Preprint
Article

A Four-Wave Cross-Lagged Study of Exposure to Violent Contexts, Cognitive Distortions, and School Bullying during Adolescence

Altmetrics

Downloads

134

Views

58

Comments

0

A peer-reviewed article of this preprint also exists.

Submitted:

29 May 2024

Posted:

30 May 2024

You are already at the latest version

Alerts
Abstract
School bullying represents a widespread expression of violence in the peer context. Guided by the social-ecological model, this study investigated the longitudinal and transactional pathways linking domestic and neighborhood/community violence exposure (through direct victimization and witnessing), self-serving cognitive distortions (CDS), and school bullying perpetration. Furthermore, the cognitive desensitization hypothesis that the literature has supposed to develop in response to chronically violent contexts was examined. Two four‐wave cross-lagged panel mediation models were tested in a sample of 778 high school students (28.1% males; Mage [Time 1; T1] = 14.20, SD = 0.58). The results showed that both domestic and neighborhood/community violence exposure, as a victim and as a witness, respectively, were associated to self-serving CDs and bullying perpetration over time. Moreover, significant associations between CDs and bullying perpetration over time were found, with bidirectional effects for each of these longitudinal patterns. Finally, self-serving CDs were found to significantly mediate the relationships between both domestic violence victimization and community violence witnessing and school bullying perpetration. These findings highlight the need to consider school bullying as a social phenomenon stemming from a complex and bidirectional interplay between the individuals and the environments they inhabit confirming a basic postulate that “violence breeds violence”.
Keywords: 
Subject: Social Sciences  -   Psychology

1. Introduction

School bullying is one of the major social problems affecting children and adolescents in all parts of the world [1]. This subtype of aggressive, intentional behavior carried out by a group or an individual repeatedly and over time against a victim who cannot easily defend him or herself [2] has been commonly recognized as a widespread expression of violence in the peer context [3]. A recent report published by the United Nations Educational, Scientific, and Cultural Organization [4] revealed that more than 30% of the world’s students have been victims of bullying, bringing deleterious, immediate, mid- and long-term consequences both on educational and mental health domains [3]. From a developmental perspective, such behaviors peak during middle school years (i.e., 12–15 years), and tends to decrease by the end of high school [5] assuming different forms across age groups [3]. During adolescence school bullying is viewed as a deliberate strategy used to re-establish social status and dominance when youth enter a new peer group [6].
In the framework of Bronfenbrenner’s [7] social-ecological model, school bullying has been investigated in light of several individual and contextual protective and risk factors [8]. Such a model focuses on understanding how individual characteristics interact with environmental contexts or systems to promote or prevent victimization and perpetration [1,8], assuming that bullying victims and perpetrators are part of the complex, interrelated multiple system levels that shape the individual—that is micro-, meso-, exo-, macro-, and chronosystem levels [9].
Taking into account this framework [7], this study builds on a previous study [10], now including in the research design an additional cohort of adolescents, new measures and a longer period of assessment. More specifically, we investigated, over four years, the longitudinal and simultaneous pathways linking youth’s violence exposure across different contexts, specifically within the more proximal and distal family and neighborhood/community microsystems, respectively, and across multiple forms (through direct victimization and witnessing), individual pro-violence moral cognitions (here represented by self-serving cognitive distortions—hereinafter CDs) considered in Gibbs’ model [11], with school bullying perpetration.
In this regard, while there is a strong evidence that both exposure to domestic (e.g., [12,13,14,15]) as well as community violence (e.g., [16,17,18]) and self-serving CDs [19,20,21] are associated with externalizing behaviors (i.e., aggression, conduct problems, delinquency), only a few studies, most of them cross-sectionals, have systematically examined how bullying behavior (as a specific subtype of aggression) is influenced by experiences of domestic (e.g., [22,23,24,25,26]) as well as community violence exposure (e.g., [10,27,28,29,30,31]). Moreover, the associations between violence exposure and moral CDs [10], according to the Gibbs’ model, as well as how such pro-violence moral cognitions are related to the involvement in bullying behavior [32] have been poorly investigated.
Finally, this study sought to test the cognitive desensitization hypothesis that the literature has supposed to develop in response to chronically violent contexts [33,34,35,36], by considering the moral CDs as indicative of cognitive desensitization that would occur after repeated experiences of violence within the family and community. In this regard, most of the evidence comes from investigating the mediating role of acceptance of violence cognitions or biased social-information processing between exposure to violence and aggressive behavior [37,38,39]; however, to date, the examination of the environmental precursors of moral attitudes underlying externalizing behavior in adolescence, as bullying, should be further investigated [10,40].

