Numerous researchers have investigated the predictors of adolescent antisocial behavior (ASB). These ASB predictors have generally been examined through the lens of social learning theory, with parent-child relations as the primary etiological factor (Reid et al., 2002). Less attention has been directed to how these predictors interact across time and context to associate with different types of ASB (i.e., overt and covert antisociality). Using an ecological or social contextual perspective (Bronfenbrenner, 1979), I examined how family, peer, and neighborhood factors predicted boys' and girls' antisocial behavior across time.
I examined a subsample of 1,196 respondents (693 boys, 503 girls, M age = 16.04 years) from the first two waves of the National Longitudinal Study of Adolescent Health (Add Health; Udry, 1998). The predictor variables included parental monitoring, adolescents' affiliation with deviant peers, and adolescents' exposure to violence within their community. Outcome variables included overt ASB and covert ASB measured at two time periods.
A series of hierarchical multiple regression analyses were conducted to identify the direct, additive, and interactive effects of parents' monitoring of the teen, the teens' deviant peers, and the teens' exposure to community violence in predicting the two subtypes of ASB for both boys and girls. Two interaction terms were created to investigate interactive effects: community violence x parental monitoring and deviant peers x parental monitoring. The additive effects of the predictors were significantly related to all eight outcome variables. The predictor variables also collectively accounted for between 13 and 40% of the variance associated with ASB outcomes. Of the predictors, exposure to community violence was the most predictive of ASB when the other variables were controlled.Moreover, parental monitoring moderated the relation between teens' exposure to community violence and later ASB. However, parental monitoring was only an influential predictor of boys' later Covert ASB.
These results suggest that social contextual theory provides a useful framework for predicting ASB outcomes, complementing social learning theory. Additionally, parental monitoring was found to be an effective protective factor for only specific populations (girls) and ASB outcome (covert rather than overt). Furthermore, parental monitoring was found to be an overall weak predictor of ASB (in contrast to the other measured outcomes). Finally, parental monitoring was not found to attenuate adolescent ASB risk factors. Instead, adolescents were found to experience a "double whammy effect," reporting that they committed high amount of sneaky ASB a year later. This "double whammy effect" indicates that adolescents were both exposed to high amounts of violence within their communities and heavily monitored by their parents. Clinical implications from these results are offered with suggestions for clinicians working with antisocial youth. Limitations of these findings and suggestions for future research are discussed.
There has been a significant increase in the prevalence and costs of antisocial behaviors (ASB) committed by American children and adolescents during the last half of the 20th century. According to Blumstein (2000), the amount of homicides committed by juveniles increased by 400% since 1965 (in America alone). In addition, the cost of youth ASB within the United States has been estimated to exceed one trillion dollars (Anderson, 1999). These significant consequences of youth antisocial behavior begs the question, how do we define ASB and what causes persistent youth ASB?
Youth antisocial behavior (ASB) in this manuscript will be defined as any behavior considered illegal, immoral, in contradiction to cultural norms, and causes significant problems within the youth's community. More specifically, ASB has been classified into two subtypes (Frick, Lahey, Loeber, Tannenbaum, Van Horn, Christ, Hart, & Hanson, 1993). These subtypes were derived from empirical studies that ASB falls into two categories of either violent (overt ASB) or sneaky type behavior (covert ASB) after the investigators conducted a factor analysis of parent reported disruptive behaviors (Achenbach, Connors, Quay, Verhulst, & Howell, 1989).
The covert subtype of ASB has been defined as disruptive behaviors that are not violent and are committed with the intention of not being observed by authority figures (e.g., stealing or vandalism) (Frick et al., 1993). On the other hand, overt ASB refers to behaviors that are violent and confrontational (Kazdin, 1992). Each subtype has been hypothesized to demonstrate a developmental trajectory for ASB (Loeber & Hay, 1997).
Both subtypes of ASB have demonstrated construct validity within numerous studies (e.g., Dekovic, 2003; Frick et al., 1993; Kazdin, 1992; Loeber & Schmaling, 1985).
Risk factors that predict ASB have been studied extensively, and three well established risk factors have been associated with the development of ASB in adolescence in particular (Dodge, Coie, & Lynam, 2006). Previous researchers have found an association between low parental monitoring and the development of ASB (e.g., Loeber & Hay, 1997; Patterson, 2002; Patterson & Stouthamer-Loeber, 1984). Another risk factor linked to ASB is an adolescent's affiliation with deviant peers (e.g., Dishion, Patterson, & Griesler, 1994; Granic & Dishion, 2003; Poulin, McCabe, & Dishion, 1999). A final risk factor found to predict adolescent ASB is their exposure to violence within their community (e.g., Farrell & Bruce, 1997; Gorman-Smith, Henry, & Tolan, 2004).
Although there have been many examples of how each of these risk factors predict the development of adolescent ASB (Dodge et al., 2006; Loeber, Burke, & Pardini, 2009), few studies have examined how these risk factors are interrelated and develop during adolescence. Even fewer studies have examined how both interpersonal risk factors (e.g., poor parental monitoring or deviant peer relationships) and community risk factors interact to predict ASB (Simons, Simons, & Burt, 2005). Rather, most previous studies focused on either how interpersonal variables predict ASB or how community variables predict ASB (Simons et al., 2005). Additionally, no study to date has demonstrated how these risk factors develop over time and result in adolescent ASB. The present study attempted to bridge this gap through the investigation of both interpersonal and community contextual risk factors of ASB using a longitudinal data set. Additionally, the present study investigated two leading theoretical frameworks to find a model that best explains the relationships among these risk factors.
An important aspect of the social contextual theory is that it presumes that the impact of major developmental influences, such as parenting practices and peers, is influenced by characteristics of the communities in which youth and their families reside (Tolan, Gorman-Smith, & Henry, 2003). (Bronfenbrenner's (1979) theory of nested ecological (contextual) niches as a model to describe human development has been used to understand different childhood problem behaviors (Tolan, et. al., 2003). More specifically, the social-contextual model demonstrates how children's development can be understood as a function of the relations among multiple environmental contexts that have either distal (removed in time and space) or proximal ("here and now") influence (Bronfenbrenner, 1979). Thus, this theory suggests that when investigating behavior within an ecological context, researchers need to examine both the immediate influences and contextual factors. Moreover, individuals are embedded within several layers of contexts, each providing protection against or acting as a risk factor for the development of problem behavior (Bronfenbrenner, 1979). Additionally, Bronfenbrenner (1979) extended Lewin's interpersonal field theory which predicted that behavior results from the combination of a person's characteristics (e.g., personality, abilities, experiences) and situation. Bronfenbrenner (1979) indicated that the development of a person's behavior results from the combination of these same two variables (person and situation). Thus, the importance of examining a person's psychological development within the naturally-occurring environment is especially salient. Examining the relationship between risk factors utilizing a social-ecological model (based on Bronfenbrenner's theory) can provide an explanation of how adolescent ASB develops within multiple interrelated contexts.
