59** 0.57** – 0.72**

– 0.48**

-0.66** 0.71**

0.10**

-0.10**

0.62**

Table 5 Unconditional models of the perpetration of physical dating violence from grades 8 to 12 Effect and variable

–

0.08**

-0.01

0.10**

735

0.01 0.02

0.06** 0.13**

0.02

-0.08**

0.11** -0.10**

0.08** 0.04** 0.08** 0.08**

-0.05** -0.07** -0.12**

-0.07*

-0.02 0.02 0.03* 0.01 0.01

-0.27** 0.25**

0.05 0.11** 0.02

-0.01 0.017 0.03

0.08** 0.28**

-0.01

(10)

(11)

(12)

(13)


Percentage of variance between neighborhoods Initial status

3%

-0.10* 0.05**

prior study using Context Study data (Reyes et al. 2011). Significant random effects were found for intercepts at the individual and neighborhood levels: significant random intercepts at the individual level (b = 0.114, p \ 0.01) indicate significant variation among students within neighborhoods for initial status of the perpetration of dating violence; significant random intercepts at the neighborhood level (b = 0.004, p \ 0.01) indicate significant variation between neighborhoods for mean initial status of the perpetration of dating violence. After decomposition of these variance component estimates, results suggest that about 3 % of the variance in levels of the perpetration of dating violence lies between neighborhoods.

* p \ 0.05; ** p \ 0.01

Neighborhood Effects on Trajectories of the Perpetration of Dating Violence Boys (N = 1,595) above diagonal; girls below (N = 1,623)

0.08** -0.08** (13) Collective efficacy

-0.27**

-0.03

-0.03

-0.08**

0.10**

0.06** 0.10** -0.05** -0.05*

-0.07**

-0.08** -0.09**

0.03** 0.06**

-0.02 0.03* 0.03* 0.27** 0.08**

0.02 0.09**

(10) Residential instability

(11) Ethnic heterogeneity (12) Physical disorder

0.07** 0.26**

0.05** 0.03

0.01 0.04

0.07**

0.09** -0.04** -0.05** 0.11** (9) Economic disadvantage

0.35**

-0.01

0.04**

0.07**

–

0.01 –

-0.05** 0.07** 0.03

-0.01 0.02

-0.02

0.02 0.02

-0.01 0.05**

-0.12** 0.28**

-0.01

(7) Dating status

(8) Moving status

0.08**

-0.04* -0.01

0.01 –

-0.07** –

-0.08** 0.03*

-0.09** -0.02

-0.04** 0.07** 0.03 (6) Failed school year

-0.05** -0.06** (5) Parental education

0.03 -0.01 -0.05** (4) Single-parent household

0.08**

0.01

-0.01

–

0.04**

0.04* 0.04** -0.06** (3) Other

-0.00

-0.32**

–

0.06**

0.03*

0.04

-0.04* 0.17**

-0.10** 0.05** -0.07** 0.04**

0.00 -0.03 0.10** 0.01

-0.32** – 0.14** (2) Black

0.01 – (1) Dating violence

(7) (6) (5) (4) (3) (2) (1)

Table 4 Bivariate correlations between all study variables across all grades, stratified by the sex of the adolescent

(8)

** p \ .01; *** p \ .001

Table 6 presents the fixed effects from the individual models assessing the effects of the neighborhood structural and physical variables on trajectories of dating violence and sex differences in those effects, controlling for individual covariates. There were no significant interactions between any of the neighborhood variables and the grade variables, indicating that the neighborhood structural and physical variables were not associated with slopes or desistance of dating violence. Neighborhood economic disadvantage, residential instability, and physical disorder significantly interacted with the sex of the adolescent to influence intercepts of the perpetration of dating violence. These interactions indicate that the effects of these neighborhood variables on the dating violence intercept differed for boys and girls. The nature of significant interactions with the sex of the adolescent were further investigated by plotting and estimating the differences in the predicted

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736 Fig. 1 Sex differences in the effects of neighborhood risk factors on the perpetration of physical dating violence across Grades 8 through 12

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737

Table 6 Reduced models assessing associations between each neighborhood structural and physical variable and the perpetration of dating violence Effect and variable

Coefficient

SE

Model 1: Disadvantage Intercept

0.195***

0.0198

Linear change rate (grade) Quadratic change rate (grade2)

0.053** -0.014**

0.018 0.004

Sex

-0.185***

0.018

Economic disadvantage Economic disadvantage 9 sex

0.289

0.001

-0.117***

0.002

0.195***

0.019

Model 2: Residential instability Intercept Linear change rate (grade)