1.1. Exposure to Violence within the Family and Community as a Social-Environmental Risk Factor for School Bullying Perpetration

According to the ecological model applied to bullying [41,42,43], understanding the factors that promote bullying behaviors requires a close examination of experiences youth daily live in environments they inhabit outside of school, as the more immediate or proximal (i.e., family), and distal (i.e., neighborhood/community) microsystems, with whom youths have direct interactions [1].
During adolescence, violence exposure is one of the experiences that reach a dramatic peak [44,45,46]. This exposure can take place across multiple, sometimes overlapping, contexts or settings (i.e., the concept of “poly-victimization”; [47]) that refer to the multiple ecological levels of the microsystem. Also, it can assume multiple forms, including direct victimization and witnessing without being directly involved.
Some studies suggested a sort of equifinality of violence exposure across different contexts, which seems to eventuate in the same outcomes, whereas other studies suggested a differential effect of violence exposure on developmental outcomes in function of the involved microsystem. This may be due to the differential proximity to the child within the hierarchically ordered social ecology [48]. More specifically, some studies [35,49,50] suggested a stronger impact of violence exposure in the family than in other settings, revealing that domestic violence exposure was a more robust predictor of adjustment problems (e.g., anxiety, depression, aggression, and antisocial conducts) than exposure to community violence. However, other studies [12,16,51] found that community violence has a unique role in predicting several externalizing behaviors accounting for other sources of violence exposure.
Another key issue concerns the differential effects of witnessing or victimization experiences that appear to affect child development differently [52]. Overall, witnessing violence has been shown to be linked to externalizing behaviors (e.g., [53]) mainly through a social learning process that leads to the acquisition of deviant social information patterns, which, in turn, increases the likelihood of aggressive behavior (e.g., [30,51,54,55]). Conversely, direct victimization appeared to be more strongly associated with the development of internalizing symptoms (e.g., [56]) through impairments in emotional self-regulation that compromise the more general ability of individual’s adaptive behavior [30]. Other studies have found no significant differences in the specific outcomes associated with witnessing and victimization experiences [57].
Referring to school bullying, although most of the research highlighted that violence exposure within the family and community places youths at great risk for negative peer experiences such as bullying perpetration, the findings vary considerably across studies.
As regards the family context, a previous review by Nocentini et al. [24] provided empirical evidence about the role of contextual (i.e., parental mental health and domestic violence) and relational family processes (i.e., child abuse and neglect, maladaptive parenting, communication, parental involvement and support) in affecting the risk of bullying others or being victimized. However, although the authors found that domestic violence exposure has a more consistent and stable role as predictor of bullying across studies, to date, the studies specifically focused on the relation between violence exposure within the family and bullying perpetration are still rather scarce.
Based on a review by Voisin and Hong [26], some studies provide empirical evidence that youths who are exposed to domestic violence are more likely to perpetrate school bullying, as well as become victims of bullying (e.g., [22,58,59,60,61,62,63]). Otherwise, other studies (e.g., [23]) have found an association of domestic violence exposure with other types of problematic behaviors (i.e., externalizing behavior or physical aggression and internalizing behaviors), rather than relational bullying behaviors (e.g., social exclusion, spreading rumors). More specifically, Baldry [22] documented that, among a large sample of Italian adolescents and preadolescents, about 1/6 had witnessed domestic violence, and bullies were more likely than not to have witnessed violence in the home, over and above age, gender, and child abuse. Other cross-sectional studies reported similar findings revealing that domestic violence witnessing represents a risk factor for bullying peers [59,60,61,62,63]. Otherwise, in a cross-sectional and retrospective study, Sanders and Jenkins [25] found that domestic violence witnessing significantly predicted the frequency of bullying victimization and presence of relational bullying victimization but not of bullying perpetration.
Conversely, among longitudinal studies, Bowes et al. [58], using a nationally representative community-based sample of children and distinguishing the differential effects of direct victimization (i.e., child maltreatment) and exposure to domestic violence, found that over and above other socio-environmental factors and children’s behavior problems, youths who were victims of parents’ maltreatment were at increased risk for bullying victimization, whereas those who witnessed domestic violence were at increased risk for bullying perpetration.
Finally, a longitudinal study [23] using a community-based sample of children found that witnessing domestic violence was related to several problematic behaviors (i.e., externalizing behavior or physical aggression and internalizing behaviors) but not to child-reported relational bullying behaviors or victimization by peers.
To date, relatively few studies [64,65] have investigated how school bullying is influenced by experiences in environments outside of more immediate or proximal settings (e.g., school and family), and by the perceptions of the neighborhood/community where youths live such as community violence exposure [10]. In this regard, some studies, without distinguishing the differential effects of community violence witnessing and victimization, found a significant association between violence exposure within the community and bullying perpetration [27,31] over and above other socio-environmental factors, such as poverty, inequality, and political violence [28]. A seminal cross-sectional study by Schwartz and Proctor [30] found that children who had been witnesses to or victims of community violence were more likely to bully their classmates.
Furthermore, using a latent transition mixture analysis, Davis et al. [29] reported that the largest proportion (25%) of youths who experienced heightened levels of community violence as witnesses were more likely to be perpetrators of school bullying. Nonetheless, Andershed et al. [66] found that bullying others in school was related to a heightened risk of being violently victimized when out on the streets among both boys and girls. In a longitudinal study [10], it was found that being exposed to community violence as a witness but not as a victim promoted the perpetration of bullying over time.
From a social-ecological systems perspective and consistent with a transactional developmental model of conduct problems [67], both family and community violence exposure and their effects on child development can be understood as resulting from reciprocal interactions between the individual and environment that are continuously influenced by experiences and conditions across multiple interrelated systems over time [68]. In this regard, there is some empirical support for causal, bidirectional influences between violence exposure and externalizing problems such that the more youths are exposed to violent contexts, the more likely they learn pro-violence behavioral models and, vice-versa, the more youths engage in aggressive and delinquent behaviors, the more likely they put themselves in high-risk situations in which they could become witnesses or victims of violence [17,18].
However, the causal nature of the relationships between exposure to violence, both in the family and neighborhood/community, and bullying remains unclear [43], given the limited number of studies that have examined the mechanisms through which violence exposure could affect involvement in bullying behavior.