In contrast, the social-interactional model, also known as the coercive family process model, explains youth antisocial behavior as a consequence of early established (and maintained) parent-child relationships (Patterson, 2002). Patterson and colleagues (2002) hypothesized that youth ASB is caused by youths' relations with their parents, and aspects of early parent-child relationships subsequently influence other relationships and risk factors in their life. More specifically, they posited that coercive interactions between parents and children (fueled by negative reinforcement contingencies) bring about disruptive behaviors. Parents of disruptive youth have been found to respond to their children's aversive behavior with inconsistent discipline, aversive behavioral responses (e.g., yelling or nagging), or acquiescence. This interactional style has been found to be cyclical and progressively more aversive for both parent and child. This interactional style leads to increasingly more aversive youth behavior and less skilled parenting (e.g., inconsistent discipline or monitoring; Patterson, 2002). Although these two theoretical models (social-ecological and social-interactional models) have been well established within the literature, little is known regarding how these models (or the previously mentioned risk factors) predict ASB.
An investigation of a model that explains how parenting, peer, and community risk factors of ASB interact may help predict how adolescents develop ASB. Additionally, having a more thorough model and conceptualization of ASB may lead to improved interventions. I evaluated how distal community factors and proximal interpersonal risk factors (e.g., parenting and peer relations) were related to ASB.
This study advances the literature in several important ways. First, selected risk factors addressed multiple settings. Previous studies generally have focused on only one major context (e.g., the family, the community, or peer relationships). In response to the lack of causal studies examining the development of adolescent ASB, the present study utilized longitudinal data derived from the National Longitudinal Study of Adolescent Health (Add Health; Udry, 1998).. Finally, the present study utilized multiple informants across two waves of longitudinal data to investigate the direct and interaction relations between these risk factors and how they influenced the development of antisocial behavior with an adolescent sample.Interpersonal Risk Factors
Parental monitoring and adolescent antisocial behavior. Aspects of parenting have been correlated with the development of antisocial behaviors among youth (Forehand, Miller, & Dutra, 1997). More specifically, several studies have demonstrated how poor parenting (poor supervision, inconsistent and harsh discipline) leads to chronic ASB (e.g., Dishion, Patterson, & Stoolmiller, 1991; Graber, Nichols, & Lynne, 2006; Griffin, Botvin, Scheier, Diaz, & Miller, 2000; Loeber & Hay, 1997; Patterson & Stouthamer-Loeber, 1984). Although there has been considerable evidence linking poor parenting with future youth ASB (consistent with the social-interactional model), parental monitoring in particular becomes a crucial predictor of ASB as children move into adolescence and are granted more autonomy (Dishion & McMahon, 1998).
Despite its importance as a proximal predictor of subsequent ASB, parental monitoring can be difficult to measure. Stattin and Kerr (2000) argued for examining parental knowledge of an adolescent's whereabouts and activities rather than the parent's report of the youth's whereabouts (Stattin & Kerr, 2000). These investigators found that parents were reporting what their teen had reported to them, rather than the parent's observations of their teen's behavior. These authors found that the quality of the parent-child relationship was a strong predictor of the accuracy of the parental knowledge of the youth's whereabouts. This link between parental lack of knowledge and the development of ASB have been replicated elsewhere (Laird, Pettit, Bates, & Dodge, 2003). These investigators found that parents who were the most knowledgeable of their child's whereabouts had adolescents who demonstrated the least ASB (Stattin & Kerr, 2000). Additionally, how parents balance control (monitoring and discipline) with granting autonomy to the youth (reported by the teen) has been cited as a potentially fruitful area of investigation (Simons et al., 2005). Consequently, in the present study I examined adolescents' report of parental involvement and parents' reported knowledge of their child's peers and activities. Through the use of both reporters, a more accurate assessment of parental monitoring may provide a more accurate prediction of ASB.
Deviant peer relations as predictors of adolescent ASB. Another powerful proximal risk factor for adolescent ASB demonstrated in numerous studies is the presence of deviant or delinquent friends (Dishion, Eddy, Haas, Li, & Spracklen, 1997; Dishion et al., 1994; Poulin & Dishion, 1999). One explanation of this strong correlation is that externalizing adolescents are often rejected by non-externalizing peers (Dishion et al., 1994). Thus, these rejected youth appear to seek out peers with similar behavioral repertoires (Dodge, Coie, & Lynam, 2006). Another hypothesis that explains this association is the modeling effect of deviant peer affiliation (Bandura, 1973; Salzinger, Ng-Mak, & Feldman, 2006). Thus, externalizing teens grouped together will influence each other through modeling of antisocial acts. For example, teens who experience a greater number of risk factors of ASB (e.g. prior ASB, poor family functioning, or have deviant peers) often are grouped together due to similar school achievement, similar community characteristics, or attending the same school (Dishion et al., 1994). These explanations of this link (i.e., having deviant peer affiliations and concurrent ASB) support the need to investigate an adolescents' ecological context.
Aside from the etiological questions surrounding deviant peer affiliations, investigators have noted measurement challenges for assessing deviant peer affiliations (Bauman & Ennett, 1996). First, teens tend to overestimate how similar they are to their peers (Mounts & Steinberg, 1995). Second, most studies often measure deviant peer affiliation by asking the target teen to report the characteristics of his or her peers (Bauman & Ennett, 1996), and teens appear to not beaccurate reporters of their peers' behavioral repertoires in comparison to the peers themselves (Reitz, Dekovic, & Meijer, 2006). In contrast, the present study addressed this measurement issue through its unique assessment of deviant peer affiliation by asking peers to report on their own behavior. The present study builds on a previous investigation (Scaramella, Conger, & Spoth, 2002) through its assessment of the peer's behavior through peer reports rather than the sampled adolescent's reports (Bauman & Ennett, 1996; Reitz et al., 2006).