0.052**

0.018

Quadratic change rate (grade2)

-0.013**

0.004

Sex

-0.184***

0.018

Residential instability Residential instability 9 sex

0.050

0.048

-0.222**

0.081

Model 3: Ethnic heterogeneity Intercept

0.197***


0.053**

0.018

-0.013** -0.183***

0.004 0.018

Quadratic change rate (grade2) Sex Ethnic heterogeneity

0.019

0.072

0.092

Intercept

0.196***

0.019


0.053**

0.018


-0.014**

0.004

Sex

-0.184***

0.018

Model 4: Physical disorder

Physical disorder Physical disorder 9 sex

0.229

0.131

-0.544*

0.219

All models specified a quadratic trajectory for the perpetration of dating violence with random intercepts at individual and neighborhood levels, and are controlled for race/ethnicity, family structure, parental education, failed school years, moving status, and dating status

p \ .10; * p \ .05; ** p \ .01; *** p \ .001

means of the perpetration of physical dating violence for adolescents living in high-risk (one standard deviation above the mean) and low-risk (one standard deviation below the mean) neighborhoods by the sex of the adolescent. Results are presented in Fig. 1. Girls living in highrisk neighborhoods perpetrated higher levels of physical dating violence across all grades than girls who lived in low-risk neighborhoods. For boys, none of the neighborhood characteristics was associated with the perpetration of dating violence. Results from the model that included all of the neighborhood structural and physical variables, control variables, and significant interactions from the individual

neighborhood models are presented in Table 7. Results show that the interaction between neighborhood economic disadvantage and the sex of the adolescent remained significant whereas other interactions that were significant in the individual models decreased to non-significant when all neighborhood variables were included in the model together. Consistent with the conclusion from the individual neighborhood models, the post hoc analyses indicate that girls living in neighborhoods with a high level of economic disadvantage perpetrated significantly higher levels of physical dating violence across all grades than girls living in neighborhood with a low level of economic disadvantage and that economic disadvantage was not associated with dating violence by boys. Mediation Analyses The mediation analyses were only conducted for girls because there were no relationships between any of the neighborhood characteristics and boys’ perpetration of dating violence. Also because neighborhood effects were found on only the initial status of dating violence, and not on the slopes or desistance of dating violence, the analyses focused on determining if collective efficacy mediates the associations between the neighborhood risk variables on the intercept of the perpetration of dating violence. Results of the mediation analyses for girls are shown in Table 8. The first step in the analyses was to regress collective efficacy on the neighborhood risk variables. The column labeled ba is the regression parameter estimates from this model. Economic disadvantage and physical disorder were negatively and significantly associated with collective efficacy and ethnic heterogeneity was marginally negatively associated with collective efficacy. Residential instability was not associated with collective efficacy. The next step was to determine if collective efficacy was associated with the intercept of the perpetration of dating violence, when controlling for the risk variables. The column labeled bb is the regression parameter estimate from this model. Contrary to expectations, neighborhood collective efficacy was not significantly associated with girls’ perpetration of dating violence when controlling for the other neighborhood predictors and individual covariates. Finally, the mediated effect was determined by multiplying ba 9 bb and determining if the mediated effect was significantly different from zero (indicating mediation) using the Monte Carlo method as suggested by Preacher and Selig (2012). As shown in the last column of Table 8, none of the mediated effects (ba 9 bb) was significant, indicating no support for the hypothesis that collective efficacy mediates the relationships between neighborhood risk factors and the perpetration of dating violence.

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738 Table 7 Combined models assessing effects of neighborhood structural and physical variables on the perpetration of dating violence


Full model b (SE)

Effect and variable

Reduced model b (SE)

Fixed effects Intercept

0.195 (0.019)***

0.195 (0.019)***


0.053 (0.018)***

0.053 (0.018)***


-0.014 (0.004)***

-0.014 (0.004)***

Sex

-0.186 (0.018)***

-0.185 (0.018)***

Black

0.101 (0.021)***

0.101 (0.021)***

Other

0.057 (0.036)

0.057 (0.036)

Single-parent household

0.117 (0.023)***

Parental education Failed school years Dating status

-0.016 (0.011)

0.009 (0.041)

0.009 (0.041)

0.282 (0.027)***

Moving status

-0.060 (0.028)*

Economic disadvantage Residential instability Ethnic heterogeneity Physical disorder