1.2. Self-Serving Cognitive Distortions as Individual Social-Cognitive Risk Factors for School Bullying Perpetration

Consistent with the social-cognitive approach (e.g., [69,70]), according to which people act upon their interpretation of social events, previous research claimed that moral cognitions represent a key factor for motivating moral or immoral acts such as bullying behaviors [20,71]. In terms of moral cognitive processes, the self-serving CDs defined as “inaccurate or biased ways of attending to or conferring meaning upon experiences” [72] (p. 1) represent one of the common limitations characterizing antisocial youth’s social cognitions [11,73,74].
According to Gibbs and colleagues’ “three Ds” formulation [75], the self-serving CDs are conceived as potentially preceding antisocial action, i.e., the primary CDs, as well as following behavior, i.e., the secondary CDs. More specifically, while the primary distortions (the category Self-centered) reflect more immature moral judgment stages [76] and serve as main motivators or “pretexts” of aggressive behaviors, the secondary distortions (the categories Blaming others, Minimizing/Mislabeling, and Assuming the worst) support the self-centered attitudes [11] and have been characterized as pre- or post-transgression rationalizations or “excuses” for facilitating aggressive behaviors.
Despite an increasing number of studies [19,20,21] found a link between self-serving CDs and externalizing behaviors, only a few studies [10,32] examined the association between CDs and bullying at school. The study by Owens et al. [32] carried out with Australian adolescents found that bullies and bully-victims showed a higher tendency than victims and not-involved persons in assuming the worst, exhibiting minimizing-mislabeling and self-centered CDs, whereas only bullies were higher in blaming others. Similarly, Dragone et al. [10] showed that the development of CDs promoted the perpetration of bullying over time.
However, a transactional developmental model [67] would be better equipped to explain the emergence of chronic antisocial behavior over time. This model suggests the possibility of a multidirectional causality between individual cognitions and behaviors, providing support to Gibbs’ conceptualization of secondary CDs as a form of post-rationalizations or “excuses” serving to emotionally and cognitively overcome dissonance between individual moral standards and behavioral transgressions and neutralize potential feelings of guilt or empathy towards the victim, thus avoid damage to one’s self-image when engaging in antisocial conducts [77,78].
Based on these considerations, disentangling the temporal order is needed to provide key evidence on the causal direction of the link between moral cognitions and aggressive behaviors [79]. However, the experimental or longitudinal designs suited to test assumptions of temporal order and to clarify a possible causal link between aggression and moral cognitions [80] (p. 45) are still rather scarce. Specifically, Aquilar et al. [81] found reciprocal influences over time among values, moral judgment- considered similarly to CDs as a moral motivator [82] of externalizing behaviors-, and antisocial behaviors. Moreover, Ribeaud and Eisner [79] conducted two-period path models to test the relationship between moral neutralization and aggressive behavior in early adolescence, and found that moral neutralization might be envisaged as facilitating aggressive behavior by providing ex ante justifications. In contrast, aggressive behavior would, in turn, induce ex post legitimizations that allow a smooth integration of norm-breaking behavior into an apparently intact moral self-concept.