Parent and peer risk factors of antisocial behavior. Notably, parenting behaviors and peer affiliations interact when accounting for the development of antisocial behavior. As adolescents develop, parental monitoring has been found to wane in significance relative to the increasing influence of peers (Brendgen, Vitaro, & Bukowski, 2000; Brown, Mounts, Lamborn, & Steinberg, 1993). However, studies have suggested that parental monitoring continues to predict ASB indirectly by mediating peer influences (Henry, Tolan, and Gorman-Smith, 2001), and directly (Dishion & McMahon, 1998). For example, Henry et al. (2001) conducted a longitudinal study to examine how parent and peer influences prospectively predicted violent and nonviolent delinquent behavior. These investigators found that parenting predicted both forms of delinquent behavior directly and indirectly (through its mediation of the relationship between deviant peer affiliations and ASB). Similarly, Scaramella, Conger, and Spoth (2002) compared different models to determine how peer and parenting risk factors best predicted adolescent delinquency. They found that a social interactional model did not adequately fit the data, whereas a social-contextual model did (Scaramella, et. al., 2002). Prior ASB and deviant peer affiliations mediated the relationship between parenting and later delinquency during adolescence. The authors indicated that the findings support the notion that parents structure and shape the teen's environment (Scaramella, Conger, & Spoth, 2002). The previously reviewed finding that poor parenting mediates the relationship between teens who have deviant friends and ASB appears to have two explanations (e.g., Henry et al., 2001; Scaramella et al., 2002) These investigators hypothesized that this relationship results from either the teens established interactional style (e.g., social-interactional model) or the combination of the teen's personal characteristics and his or her situation (e.g., social-ecological model).Community Risk Factors
Exposure to violence within the community is another significant risk factor for antisocial behavioral outcomes, such as increased aggression and desensitization to violent acts (e.g., Ng-Mak, Salzinger, & Feldman, 2004). Extensive evidence suggests that adolescents exposed to violence within their community is a strong risk factor for ASB (Farrell & Bruce, 1997; Farrell & Sullivan, 2004; Gorman-Smith, Henry, Tolan, 2004; Nofziger & Kurtz, 2005; Shahinfar, Kupersmidt, & Matza, 2001; Spano, Rivera, Vazsonyi, & Bolland, 2008; Spano, Vazsonyi, & Bolland, 2009).
Aside from this variable's robust direct relations with ASB, how it is interrelated with other risk factors has been found to be salient as well. This proximal risk factors mentioned before (parent and peer) is hypothesized to be embedded within this distal community risk factor. For example, Richards and colleagues (2004) found that the more time a youth spent in unmonitored and unstructured contexts with his or her peers, the more likely the youth had been exposed to community violence.
Parenting practices have been demonstrated to be related to community violence exposure. Mazefsky and Farrell (2005), utilizing a cross-sectional design, found that parenting practices (i.e., supervision and discipline) mediated the relationship between witnessing violence and later aggressive behavior within a rural population. In contrast, Gorman-Smith et al., (2004) conducted a longitudinal study and found that poor parenting skills and a strained parent-child relationship were linked to a higher incidence of youth violence and exposure to violence. However, another longitudinal study demonstrated a direct relationship between violence exposure and development of ASB after controlling for parenting variables (e.g., discipline, monitoring, and parent-child relationship; Miller, Wasserman, Neugebauer, Gorman-Smith, & Kamboukos, 1999).
Parenting practices appears to attenuate the effects of being exposed to community violence when predicting ASB. Miller, et. al. (1999) not only demonstrated longitudinal relations between youth exposure to violence and future ASB, but the severity of parent-child conflict moderated the relationship between these two variables. These investigators concluded that youths exposed to high violence and experience poor parent-child relations were at greatest risk for antisocial behavior outcomes, whereas youth exposed to high levels of violence and positive parent-child relations were not at risk for future ASB. (e.g., Gorman-Smith et al. 2004).
Violence exposure appears to be related to deviant peer affiliation when predicting later ASB. Nofziger and Kurtz (2005) found that witnessing peer violence was a strong mediating influence between having deviant peers and youth's future violent behavior. Other studies have found similar findings that adolescent that have deviant peers are more likely to be exposed to community violence (Brook, Brook, & Whiteman, 2007). These findings (e.g., proximal contexts mediating or moderating distal contexts) beg the question, how do these three risk factors interact to predict ASB?Multiple Contextual Predictors of Antisocial Behavior
Because adolescents are greatly influenced by people and situations outside of their families, it is important to examine adolescents within multiple contexts (e.g., school, community, and peer systems) beyond the youth's individual characteristics (e.g., temperament or attribution style in order to delineate a model that accurately predicts ASB.
Those studies that have examined multiple contexts have predominantly looked at community structure (e.g., concentrated poverty, neighborhood disadvantage, employment rates) or community social organization processes (e.g., collective efficacy; Simons et al., 2002) as predictors of ASB. For example, Rankin and Quane (2002), utilizing a cross-sectional study, demonstrated that higher rates of community collective efficacy, a combination of both neighborhood cohesion (how close-knit and available neighbors are for each other) and social control (or the amount of help parents get from adult neighbors) found within a neighborhood, was associated with strong parental monitoring, fewer deviant peer affiliations, and reductions in reported adolescent ASB. These investigators found that parenting skills and deviant peer affiliations mediated the relation between collective efficacy and ASB. The authors suggested that ASB seems to have a stronger relation to collective efficacy than to social structural variables, like residing in a disadvantaged neighborhood. In addition, these investigators found that collective efficacy moderated the relationship between parental monitoring and ASB (Rankin & Quane, 2002).
Although these findings support the social-contextual model of the development of ASB, causal inferences require longitudinal data. TolTTolaToldan, Gorman-Smith, and Henry (2003) conducted a multi-wave longitudinal study to determine how neighborhood structural characteristics, community collective efficacy, peer relationships, and parenting practices predict youth violence. The authors demonstrated that interpersonal risk factors mediated the relationship between neighborhood structural characteristics and later violent behavior. Furthermore, they found that parenting practices mediated the relationship between by peers' violence and the teenager's own violent behavior. The authors recommended that future investigations include more specific community contextual factors, such as exposure to violence within the community, to increase predictive power. Notably, previous studies have documented similar findings when they utilized the broader outcome measure of problem behaviors rather than more specific outcomes (e.g., delinquency, violence, aggression, drug use, etc.; Barrera, Biglan, & Ary, 2001).