0.117 (0.023)***

-0.016 (0.011)

0.282 (0.027)*** -0.060 (0.028)*

0.185 (0.288)

0.183 (0.288)

-0.029 (0.068)

-0.031 (0.068)

0.005 (0.116) 0.196 (0.220)

0.007 (0.116) 0.201 (0.221)

Interactions Economic disadvantage 9 sex

-0.978 (0.477)*

Residential instability 9 sex Physical disorder 9 sex

-1.118 (0.291)***

0.013 (0.121)

–

-0.181 (0.295)

–

Random effects All models specified a quadratic trajectory for the perpetration of dating violence with random intercept at individual and neighborhood levels * p \ .5; *** p \ .001

Level 1 (temporal change)

0.203 (0.006)***

0.203 (0.006)***

0.132 (0.008)***

0.132 (0.008)***

0.001 (0.001)

0.001 (0.001)

Level 2 (student within neighborhoods) Individual initial status Level 3 (between neighborhoods) Neighborhood initial status

Table 8 Mediated effects of collective efficacy in the relationships between neighborhood structural and physical variables and the intercept of girls’ perpetration of dating violence (N = 1,623) Predictors

ba (SD)

bb (SD)

babb (95 % CI)

Economic disadvantage

-0.798 (0.321)*

-0.066 (0.258)

0.053 (-0.387, 0.538)

Residential instability

-0.093 (0.096)

0.006 (-0.065, 0.087)

Ethnic heterogeneity

-0.459 (0.225)

0.030 (-0.229, 0.319)

Physical disorder

-0.492 (0.146)**

0.032 (-0.231, 0.308)

All models controlled for individual covariates ba: Regression coefficient estimates of neighborhood predictors on collective efficacy bb: Regression coefficient estimates of collective efficacy on intercept of the perpetration of dating violence after controlling for neighborhood predictors

p \ 0.10; * p \ 0.05; ** p \ 0.01

Discussion Although neighborhood characteristics have been associated with many adolescent risk behaviors, including adolescent violence, very little research has examined associations between neighborhood characteristics and adolescent perpetration of dating violence, and none has examined the influence of neighborhoods on trajectories of

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dating violence. The current study used longitudinal data to test three hypotheses guided by developmental considerations and theories of neighborhood influence. Hypothesis 1 proposed that adolescents living in structurally and physically risky neighborhoods would have higher initial levels of the perpetration of dating violence, steeper slopes of the perpetration of dating violence across grades, and slower desistance from the perpetration of dating violence,


than those who lived in less structurally and physically risky neighborhoods. Hypothesis 2 was that collective efficacy would mediate the relationships. And, hypothesis 3 was that the direct and indirect effects of risky neighborhood characteristics on trajectories of dating violence would be stronger for boys than for girls. We found partial support for hypothesis 1, for girls only, and no support for hypotheses 2 and 3. Consistent with hypothesis 1, for girls, risky neighborhood characteristics were associated with initial levels of the perpetration of dating violence. When examining the effects of the risky neighborhood variables one at a time, neighborhood economic disadvantage, residential instability, and physical disorder were significantly and positively associated with the perpetration of dating violence; girls living in high risk neighborhoods reported perpetrating more dating violence at grade 8 than those living in less risky neighborhoods, and that increased risk was maintained across all grades. However, in models that included all of the risky neighborhood variables simultaneously, only economic disadvantage was significantly associated with dating violence by girls. Neighborhood economic disadvantage was highly correlated with residential instability and physical disorder, and thus, the associations found in the individual models between the latter two neighborhood variables and dating violence were likely confounded by economic disadvantage. Chaix et al. (2010) emphasize the importance in neighborhood-focused studies of controlling for the effects of other neighborhood characteristics to prevent neighborhood-level confounding. They stress the potential for neighborhood economic disadvantage, specifically, to confound associations between other neighborhood variables and individual behaviors because neighborhood economic disadvantage is the neighborhood characteristic most likely to drive exposures and resources that can impact behaviors. Neighborhood economic disadvantage has been found to be the most consistent risky neighborhood characteristic associated with intimate partner violence (Benson et al. 2003; Pinchevsky and Wright 2012) and with other adolescent problem behaviors, including youth aggression (Leventhal and Brooks-Gunn 2004; Mrug and Windle 2009; KarrikerJaffe et al. 2009). Further consideration should be given to why or through what process neighborhood economic disadvantage increases the risk for perpetrating dating violence by girls living in primarily rural areas. Counter to the first hypothesis, none of the risky neighborhood variables was associated with the slopes or desistance of dating violence for boys or girls. The expectation was that adolescents living in risky as compared to less risky neighborhoods would have steeper slopes of dating violence, indicating stronger neighborhood effects with increasing grade, and slower desistance from