1.3. Examining how the Cognitive Desensitization Process Could Link Exposure to Violent Contexts, Self-Serving Cognitive Distortions, and School Bullying Perpetration

According to social learning theory [83], children chronically exposed to violence in their daily life environment may learn, via an observational process, that violence itself is a socially acceptable means for conflict resolution [84]. Such process resulting from growing up in violent contexts and facilitating more approving violent beliefs, more positive moral evaluations of aggressive acts, and more justification for inappropriate behavior inconsistent with social and individual’s moral norms has been defined by Huesmann and Kirwil [34] “cognitive desensitization to violence”. Consistently, Ng-Mak and colleagues [36] formulated a “pathologic adaptation” model according to which repeated exposure to high levels of violence in inner-city urban neighborhoods leads to cognitions that normalize violence through mechanisms of neutralization of moral standards which, in turn, facilitate the engagement in future episodes of violence, thus, perpetuating the cycle of violence.
The depiction of moral cognitive processes as mediators of life experiences and as proximal mechanisms for externalizing behaviors is consistent with the biopsychosocial perspective on the development of adolescent conduct problems [67]. According to this perspective, it is assumed that as a function of the aggressogenic life experiences, such as the repeated experience of being a witness or a victim of violence, children develop idiosyncratic social knowledge about their world and social-information processing patterns that justify the appropriateness of behaving aggressively in problematic social situations.
However, to date, relatively few studies have investigated whether the moral cognitive processes could mediate the effect of early life experiences, such as violence exposure within the family and community, on later behavioral problems, such as bullying, also disentangling the potential differential effects due to victimization and witnessing violence (e.g., [30]). Specifically, the transmission of violence among adolescents who directly suffer maltreatment or are indirectly exposed to domestic violence could be cognitively mediated [85]. In this regard, some studies (e.g., [86,87,88]) found that while experiencing witnessing was more likely associated with the schemas of justification of violence which, in turn, lead to aggressive behavior, direct victimization was linked with less aggressiveness and more depression, through the schema of mistrust [87] as well as with later bullying victimization in the school through the development of maladaptive schemas of rejection [89].
Nevertheless, it was found that also direct victimization experiences within the family, such as childhood maltreatment and physical abuse, might contribute to the development of cognitive structures or schemas in the victims, which, in turn, would influence their subsequent behavior. Such schemas assume the form of normative beliefs about the social appropriateness of aggression [90], including the idea that the use of the aggression is justified (e.g., because the other deserves it) and leads to positive outcomes for the individual (e.g., because it serves to obtain respect from others). Furthermore, when exposed to childhood maltreatment and physical abuse, these socio-cognitive biases could also take the form of hostile attributional bias when faced with the ambiguous intentions of others in social situations [91] or of acceptance of violence as normative in adult relationships [92].
Also, with regard to the link between violence exposure within the neighborhood/community and social-cognitive processes, research findings have been mixed. For instance, Wilkinson and Carr [93] using qualitative data from male violent offenders, found that individuals respond to exposure to violence, without distinguishing between violence witnessing and victimization, in many ways, some of which would be consistent with traditional concepts of moral disengagement. In the same direction were the results of Hyde et al. [40], who found a positive association between neighborhood impoverishment and moral disengagement.
Nonetheless, several studies have found significant associations between community violence and acceptance of violent cognitions or bias of social-information processing (see, for example, [37,38]), whereas Bacchini et al. [94] showed that higher levels of exposure to community violence as a witness, along with the perception of higher levels of deviancy among peers, reduced the strength of moral criteria for judging moral violations. Among the very few longitudinal studies, Esposito et al. [95] showed that a high frequency of exposure to community violence was a significant risk factor for being in the class with higher and tendentially stable CDs, relative to the moderate and decreasing class. Similarly, Dragone et al. [10] found a longitudinal relationship between community violence witnessing and the development of CDs.
Other studies [30] have shown distinct mediational pathways linking each form of violence exposure to social difficulties with peers suggesting that the impact of victimization on aggressive behavior took place through impairments in emotion regulation, whereas witnessing influences aggressive behavior through social-cognitive biases about aggression when involved in processing social situations. Consistently, Esposito et al. [96] found that a moderate exposure to community violence, as a witness and as a victim, as well as only as a witness, were linked to increased bullying perpetration through greater moral disengagement. Interestingly, high levels of exposure as both a witness and a victim did not show significant associations with either moral disengagement or bullying perpetration.
Taking this evidence as a starting point, further longitudinal research is needed to clarify whether both experiences of violence exposure, as a victim and/or as a witness, are associated with constructs of moral cognitions such as CDs, which, in turn, could promote the involvement in aggressive behaviors as school bullying perpetration.