These risk factors have been shown to be interrelated when predicting more wide-ranging outcomes such as delinquent behavior rather than violent behavior. Chung and Steinberg (2006) examined how community risk factors (i.e., social organizational and collective efficacy) were related to peer and parenting influences in the prediction of delinquent behavior. These investigators utilized cross-sectional data that demonstrated that weak neighborhood social organization was indirectly related to delinquency through its association with deviant peer affiliations and parenting practices. Additionally, the authors found that deviant peer affiliations fully mediated the relation between parenting practices and delinquency. Further, low neighborhood social cohesion was not directly related to delinquency. The authors stressed that models predicting delinquency are often oversimplified due to the investigation of single contexts. Instead, they suggested that parent and peer influences can be combined as a joint risk factor (Chung & Steinberg, 2006). Echoing previous findings from Tolan et al. (2003), Chung and Steinberg (2006) suggested that examining a more specific community factor (e.g., exposure to violence) may provide a more fruitful investigation of how these variables are interrelated and provide a more effective predictive model of ASB.
Similarly, exposure to community violence has been found to be related to the interpersonal risk factors (peer and parent influences of ASB) Salzinger, Ng-Mak, and Feldman (2006) examined four different variables, including negative parenting, deviant peer affiliations, stressful family contexts (i.e., stressors on family, neighborhood collective efficacy, and neighborhood fear), and the youth's behavior (both attributions about and actual delinquent behavior). The investigators found that adolescents' deviant peer affiliations (the proximal contexts) mediated the effects of both stressful family contexts and negative parenting (the distal contexts) in the prediction of later risk for exposure to community violence as an outcome (Salzinger et al., 2006). The proximal contexts were found to mediate the distal contexts when predicting exposure (Salzinger et al., 2006). However, this study only focused on future exposure to violence as an outcome to the neglect of antisocial behavioral outcomes.TTThe Present Study
I investigated the relationships among adolescents' exposure to community violence, parental monitoring, adolescents' deviant friends, and adolescents' committing antisocial acts. The present study used archived longitudinal data from the National Longitudinal Study of Adolescent Health (Add Health; Udry, 1998). This stratified national sample of 7th to 12th grade students was collected in multiple waves and used multiple informants across different settings. This data set was used to predict how adolescent covert and overt antisocial behavior develops within the social context of interpersonal and community risk factors (Udry, 1998). I conducted eight hierarchical regression analyses (four models using data from boys and four models using data from girls) to assess relations between the predictor variables and both ASB outcomes. For each gender, two initial models utilized cross-sectional data (with predictors and both subtypes of ASB obtained in Wave 1), and two subsequent models using longitudinal data (with predictors from Wave 1 and both subtypes of ASB measured in Wave 2).
I expected that parental monitoring would directly predict ASB. Specifically, the first research hypothesis (H1) predicted that poor parental monitoring would lead to a higher incidence of ASB at both Wave 1 and Wave 2. Thus, parents' monitoring was expected to be a strong protective influence (by itself) in the development of adolescent ASB.
parent-child relations can both mediate and moderate the relationship between contextual risk factors and ASB (e.g., Rankin & Quane, 2002; Tolan et al., 2003). Additionally, the protective influence of parental monitoring was expected to interact with the risk factors and act as a mechanism in the reduction of access to ASB risk factors (in the present study, unsafe environments and access to deviant peers).
The present study methodologically advances research in this area in several ways. First, parental monitoring was measured using multiple reporters (parent and youth self-report). Second, the present study examined peer affiliation using peers' reports of their own (the peer's) behavior. Third, this study combined both interpersonal contexts and a community context in the prediction of ASB (e.g., with a more specific community risk factor, violence exposure). Finally, few studies have examined these hypotheses utilizing a stratified national (and longitudinal) subsample of adolescents.
MethodData and Participants
The present study utilized a subsample of the publicly released data (in-home parent surveys, in-home adolescent interviews, and in-school questionnaires) from the National Longitudinal Study of Adolescent Health (Add Health; Udry, 1998). The investigator's use of this secondary data set was approved by Roosevelt University's Institutional Review Board. The Add Health study assessed a nationally stratified representative sample of adolescents in grades seven to twelve, and examined how various social contexts influence adolescents' health behaviors and psychological adjustment. The sampling design is comprehensively reviewed on the National Longitudinal Study of Adolescent Health website http://www.cpc.unc.edu/addhealth/design(Bearman, Jones, & Udry, n.d.).
In the present study, samples from Wave 1 (home interview, parent survey, and in-school questionnaire collected between 1994 and 1995) and Wave 2 (collected in 1996, containing home interview data only) were utilized because they both measured the target age group. Add Health's Wave 3 was not used because of its focus on an adult sample (Bearman et al., n.d.). The present study utilized predictor, control, and ASB outcome variables measured in Wave 1 and ASB measured in Wave 2 (approximately one to two years after the Wave 1 variables were measured).
Among the adolescents in the original data set, 48% were boys and 52% were girls. Adolescents' ages ranged from 12 to 21 years (M = 16.04, SD = 1.77). Approximately, 66% of the sample was Caucasian American, and approximately 34% of the sample was an ethnic minority.
Wave 1 included 20,745 adolescents who were interviewed both in their homes and at their schools. Anonymity was provided to participants who listened to these pre-recorded questions on earphones and entered their responses directly into laptop computers (Udry, 1998). A total of 17,715 parents or adult caregivers also completed an
interview. The Wave 2 sample included approximately 15,000 of the same students from Wave 1. The sample for Wave 2 received the same Wave 1 in-home interview, except participants who were in the 12th grade during Wave 1 were not re-interviewed at Wave 2, and participants who were in only the Wave 1 disabled sample were not re-interviewed as well.