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the perpetration of dating violence at later grades when desistance typically occurs. Developmental considerations regarding increasing autonomy and unsupervised time across adolescence, and thus greater neighborhood exposure, guided our expectations of stronger neighborhood influences over times (Kupersmidt et al. 1995; Kroneman et al. 2004). However, the neighborhood effects that we did find (for girls only) were maintained across adolescence at the same magnitude. No study has examined neighborhood effects on slopes or desistance from the perpetration of dating violence, and therefore we cannot compare our findings to those from other dating violence studies. However, a number of studies have examined the influence of neighborhood characteristics on trajectories of other forms of youth aggression. Those that found neighborhood effects on the slopes of aggression typically found that the age at which neighborhood effects became significant was younger than the ages encompassed in our sample (Winslow and Shaw 2007; Vanfossen et al. 2010; Kim et al. 2011). Thus, the developmental transition to increased autonomy and unsupervised time in the neighborhood and greater influence by neighborhoods may have already occurred for the study sample. No studies have examined neighborhood effects on desistance from aggression. There was no support for the hypothesis that collective efficacy would mediate the associations between risky neighborhood characteristic and trajectories of dating violence. This hypothesis was tested only for girls since none of the risky neighborhood characteristics was associated with dating violence for boys. Mediation would be evidenced by significant associations between the risky neighborhood variables and collective efficacy, and between collective efficacy and dating violence when controlling for the risky neighborhood variables, and by the presence of significant mediated effects (MacKinnon et al. 1995). Although a couple of the risky neighborhood characteristics (economic disadvantage and physical disorder) were negatively associated with collective efficacy as expected, and although collective efficacy was associated as expected with dating violence in bivariate analyses, collective efficacy was not associated with the perpetration of dating violence when controlling for the risky neighborhood variables and the individual-level covariates, and none of the mediated effects was statistically significant. Future studies are needed that assess other mechanisms besides collective efficacy through which risky neighborhoods influence the perpetration of dating violence such as poor parenting (e.g., low nurturance, harsh and inconsistent discipline, low parental monitoring), exposure to domestic violence, and deviant peer affiliation; each of which have been associated with both risky neighborhoods (Brody et al. 2001; Benson et al. 2003; Rankin and Quane 2002), and with the perpetration of dating violence (Foshee et al. 2013; Jouriles et al. 2012; Leadbeater et al. 2008).

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740

Although no other dating violence study has examined whether collective efficacy mediates associations between risky neighborhood characteristics and dating violence, several examined the main effects of collective efficacy and each found, as we did, little support for collective efficacy being protective against dating violence (Banyard and Modecki 2006; Rothman et al. 2011; Schnurr and Lohman 2013). Although Rothman et al. (2011) found a protective effect of collective efficacy, measured with aggregated adolescent reports, on the perpetration of dating violence, that effect could have been due to commonmethod variance (Williams and Brown 1994) since the relationship did not hold when using parental reports of the neighborhood. Also, their models did not control for individual-level socioeconomic status, or the potential for neighborhood-level confounding because neighborhood effects were examined one at a time. One important dimension of collective efficacy is informal social control as indicated by neighbor willingness to step in and do something if they see adolescents engaged in problematic behaviors. Perhaps the private nature of dating violence, in that it typically occurs when others are not around and even parents and friends rarely know about it (Foshee 1996), render it less susceptible to social control by neighbors than other more openly performed behaviors such as cigarette smoking, substance use, and other forms of violence. Alternatively, it may be that collective efficacy influences dating violence only under certain conditions. Jain et al. (2010) examined whether the effect of collective efficacy on dating violence varied by levels of neighborhood poverty. They found that for males living in neighborhoods of low- to mid-poverty, collective efficacy was protective against the perpetration of dating violence, but for those living in the highest poverty neighborhoods, levels of collective efficacy increased the risk of perpetrating dating violence. Schnurr and Lohman (2013) also found that increased collective efficacy was associated with increased perpetration of dating violence among males in their study that was conducted with low income neighborhoods in several large urban cities. Others have suggested that in high risk neighborhoods characterized by crime, violence, and potential gang activity, collective efficacy may actually increase problematic behaviors (Kaylen and Pridemore 2011) because the increased feelings of connectedness to such neighborhoods may increase the likelihood of youth modeling those violent behaviors (Schnurr and Lohman 2013). Although not presented, preliminary analyses conducted in the current study indicated that there were no significant interactions between collective efficacy and any of the risky neighborhood variables for boys or girls. But the potential for risky neighborhood characteristics and social organization to interact should be examined further in future dating violence studies.