1.4. The Current Study

The main aim of this study was to investigate the mediating role of self-serving CDs in the relationship between violence exposure within the family and neighborhood/community, as a victim and as a witness, and school bullying perpetration over time. A four-wave cross-lagged panel mediation design was used, which allowed us to control for baseline values of all variables in each wave and to examine the transactional nature and likely causal direction of the pathways linking exposure to domestic and community violence, CDs, and school bullying perpetration. More specifically, we expected that: (i) being exposed to violence within the family and neighborhood/community increased the likelihood that adolescents would develop self-serving CDs and perpetrate bullying; and (ii) making use of CDs would promote engagement in future episodes of bullying perpetration. In addition, consistent with the transactional developmental model, we expected that the associations between both domestic and community violence exposure with CDs and bullying perpetration, as well as between CDs and bullying perpetration, were reciprocal over time.
As regards the specific hypotheses we made about the differential effects of direct victimization and witnessing, while for domestic violence exposure, no a priori hypotheses were formulated due to the limited and conflicting prior literature, with regard to community violence exposure, we hypothesized significant associations between violence witnessing and both CDs and bullying perpetration, whereas no a priori hypotheses were formulated for violence victimization, due to the limited prior literature.
Given that gender-based differences have been observed in violence exposure and its developmental effects [22,97] as well as in CDs [32] and bullying behavior [98], adolescent gender was included as control variable. Adolescents’ social desirability was also used as a control variable, given its potential confounding effect on all study variables [99].

2. Materials and Methods

2.1. Participants

Participants were drawn from a longitudinal research project (Arzano Longitudinal Project, ALP) aimed at investigating the determinants and pathways of typical and atypical development from early to late adolescence. The study design originally involved sixth and ninth graders of the middle and high schools of Arzano, a relatively small town located in the metropolitan area of Naples. This area is characterized by serious social problems such as high unemployment, school dropout, and the presence of organized crime, with rates that are among the highest in Italy [100].
The sample for the current study consisted of 778 adolescents (346 males and 432 females; Mage [Time 1 (T1)] = 14.20, SD = 0.58)], from two cohorts of adolescents who were enrolled in 9th grade (T1 of the study) in 2013 and 2016 and longitudinally assessed from 2013 to 2016 and from 2016 to 2019 (4 data points, one-year intervals), respectively.
Cohort effects were tested by comparing the mean levels of the main variables at the same age. Specifically, a set of Univariate Analyses of Variance (ANOVAs) found no significant differences between the two cohorts for gender, age, social desirability and all main variables of the study at T1 and T2; conversely, significant differences between the two cohorts were found for domestic violence victimization and self-serving CDs at T3 (Fs(1, 662) = 8.21 and 16.56, ps < 0.01 and 0.001, respectively), as well as at T4 (Fs(1, 623) = 5.33 and 6.88, ps < 0.05 and 0.01, respectively), and for community violence witnessing at T4 (F(1, 623) = 4.30, p < 0.05). However, although statistically significant, Cohen’s d measure of effect size indicated that such cohort differences were very small in size (all ds < |0.33|). Accordingly, the data from the two cohorts were combined.

2.2. Procedure

Data collection was authorized by the managers of the schools involved in the study and took place during the spring of 2013 and 2016 (T1), 2014 and 2017 (T2), 2015 and 2018 (T3), and 2016 and 2019 (T4). During this period, no noteworthy events occurred in either cohort of adolescents.
Parents’ written consent and adolescents’ assent were obtained prior to the administration of questionnaires. All the administrations was conducted during classroom sessions by trained assistants. Participants were informed about the voluntary nature of participation and their right to discontinue at any point without penalty.
The study was approved by the Ethical Committee of Psychological Research of the Department of Humanities Studies, University of Naples “Federico II”.
The American Psychological Association’s ethical standards regarding research with human subjects were followed throughout the research design and implementation.