The sample (N = 1196) for specific analyses in the present study used all participants who meet the inclusion criteria, including adolescents who (a) participated in both Wave 1 and 2, (b) had a parent or adult caregiver provide data in Wave 1, (c) had at least one peer nominate the respondent to reciprocate friendship in network data in Wave 1, and (d) had no missing data for relevant items on either wave of data collection. The selection criteria provided a sample of 1,196 respondent/parent-adolescent pairs. Among the parents, 21.2% were men and 78.8% were women. Respondents'/parents' ages ranged from 20 to 80 years (M = 41.66, SD = 6.54); their education ranged from elementary school to professional training beyond college. Of the respondents/parents, 80.7% were mothers of the target adolescent, 3.8% were fathers, 1.6% were grandmothers, and 13.9% were other familial and non-familial caretakers. Sixty-one percent were Caucasian American, 19.4% were African American, 8.3% were Hispanic American, 1.3 percent was Native American, and 2.7% were Asian/Pacific Islander American. Demographic data were also collected from the 693 adolescent boys (57.9%) and 503 adolescent girls (42.1%), who ranged from 13 to 18 years (M = 15.67, SD = 1.68).Measures
Demographic information. These items included the target child's race (dummy coded as Caucasian American or ethnic minority), gender (boy or girl), and age (at the time of data collection of Wave 1). The actual items used to construct each demographic variable are found in Appendix A.
Overt antisocial behavior outcomes from waves 1 and 2. The measure of antisocial behavior for both subtypes was derived from responses to a series of items collected during both waves of in-home interviews. The "Overt ASB" measure included items indicating violent or aggressive behavior directed toward people. Examples of the 6 items utilized for this scale included fighting, injuring another person, and using or threatening to use a weapon. See Appendix B for a complete list. For each, adolescents reported how often they participated in the action during the past twelve months and was coded from 0 (if youth did not participate during the past year) to 3 (if youth participated in the act several times). This 6-item subscale provided a Cronbach's alpha (a) of .73 for Wave 1 and .75 for Wave 2.
Covert antisocial behavior outcomes from waves 1 and 2. The "Covert ASB" measure included behaviors such as theft, property damage, rule-breaking, and deceitfulness. In contrast to Overt ASB, this scale assessed forms of antisociality that did not entail physical aggression. Specific examples of the 8 items include: painted graffiti, committing property damage, shoplifting, stealing, lying to parents, and selling drugs. See Appendix B for a detailed list. More specifically, the items asked adolescents to report how often during the past 12 months they have participated in these activities. For each, adolescents reported how often they participated in the action during the past twelve months and was coded from 0 (if youth did not participate during the past year) to 3 (if youth participated in the act several times). The 8-item subscale yielded a Cronbach's alpha (a) of .78 for Wave 1 and .75 for Wave 2.
Parent- and adolescent-reported parental monitoring predictor. This measure assessed both parents' reports of their monitoring of their adolescent's activities as well as adolescents' perceptions of parent monitoring (Grotevant et al., 2006). Items asked parents about their adolescent's best friend and their involvement in their adolescent's day to day life, such as: ''Have you met this friend in person?;'' ''Have you met this friend's parents?;'' "Have you talked about grades with adolescent?;" "Have you participated in a school fundraising activity with adolescent?;" and "Have you talked about other school activities with teens as well?" Adolescents' perceptions of parental monitoring were measured with several items that assessed whether they participated in different activities with their parents, using a scale of yes (0) or no (1). Representative shared tasks included: "went shopping," "played a sport," "talked about a personal problem," "talked about life," "talked about school grades," "went to a religious services," "went to a movie together," and "worked on a school project together." See Appendix C for a list of items that comprised this scale. The 24-item scale had a Cronbach's alpha (a) of .72 in this sample.
Peer-reported deviant peer affiliation predictor. This measure utilized peer network data from the Add Health study. Participants in the Add Health study had the opportunity to nominate friends during the in-school survey that allows the measures of their peers' reported behavior (Wave 1). More specifically, the Add Health study provides access to the respondents' peer behavior which is linked to the respondent. Peer deviant behavior was calculated by first defining the respondent's peer network, which comprised adolescents whom the respondent nominated as a friend and those adolescents who nominated that respondent (e.g., the send-and-receive network). The scale assessed peer participation in minor deviant acts. Peers were asked how often during the past year they had gotten drunk, smoked cigarettes, skipped school without an excuse, and been involved in serious physical fights. Responses to items ranged from 0 (never) to 5 (three to five days a week). See Appendix C for a complete list of items. The 6-item scale, labeled deviant peer affiliation, had a Cronbach's alpha (a) of .81 in this sample.
Adolescent-reported community violence exposure predictor. This measure, derived from the in-home survey conducted in Wave 1, included the following items relevant to witnessing or being the victim of community violence, each coded on a yes (0) or no (1) basis: "Have you witnessed someone being shot or stabbed?;" "Have you had a knife or gun pulled on you?;" "Has someone shot or attempted to shoot you?;" "Has some cut, stabbed, or attempted to cut/stab you?;" and "Has someone or group of people jumped you?" See Appendix C for a list of items that were used for this scale. This 5-item scale, labeled community violence exposure, had a Cronbach's alpha (a) of .71 in this sample.
ResultsOverview of data analyses
After calculating descriptive statistics for variables of interest, I examined the bivariate relations among the predictors and outcome measures to determine significant correlations and to rule out multicolinearity effects. I then examined the direct and interactive multivariate relations between parental monitoring, community violence exposure, and peer deviance in the prediction of ASB over time to investigate the present study's hypotheses. Both predictor and outcome variables were standardized prior to bivariate and multivariate regression analyses.
To test the multivariate relations, I performed a series of 3-step hierarchical multiple regression analyses to identify the significance of parental monitoring, deviant peers, and exposure to violence in predicting the two subtypes of ASB (i.e., covert and overt) for boys and girls, separately. Step one of all models included the covariates of age (at Wave 1) and race (coded as either Caucasian or not Caucasian). For the four regression models predicting ASB outcomes at Wave 2, the corresponding teen-reported ASB at Wave 1 was entered as an additional control variable. Step two added the three primary predictor variables to the model (i.e., parental monitoring, deviant peer affiliations, and exposure to community violence). Two interaction terms were entered into Step three to test for moderated relations (parental monitoring x exposure to community violence for the first term, and parental monitoring x deviant peer affiliation to create the second term). Thus, there were a total of eight regressions predicting ASB outcomes (each included a specified gender, a subtype of ASB, and a measured time period).Descriptive Statistics and Correlations Hypothesized Multivariate Relations
As summarized in Tables 3 through 6, I performed eight hierarchical multiple regression analyses to explore direct, indirect and interactive contributions of contextual risk factors (parental monitoring, deviant peer affiliations, and community violence exposure) and both types of ASB, for boys and girls separately at each time period. The predictors as a set were significantly related to all outcome variables and collectively accounted for between 13 and 40% of the variance associated with the multiple ASB outcomes. Of the predictor variables, exposure to community violence was found to be most predictive of ASB when all other variables were controlled.