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There was also no support for the hypothesis that the expected effects would be stronger for boys than girls. In fact, we found support for the opposite. In addition to finding associations between several risky neighborhood characteristics and the perpetration of dating violence for girls but not boys, we also found that the intraclass correlation was higher for girls (6 %) than for boys (0 %), suggesting that there was more variation in dating violence perpetrated by girls than boys that could be explained by neighborhood factors. Only two studies have statistically tested sex differences in the effects of neighborhood characteristics on the perpetration of dating violence (Jain et al. 2010; Schnurr and Lohman 2013). Jain et al. (2010) found that the intraclass coefficient was much larger for boys (20.9 %) than for girls (0.08 %). Although in their study there were no main effects of neighborhood characteristics on dating violence by either boys or girls, the interaction that they found between neighborhood poverty and collective efficacy was for boys only. Schnurr and Lohman (2013) found stronger neighborhood effects for boys than girls. A difference between those studies and the current study is that they were conducted with inner-city urban samples whereas the current study was conducted with a primarily rural sample. Because so few studies have examined sex differences in associations between neighborhood characteristics and the perpetration of dating violence, and none have done so in rural areas, whether the effects of neighborhood characteristics on the perpetration of dating violence vary by the sex of the adolescent remains uncertain. There are several potential explanations for why we found few significant neighborhood effects on adolescent dating violence. The maximum values on the risky neighborhood constructs were lower in the current study than in other studies that have used similar indicators (Beyers et al. 2003; Schnurr and Lohman 2013). It has been suggested that a threshold of disadvantage needs to be surpassed before the neighborhood produces risk (Winslow and Shaw 2007). Also, the intraclass coefficient, which characterizes the degree of similarity in levels of dating violence within neighborhoods, is much smaller in the current study (3 %) than has been found in other studies of youth aggression, which generally falls between 5 and 10 % (Leventhal and Brooks-Gunn 2000). Perhaps this is due to the rural nature of the sample. It is also possible that the traditional measures of neighborhood structural characteristics and social organization suggested by social disorganization theory and commonly used in urban studies may in fact be less applicable to studies conducted in rural areas (Kaylen and Pridemore 2011). For example, Kaylen and Pridemore (2011) have suggested that decline in small and local businesses such as drugstores, grocery stores, and hardware stores may be good indicators of social disorganization in


rural areas. Future studies are needed that compare neighborhood effects on dating violence for those living in rural versus urban areas. There are several study limitations that should be noted. One is that the neighborhood variables were treated as time-stable rather than time-varying in that the characteristics of the neighborhood the adolescent lived in at the first assessment, rather than at all assessments, were used in models. Neighborhoods may have more proximal effects on the perpetration of dating violence that could not be captured by measuring the neighborhood characteristics at only one point in time. However, it is important to note that a small percentage of adolescents moved to different neighborhoods over the study period (15 %) and analyses controlled for moving status. Also, neighborhood violence and crime, which could be important predictors of dating violence and confounders of associations between other neighborhood variables and dating violence, were not included in the model. Neighborhood violence and crime have been found to be associated with structural and organization aspects of neighborhoods (Sampson et al. 1997; Hipp 2010), intimate partner violence (Browning 2002; Malik et al. 1997), and with dating violence specifically (Champion et al. 2008; Reed et al. 2011). Only the perpetration of physical dating violence was examined; associations could have been different with other dating abuse outcomes such as psychological and sexual dating abuse. And finally, only the perpetration of dating violence was considered whereas there is some evidence that neighborhood effects may be stronger on the victimization of dating violence than on the perpetration of dating violence (Jain et al. 2010). That the study was conducted in a rural area is both a study weakness and a strength. The weakness is that the generalizability of the findings may be limited only to similar rural areas. The strength is that very little research has been conducted on neighborhood influence on youth risk behaviors including dating violence among those living in rural areas, yet a substantial number of adolescents in the United States live in rural areas (Gesler and Ricketts 1992). The studies conducted in urban areas may not generalize to rural settings. Thus, this study contributes to the scholarship on risk behaviors of rural youth. This study has several other strengths. To our knowledge, it is the first study to examine the relationships between multiple dimensions of neighborhood characteristics and trajectories of adolescent dating violence. The cohort sequential study design allowed for examining not only the effects of neighborhood characteristics on the initial level of the perpetration of dating violence trajectory but also the rates of change in the perpetration of dating violence over time. The study tested the theoretically-based hypothesis that neighborhood risk characteristics will