2.3. Attrition and Missing Data Analysis

The participation rate was approximately 80% across all time points, with 71 (9.1%), 114 (14.7%), and 153 (19.7%) adolescents from the original sample (N = 778) not assessed at T2, T3, and T4, respectively. The total attrition was mainly due to school dropouts. Although the attrition rate seems very high, it is in line with the latest national statistics on school-dropout rates [101]. The Little’s test [102] for data missing completely at random (MCAR) in SPSS 21 (IBM Corp.; Armonk, NY) was significant (χ2(90) = 134.19, p < 0.01), indicating that data were not missing completely at random. Subsequent t-test analyses showed that participants who were missing at T2 and/or T3 and/or T4 significantly reported higher levels of community violence witnessing and CDs at T1, who were missing at T2 and/or at T3 significantly reported also higher levels of bullying perpetration at T1 and who were missing at T3 significantly reported also higher levels of domestic violence witnessing and community violence victimization at T1, than participants who had data at all assessments (ps ≤ 0.05). Accordingly, Full Information Maximum-Likelihood (FIML) was used to handle missing data, enabling us to include all available data in the analyses.

2.4. Measures

2.4.1. Exposure to Domestic Violence

At each time point of the study, a reduced version of the adaption for the Italian context by Baldry [22] of the Conflict Tactics Scale [103] was used. Each scale included six items to which participants were asked to indicate, using a 5-point Likert scale (from 1 = Never to 5 = More than five times), the frequency of their being the witness of violence (i.e., verbal or physical) by each one of their parents against the other parent or the target victim of violence (i.e., verbal or physical) by each of their parents during the last year. Sample items were: “He or she insulted or said bad words to her or him” and “He or she insulted you or said bad words to you”, for domestic violence witnessing and victimization, respectively. Cronbach’s α ranged from 0.83 to 0.89, and from 0.86 to 0.91, for violence witnessing and victimization, respectively.

2.4.2. Exposure to Community Violence

At each time point of the study, we used two adapted scales for the local context (Exposure to community violence Questionnaire; [17]) of the Community Experience Questionnaire by Schwartz and Proctor [30]. Each scale included six items to which participants were asked to indicate, using a 5-point Likert scale (from 1 = Never to 5 = More than five times), the frequency of their being the witness or the victim of violence in the neighborhood. Sample items were: “How many times have you seen somebody get robbed?” and “How many times have you been chased by gangs, other kids, or adults?”, for violence witnessing and victimization, respectively. Cronbach’s α ranged from 0.81 to 0.92, and from 0.79 to 0.92, for violence witnessing and victimization, respectively.

2.4.3. Self-Serving Cognitive Distortions (CDs)

At each time point of the study, participants were asked to respond to the 39 items of the How I think Questionnaire (HIT; [72]; Italian validation by [104]), measuring self-serving CDs. For each item, participants were asked to indicate their agreement on a 6-point Likert scale (from 1 = Disagree strongly to 6 = Agree strongly). Sample items were: “People need to be roughed up once in a while” and “Everybody breaks the law, it’s no big deal”.
A composite score was obtained by averaging item ratings, with a higher score reflecting higher levels of CDs. Cronbach’s α ranged from 0.95 to 0.97 across all time points.

2.4.4. School Bullying Perpetration

At each time point of the study, bullying perpetration was self-reported by adapting the classical Florence Bullying and Victimization Scales (FBVSs; [105]). For the purposes of the present study, we only used data about bullying scale. Adolescents were provided with a definition of bullying as intentional, repetitive aggressive behaviors including some sort of power imbalance between those involved, and were asked to indicate, using a 5-point Likert scale (from 1= Never to 5 = Several times a week), the frequency with which, since the beginning of the school year, they had exhibited eight different bullying behaviors, direct (i.e., physical, e.g., hitting/kicking, “I hit, kicked, or punched someone” and verbal, e.g., threatening, “I threatened someone”) and indirect (e.g., excluding/ignoring, “I made nicknames for others that they didn’t like”).
A composite score was obtained by averaging item ratings, with a higher score reflecting higher involvement in bullying behaviors as a perpetrator. Cronbach’s α ranged from 0.84 to 0.89 across all time points.

2.4.5. Control Variables

Information about the sociodemographic characteristics of the sample were obtained by asking participants to indicate their own age and gender (1 = male, 2 = female). At T1 of the study, participants were also asked to complete 12 items from the Lie scale of the Big Five Questionnaire [106]. Each item was rated on a 5-point Likert scale (from 1 = Very false for me to 5 = Very true for me). Sample items were: “I’ve always gotten along with everyone” and “I’ve never told a lie”.
A composite score was obtained by averaging item ratings, with higher score reflecting higher levels of socially desirable responding. Cronbach’s α was 0.97 at T1.