Hypothesis 1 (H1). To test H1, I examined whether parental monitoring directly predicted ASB. Overall, parental monitoring was found to be the weakest predictor variable, only significantly predicting two of the eight possible outcomes. As demonstrated in Table 3, parental monitoring for girls was significantly associated with Covert ASB at Wave 1 ( = - .12, t(942) = -3.30, p < .001). This inverse relationship suggests that greater parental monitoring may serve as protective influence for girls' initial sneaky behavior, as predicted in H1. As shown in Table 5, parental monitoring significantly predicted boys' Covert ASB at Wave 2 ( = .09, t(516) = 2.58, p < .05).Discussion
I investigated whether interpersonal or community based risk factors of ASB, had a relatively greater impact (directly predicting outcomes) and how they were interrelated on two subtypes of ASB. This comparison of the risk factors' direct impact (in the prediction of ASB) was made by juxtaposing the two previously reviewed models (social contextual versus social interactional) using a large scale data set analyzing boys and girls separately over two periods of time (measured approximately a year apart). In addition to analyzing direct relations, I investigated how these risk factors were interrelated by examining the interactions and indirect relations between three adolescent risk factors associated with the development of antisocial behavior. These analyses were conducted using several samples with outcomes that varied by gender, time period, and either aggressive or sneaky subtypes of ASB.
The results from the present investigation appear to answer several questions. First, how salient is the social contextual model when conceptualizing the development of adolescents' ASB? Second, does the gender of the adolescent affect how parental monitoring influences the development of ASB? Third, does parental monitoring vary in its ability to predict either measured subtype of ASB? Fourth, how strongly is parental monitoring related to all measured outcomes? Finally, does parental monitoring act as an intervening variable, protecting teens from future ASB?
Relevance of the social contextual model. The social contextual model appears to be a relevant framework for conceptualizing the development of adolescent ASB. This assertion was supported by the findings that a community contextual variable was the most powerful and pervasive predictor of ASB outcomes (when both interpersonal and demographic risk factors were controlled). More specifically, the influence of community violence exposure was a pervasive, robust, and significant predictor when conceptualizing the development of adolescent ASB. Of note, even after controlling for adolescents' previous ASB from Wave 1, adolescents' exposure to violence was found to still strongly predict future ASB. The findings appear to support the need to examine interpersonal risk factors like family or peer risk factors, but also consider more distal contextual risk factors (community or school) when conceptualizing the development of ASB.
The present study replicated previous findings that adolescents' exposed to community violence significantly predicted violent behavior (Gorman-Smith, et. al., 2004; Trentacosta, Hyde, Shaw, & Cheong, 2009). The present study extended this finding by demonstrating that community violence exposure can be a risk factor for current and future sneaky or undetected antisocial behavior as well (not just current and future violent behavior). The present study also demonstrated that the patterns of relations between risk factors and outcomes were influenced by gender, time of measurement, and the type of ASB. These patterns of relationships appear to be, in general, influenced by the community context. Thus these patterns support the need to investigate the interactions of these multiple contexts to further our understanding of how ASB develops. Other investigators have supported this notion as well (e.g. Miller, et.al., 1999; Scaramella, et.al., 2002; Tolan, et.al., 2003).
Previous theorists have provided both single and multiple path models to explain the toxic effects (e.g. perpetration of ASB) of living in a dangerous neighborhood. An enduring single path model explanation has been the social learning framework. This framework focuses solely on how modeled (reinforced) behavior, which is fueled by witnessing ASB perpetrated by similar peers. However, this framework neglects the influence of adolescents' unique interpersonal transactional styles and their attributions affecting (the persons' unique characteristics) the situation or interpersonal field (Bandura, 1973). For example, teens exposed to violence and other antisocial acts will imitate (due to modeling effects) the behaviors of others within their environment resulting in later ASB.
Previous theorists have extended this model using multiple paths and transactional models. For example, Patterson (2002) extended social learning theory, positing that adolescent ASB results from poor parent-child relationship patterns (i.e., coercive family process) that are reciprocal in nature. This reciprocal coercive pattern has been linked with parents who are less skilled in parenting, monitor less, and overall take less of an active role in the teens' life via communication or interaction (e.g., Patterson & Stouthamer-Loeber, 1984).
The social contextual model extends the reciprocal influences of the coercive family process, adding the caveat that adolescent ASB outcomes are multi-determined in etiology. This framework stresses the importance of determining the multiple influences impinging on the family, especially the environment outside of the home. For example, consider each perspective of the participants within the parent-child relationship and the typical conflicts faced by both the parent and child. Parents raising their children in dangerous neighborhoods are expected to protect their teen from harm, yet help the teen develop into autonomous and socialized adults. From the teen's perspective they are at a developmental stage where they learn by continually testing boundaries to gain more independence. If they lack support from their parents due to a coercive family process and live in a community devoid of positive role models (yet full of violent ones), teens will gravitate to their deviant peers and the peers' behavioral repertoire. This conflict (adolescents' need to become independent within the context of a warm and safe environment and parents need to provide this relationship) frequently arises during adolescence but appears to be exacerbated by living in a dangerous neighborhood. This conflict appears to be exemplified by an investigation that found that authoritative parenting styles moderates the influence of parent management strategies when predicting less teen drug use (Mounts, 2002). Another important finding that supports the social contextual model is the gender differences between parental monitoring effectiveness in reducing ASB.
Gender differences in the influence of parental monitoring. Significant differences were found in how each gender responds to parental monitoring. Interestingly, a paradoxical effect was found where boys who were heavily monitored by their parents were found to be worse off a year later, committing more Covert ASB at Time 2. This effect appears to be inconsistent with previous findings (i.e., Laird, Pettit, Bates, & Dodge, 2003). Alternatively, girls were found to respond to parental monitoring with less ASB outcomes measured at both time periods. However, parental monitoring was found to be significantly correlated with girls (both time periods) and boys (at Time 1) Overt ASB.