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influence the perpetration of dating violence through collective efficacy, which has not been previously tested. Sex differences in associations were statistically assessed. We controlled for key individual-level variables that could confound neighborhood effects including adolescents’ race/ethnicity, family structure, and parents’ education. We also controlled for the potential for neighborhood-level confounding and for neighborhood clustering. In addition, the current study included a large and demographically diverse adolescent sample with high response rates for the in-school surveys and parent interviews. The neighborhood measures were not based on adolescent self-reports and the measure of the perpetration of dating violence excluded situations in which adolescents perpetrate dating violence in self-defense.

Conclusion We found partial support for our hypothesis that adolescents living in structurally (high economic disadvantage, residential instability, ethnic heterogeneity) and physically (high physical disorder) risky neighborhoods would be at increased risk for the perpetration of dating violence; girls living in economically disadvantaged neighborhoods were at increased risk of perpetrating dating violence at all grades. We found no support for our hypotheses that the relationship between structurally and physically risky neighborhoods and adolescent dating violence trajectories would be mediated by collective efficacy, and that the direct and indirect effects of risky neighborhood characteristics on trajectories of dating violence would be stronger for boys than for girls. In fact, we found stronger associations for girls than boys. The increased risk of perpetrating dating violence by girls living in economically disadvantaged neighborhoods should be investigated in future studies and considered in efforts targeted at preventing girls from perpetrating dating violence. Acknowledgments This research was funded by the National Institute on Drug Abuse (R01 DA16669) and the Centers for Disease Control and Prevention (R49 CCV423114). Conflict of interest The authors declare that they have no conflicts of interest. Author contributions L.C. conceived of the study, performed data analyses, and drafted the manuscript; V.F. participated in its design, contributed to substantive content of the paper, and helped in writing the manuscript; H.L.M.R. contributed to the overall analytical approach, helped in data analyses, and contributed to the interpretation of the findings; S.E. participated in the design and coordination of the study, and helped to draft the manuscript; C.H. contributed to the substantive content of the paper and helped to draft the manuscript. All authors read and approved the final manuscript.

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Relationship Characteristics Associated with Teen Dating Violence Perpetration.

Longitudinal relationships between individual and class norms supporting dating violence and perpetration of dating violence.

Dating violence victimization and perpetration rates among high school students.

Neighborhood Factors and Dating Violence Among Youth: A Systematic Review.

Development of a Dating Violence Assessment Tool for Late Adolescence Across Three Countries: The Violence in Adolescents' Dating Relationships Inventory (VADRI).

Patterns of Dating Violence Perpetration and Victimization in U.S. Young Adult Males and Females.

Teen dating violence perpetration and relation to STI and sexual risk behaviours among adolescent males.

Longitudinal Mediators of Relations Between Family Violence and Adolescent Dating Aggression Perpetration.

Exposure to Spousal Violence in the Family, Attitudes and Dating Violence Perpetration Among High School Students in Port-au-Prince.

Gender Role Attitudes and Male Adolescent Dating Violence Perpetration: Normative Beliefs as Moderators.

The effects of social capital and neighborhood characteristics on intimate partner violence: a consideration of social resources and risks.

Understanding sexual violence perpetration.

Witnessing Interparental Violence and Acceptance of Dating Violence as Predictors for Teen Dating Violence Victimization.

Understanding sexual violence perpetration.

Stressful life events and the perpetration of adolescent dating abuse.

Direct and indirect effects for neighborhood-based clustered and longitudinal data.

Direct and indirect effects of neighborhood factors and self-care on glycemic control in adults with type 2 diabetes.

Neighborhood influences on girls' obesity risk across the transition to adolescence.

Understanding sexual violence perpetration-reply.

The relation between alcohol use and psychological, physical, and sexual dating violence perpetration among male college students.

Direct and indirect effects of organizational justice on work ability.

The temporal relationship between alcohol, marijuana, angry affect, and dating violence perpetration: A daily diary study with female college students.

Experiences with Dating Violence and Help Seeking Among Hispanic Females in Their Late Adolescence.

Role of Direct and Indirect Violence Exposure on Externalizing Behavior in Children.