2.5. Analytic Strategy

Preliminarily, concurrent and longitudinal associations among study variables were performed through Pearson correlations. Subsequently, four-wave cross-lagged panel analyses were used to test the hypothesized longitudinal relations among the study variables (see Figure 1 and Figure 2). Extensive overviews of the use of this model for mediation analyses are given by Cole and Maxwell [107] and MacKinnon [108], as it allows to better investigate the likely direction of causal influence among variables, test for alternative models and lessen biases in testing mediation. The analyses were modeled in Mplus 8 [109] using the Maximum Likelihood Estimation with Robust Estimators (MLR), due to the non-normality of domestic violence witnessing, community violence victimization, and bullying perpetration measures (skewness and kurtosis values ranged from 3.25 to 12.03, 3.32 to 12.21, and 3.13 to 11.77, respectively). Missing data were handled by using Full-Information-Maximum-Likelihood (FIML) estimation of the parameters. As indicated in previous work [110], FIML is an especially useful missing-data treatment in longitudinal designs because the outcome scores for dropouts tend to be correlated with their own previously recorded responses from earlier waves (i.e., a MAR pattern).
Two models were run separately for each specific daily life context: one for exposure to violence within the family and the other for exposure to violence within the neighborhood/community, both as a witness and as a victim. The models included correlations among concurrent constructs at all time points, autoregressive paths for each construct across time, and all cross-lagged paths. Adolescent gender and social desirability were included in the models as observed covariates to ensure that the associations between the variables were adjusted for their potential confounding effects.
Several indexes were used to evaluate the goodness of fit: the Yuan-Bentler [111] scaled chi-square statistic (YBχ2), the Comparative Fit Index (CFI; [112]), the Tucker-Lewis Index (TLI; [113]), and the Root Mean Square Error of Approximation with associated 90% confidence intervals (RMSEA; [114]). A CFI ≥ 0.90 and RMSEA ≤ 0.08 indicate a model’s acceptable fit to the data [115]. To test equivalence of the structural parameters across time, two nested models were considered: a baseline model, in which parameters were freely estimated across time, and a fully constrained model, in which the structural paths and correlations among concurrent constructs were constrained to be equal over time. The Satorra–Bentler chi-square difference test (ΔSBχ2) was used to test relative fit of nested models [116]. When the more constrained model was rejected, a less restrictive model of partial invariance was tested in which, in accordance with modification indices, equality constraints on one or more parameters were relaxed until the change in fit was no longer significant.

3. Results

3.1. Correlations among Study Variables

Means, standard deviations, and Pearson correlations among all study variables are shown in Table 1a-b. For the sake of clarity, we reported correlations separately for each specific daily life context: one for exposure to violence within the family, the other for exposure to violence within the neighborhood/community. Overall, all study variables were significantly and concurrently intercorrelated with each other at all time points with most of them associated with each other also across time.

3.2. Cross-Lagged Panel Modeling

3.2.1. Exposure to Domestic Violence as a Witness and as a Victim

The model with all autoregressive and cross-lagged paths, as well as correlations among concurrent constructs freely estimated across time, showed an adequate fit to the data, YBχ2(48) = 238.74, p < 0.001; CFI = 0.94; RMSEA = 0.07, 90% C.I. [0.06, 0.08].
Imposing equality constraints to autoregressive and cross-lagged paths as well as correlations among concurrent constructs, in order to test their invariance over time, lead to a significantly worse of model fit, ΔSBχ2(74) = 141.46, p < 0.001. In accordance with modification indices, the equality constraints on the autoregressive paths between domestic violence witnessing at T1 and T2 as well as at T3 and T4, on the cross-lagged paths linking T1 social desirability with T1 and T4 CDs, and on the correlations between domestic violence witnessing and victimization at T3 and T4, between domestic violence witnessing and victimization with bullying perpetration at T3 and T1, respectively, between domestic violence victimization and CDs at T4, and, finally, between CDs and bullying perpetration at T1 and T2 were relaxed in order to improve the model fit, YBχ2(111) = 327.10, p < 0.001; CFI = 0.93; RMSEA = 0.05, 90% C.I. [0.04, 0.06]. Thus, the partially constrained model did not differ significantly from the freely estimated model, ΔSBχ2(63) = 76.64, p = 0.12. Significant paths and standardized coefficients for the final model are shown in Figure 1.
As can be noted in Figure 1, all variables were correlated with each other within all time points. As regards the stability of study variables across time, all measures showed moderate to high stability over time with the highest levels of stability emerged for CDs and domestic violence victimization. Regarding cross-lagged paths, significant associations between domestic violence victimization and both CDs and bullying perpetration at each time point were found; conversely, bullying perpetration significantly predicted domestic violence witnessing, but not vice-versa. Moreover, CDs significantly predicted bullying perpetration at each time point. Bidirectional relations between domestic violence victimization and both CDs and bullying perpetration, as well as between CDs and bullying perpetration over time were also found.
Finally, as can be seen in Table 2, the mediation analyses highlighted marginally significant indirect effects from T1 and T2 domestic violence victimization to T3 and T4 bullying perpetration through T2 and T3 CDs, respectively (β = 0.01, p < 0.05, 95% C.I.s [0.002, 0.017] and [0.002, 0.018], respectively). Moreover, the mediation analyses confirmed a series of reciprocal associations between CDs and bullying perpetration over time, such that earlier high CDs at T1 and T2 increased CDs at T3 and T4 through the mediation of bullying perpetration at T2 and T3, respectively (β = 0.02, p < 0.001, 95% C.I. [0.01, 0.03], respectively) as well as an earlier involvement in bullying at T1 and T2 increased the tendency to bully others at T3 and T4 through the mediation of CDs at T2 and T3, respectively (β = 0.02, p < 0.001, 95% C.I. [0.01, 0.03], respectively).