Boys and girls parent-child relations (affecting the adolescents' expectations of independence granted from their caregivers) may explain the gender differences found. For example, males might expect more autonomy granted by their caregivers in comparison to their female counterparts. Vieno, Nation, Patore, & Santinello (2009) suggested the importance of closeness in parent-child relations and perceived control contribute to different outcomes for both boys and girls. Girls were found to respond well to being close and monitoring by parents, whereas boys were found to be less close to parents and respond with parental monitoring behaviors as a control, which was linked with the perpetration of more ASB. Gorman-Smith & Loeber, (2005) reported similar findings. These investigators found that girls' covert ASB outcomes (within their sample) were found to be significantly associated with parental monitoring, whereas, boys' covert and overt ASB outcomes, were not. Another important finding that extends the argument for examining multiple contexts of ASB is how parental monitoring appears to be related with only violent or aggressive subtypes of ASB.
Parental monitoring and its influence on aggressive behavior. Parental monitoring was found to predict sneaky ASB but not aggressive ASB across all outcomes. Thus, the teens self-reports of violent acts does not seem to be influenced by their parents' monitoring both at the time monitoring was measured nor a year later. These findings were unexpected and inconsistent with the previous literature (e.g. Dishion and McMahon, 1998).
A plausible explanation for this finding is the reactive and impulsive nature of most aggressive acts (Dodge, et. al., 2006). These acts may not be as influenced by parent control strategies. In contrast, sneaky behaviors might be less impulsive and proactive, and possibly more influenced by parent control strategies (e.g. if I get caught stealing this candy bar, I will get grounded or lose my cell phone).
Moffitt's (1993; 2003) "Life Course Persistent and Adolescent Late Onset" subtypes of ASB developmental model may help explain this finding as well. Children who fall under the "life course persistent" rubric demonstrate lifelong difficulties with violent behavior. As such, they will likely be more prevalent in the measured Overt ASB variables from the present study. Due to these children's longstanding problems, their problems may not be as amenable to change (from parental control strategies) than youth whose onset of behavioral problems are less pervasive and severe.
Parental monitoring and its direct influence on various ASB outcomes. Parental monitoring was found to be a weak predictor of ASB, within the present study, diverging from previous investigations (Dishion and McMahon, 1998). For example, Sandstrom & Coie (1999) found just the opposite after investigating this relationship. These investigators found that parents affect children's interpersonal relationships through monitoring their youth's social activities. Furthermore, they found that poor monitoring was linked to higher rates of delinquency and externalizing behavior.
This inconsistency could be due to the previous studies not taking into consideration the influence of distal community contexts. It was expected that the parenting variable would explain the most variance (as hypothesized in the present study) due to large amount of research that has supported the social interactional framework (e.g. Patterson, 2002). Previous investigations did not have these community predictor variables pulling variance away from parental monitoring when predicting ASB. With these community variables missing from their models, the parenting variable's influence may actually have been inflated. It would appear that when you add more distal contexts, the explanation power of parenting variable decreases. Thus examination of how parental monitoring may act as a third variable (H2 of the present study) between risk factors of ASB and ASB outcomes appears warranted.
Parental monitoring acting as an intervening variable in predicting ASB. Overall, little evidence was found to support parental monitoring acting as an intervening protective variable from the adolescents' development of ASB. The hypothesis that parental monitoring mediates either of the remaining investigated risk factors of ASB was not supported by the results. Regarding moderation effects, when parental monitoring interacted with community violence exposure, it was found to exacerbate the community risk factor when predicting later covert ASB. That is, when you multiply the effects of living in a dangerous neighborhood and having high amounts of parental involvement in the boys' life, these boys demonstrated higher levels of antisocial behavior frequently undetected by adults (Covert ASB), than their peers whose parents' were monitoring them less. No other moderating effects were found within the analyses.
Although parental monitoring was not found to act as a mediating variable between community violence exposure and ASB in the present investigations, other investigations have provided evidence of this relationship. Previous investigators have found, using a five wave longitudinal study, that parenting practices act as a mediator between being exposed to community violence and later violent behavior (Spano, Vazsonyi, & Bolland, 2009). Interestingly, in another investigation (utilizing the same longitudinal data) these same investigators found that community violence exposure acts as a precursor for a decline in parental monitoring (Spano, Rivera, Vazsonyi, & Bolland, 2008).
Divergent findings have been found in previous investigations regarding parental monitoring interacting with risk factors of ASB (as demonstrated within the present study) to attenuate their toxic effects. Previous investigators have suggested that skilled parenting practices were not as protective as once had been thought, especially when adolescents are exposed to extensive violence within their community (Miller, et. al., 1999). In contrast, Mazefsky and Farrell (2005) found that parenting practices moderates the relationship between witnessing violence and aggressive behavior. The data from the present study, however, provided no evidence for a relationship between parental monitoring and adolescent aggressive behavior. After examining different subtypes of ASB and gender patterns, parenting practices appear to attenuate the strength of co-occurring risk factors of ASB, but not in every circumstance.Implications for Reductions in Adolescent ASB
Implications for Prevention. The findings from the present study may have some potential practical applications. For example, community based programs aimed at preventing or intervening in adolescent ASB would benefit from tailoring their efforts to specific adolescent ASB subtypes and their idiographic interpersonal and neighborhood contexts. Programs viewing the development of ASB as multi-determined and through the lens of proximal and distal contexts will likely be more effective (a future outcome study appears warranted to test this assertion). Thus, programs that target certain risk factors (to the neglect of other risk factors) may not be as effective addressing specific outcomes. Additionally, prevention strategies intending to alter or support individual and family adaptations to toxic neighborhood context appears particularly useful.
In an attempt to translate the findings from the present study to real world applications (in the form of preventative measures of adolescent ASB), I will use a well established model of how community contexts influence children to frame this discussion. Leventhal and Gunn (2000) reported that there are three mechanisms through which neighborhoods influence children. Perhaps by creating prevention strategies addressing each mechanism will help reduce these mechanisms' toxic effects. The first mechanism is families' access to institutional resources (e.g. healthcare, education, recreation, child-care, or employment opportunities). Increasing families access to institutional resources appears to be best addressed through political advocacy and becoming an active citizen participating in grass root organizations with missions to build a recreation center or increase access to healthcare within underserved communities. Other ways to address this problem is to help empower those affected by this dearth of services, through local organization of citizens.
The second mechanism suggested by Levanthal and Gunn (2000) was the characteristics of parent-child-relations, parenting skills, and the level of proximal social supports accessible to parents (e.g. other family or friends providing support, like child care or positive healthy relationships). Strategies aimed at decreasing unmonitored interactions with peers and improvement in family functioning appears to away to attenuate the effects of this mechanism (Brown, et. al., 1993; Pettit, et. al., 1999).
A third mechanism suggested by these authors was the effects of collective efficacy (social cohesion and trust amongst persons within the community) on youth outcomes (Leventhal and Gunn, 2000). Interestingly, increases in a communities' collective efficacy has been found to have an inverse relationship with adolescent ASB and moderates the effects of parental monitoring on adolescent ASB (Rankin & Quane, 2002).
Altogether, these findings would suggest the importance of preventive efforts focused on how to encourage neighbors to pull together. By increasing the institutional resources through political organization, provide each other social support, and increase the amount of collective efficacy within their community, they may reduce the toxic effects of these community risk factors for their children. Now that we have considered the implications of the findings for preventative efforts, what are the implications of these findings for mental health professionals treating antisocial youth?
Implications for interventions. The overall message of the findings from this study as it applies to mental health care providers and professionals' attempting to create prevention programs is to think like a community psychologist. For example, when assessing and formulating the adolescents' presenting problems, it is important that professionals not only assess the adolescents' relationships or the family, but the community in which they reside as well. Through the examination of multiple contexts, mental health professionals appear to need to get outside of the consulting room when assessing and intervening. This strategy not only provides more ecological validity with our measures but also provides us with an added layer of empathy we can bring to our therapeutic and diagnostic work.
Another implication would be to improve our contact and relationships with other contexts (e.g. schools, community organizations, local political leaders, etc). Unfortunately, due to our current billing structure, traditions, and our training experiences (with the exception of social workers), these important areas of our clinical work often are neglected. Becoming involved in the community we serve may lead to interventions that would be impossible within the consulting room. In addition, interventions may benefit from therapist's active involvement within a school setting. For example, by helping others understand that boys will likely rebel against increased parental monitoring (possibly other authoritarian control) should be considered when consulting with schools systems wishing to develop their own behavior or education plans with antisocial youth.
When we treat youth, recognizing gender differences in how teens respond to parental monitoring, may prove to be crucial when developing a treatment plan or engaging a youth in the treatment process. Therapists can play active roles in assessing these contexts looking for relevant protective and risk factors. Even something as simple as the therapist inquiring about the teen's perspective of their community may improve therapist efficacy. For example, therapists can ask more about gang activity or what has been going on in their school or neighborhood may provide fruitful data.
When searching for previously established and evidenced based psychotherapeutic intervention that targets multiple contexts in both problem formulation and intervention with adolescent ASB one family based intervention stood out from the rest. Henggeler, Cunningham, Schoenwald, and Borduin (2009) provided an example of such an intervention which is called Multisystemic Therapy (MST). MST is a home-based intervention originally created to address youth ASB. MST is based on social contextual (ecological) framework for case formulation and its theory of behavioral change. Therapists primarily intervene through finding ways to improve family functioning and parental monitoring, to help parents address the influence of deviant peers, and to help parents utilize naturally occurring support systems or help them create and nurture these support systems.
The implications of MST's hypothesized theory of change includes the understanding that adolescent ASB is multi-determined in etiology, families should be empowered to reduce adolescent ASB, the influence of having deviant peers needs to be altered, adolescents achievement needs to be enhanced, and indigenous support systems need to be used to help families promote and sustain treatment gains. MST stresses the importance of learning not just the interpersonal contexts (parenting skills or peer influences) but also the more distal neighborhood contexts' influence, like being exposed to community violence (Henggeler, et. al., 2009).
Evidence for MST's external validity and effectiveness has been well documented. This intervention has been generalized to other problem behaviors as well (e.g. substance use) and as a prevention program within a high risk community (Swenson, Henggeler, Taylor, & Addison, 2005). Additionally, this model has been shown to be effective in multiple outcome studies (e.g. Huey, Henggeler, Brondino, & Pickrel, 2000; Henggeler, et. al., 2009) and clearly resonates with the findings from the present study.Limitations and Future Research
There are several limitations of the present investigation that suggest directions for future research. First, this longitudinal study was only measured at two points in time. The risk factors in a future study could be measured at multiple times to provide evidence of causation between predictors and outcomes. Additionally, by spreading out the number of time points more nuanced developmental factors may be unearthed.
Second, due to the nature of the data collection source (during wave 2 only home interview questions were collected), I was unable to run analyses of the predictor variables from Wave 2. Thus, I was unable to use these predictor variables from Wave 1 as control variables during the analyses of Wave 2 outcomes. These control variables could help rule out the effects of the risk factors (e.g. a teen that no longer has deviant peers or a teen that moved away from a dangerous neighborhood) from Wave 1 while measuring predictor and outcomes relationships within the samples from Wave 2. Future studies may benefit from consistent measurement of all variables across multiple waves.
A third limitation includes the potential underestimate of self-reported ASB. One way to extend the present study's findings would be to measure ASB using institutional data collection (e.g. police reports) of ASB. Another way to increase the external validity of the outcomes measures would be for future investigators to use multiple reporters (including parental or peer report) of the target adolescents' behavior.
A fourth limitation was that the present investigation did not include other relevant contexts (e.g. school) due to the data being unavailable. Future investigations may benefit from examining these variables within the school context to investigate the utility of the social contextual model and gain one more natural setting to increase the interpersonal measures (especially deviant peer affiliation) ecological validity.
A fifth limitation, examining other parenting and family risk factors of ASB may extend the finding that the family context may attenuate the risk factors of ASB in specific situations. Specifically, replicating this study utilizing more parenting skill measures (e.g. discipline), parent-child communication, parental warmth, and overall parent-child relationship measures may broaden the scope of the results of the present study. Previous investigations have found that the quality of parent child communication, autonomy granting, and parental knowledge of children's activities may extend the finding that in some instances high parental monitoring may become intrusive and actually increase teens' sneaky ASB (Keijsers, Frijns, Branje, & Meeus, 2009; Kerr and Stattin, 2000). Perhaps future studies could tease apart these relationships embedded within the multiple contexts of ASB.Conclusions
In conclusion, this study has contributed to the existing literature on ASB in important ways. The results show the relevance of the social contextual model, the toxic effects of being exposed to community violence, and the limitations of parental monitoring as a protective factor, when conceptualizing adolescent ASB. However, there is still much research that needs to be done to fully understand the etiology and development of adolescent ASB. Researchers and clinicians need to continue to work toward prevention and intervention efforts aimed at multiple contexts, like different members of the community supporting one another, to address these problem behaviors.
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