3.2.2. Exposure to Neighborhood/Community Violence as a Witness and as a Victim

The model with all autoregressive and cross-lagged paths, as well as correlations among concurrent constructs freely estimated across time, showed an adequate fit to the data, YBχ2(48) = 143.33, p < 0.001; CFI = 0.96; RMSEA = 0.05, 90% C.I. [0.04, 0.06].
Imposing equality constraints to autoregressive and cross-lagged paths as well as correlations among concurrent constructs, in order to test their invariance over time, lead to a significantly worse of the model fit, ΔSBχ2(74) = 182.02, p < 0.001. In accordance with modification indices, the equality constraints on the autoregressive paths between community violence witnessing at T1 and T2 as well as at T3 and T4, on the cross-lagged paths linking T1 social desirability with T1 and T4 CDs as well as gender with T4 community violence witnessing, and on the correlations between community violence witnessing and victimization at each time point, between community violence witnessing and CDs at T1 and T3 as well as with bullying perpetration at T1, and between CDs and bullying perpetration at T2 were relaxed in order to improve the model fit, YBχ2(110) = 225.91, p < 0.001; CFI = 0.95; RMSEA = 0.04, 90% C.I. [0.03, 0.04]. Thus, the partially constrained model did not differ significantly from the freely estimated model, ΔSBχ2(62) = 79.10, p = 0.07. Significant paths and standardized coefficients for the final model are shown in Figure 2.
As can be noted in Figure 2, all variables were correlated with each other at all time points. As regards the stability of study variables across time, all measures showed moderate to high stability over time with the highest levels of stability emerged for CDs and community violence witnessing. Regarding cross-lagged paths, significant associations between community violence witnessing and both CDs and bullying perpetration at each time point were found and CDs significantly predicted bullying perpetration at each time point. Moreover, a series of bidirectional relations between community violence witnessing and both CDs and bullying perpetration, as well as between CDs and bullying perpetration over time were also found.
Finally, as can see in Table 2, the mediation analyses highlighted marginally significant indirect effects from T1 and T2 community violence witnessing to T3 and T4 bullying perpetration through T2 and T3 CDs, respectively (β = 0.01, p < 0.05, 95% C.I.s [0.001, 0.018] and [0.001, 0.020], respectively). Moreover, the mediation analyses confirmed a series of reciprocal associations between CDs and bullying perpetration over time, such that earlier high CDs at T1 and T2 increased CDs at T3 and T4 through the mediation of bullying perpetration at T2 and T3, respectively (β = 0.02, p < 0.001, 95% C.I.s [0.01, 0.03] and [0.01, 0.02], respectively) as well as an earlier involvement in bullying at T1 and T2 increased the tendency to bully others at T3 and T4 through the mediation of CDs at T2 and T3, respectively (β = 0.02, p < 0.001, 95% C.I. [0.01, 0.03], respectively).

3.2.3. Control Variables

With respect to covariates, adolescent gender was negatively related to both community violence witnessing and victimization at T1 (βs = -0.07 and -0.05, ps < 0.001 and < 0.05, respectively), T2 (βs = -0.07 and -0.05, ps < 0.001 and < 0.05, respectively), and T3 (βs = -0.06 and -0.04, ps < 0.001 and < 0.05, respectively), and with community violence victimization also at T4 (β = -0.04, p < 0.05), as well as to CDs and bullying perpetration at each time point, at T1 (βs = -0.10 and -0.17, p < 0.001, respectively), T2 (βs = -0.10 and -0.16, p < 0.001, respectively), T3 (βs = -0.09 and -0.16, p < 0.001, respectively), and T4 (βs = -0.09 and -0.16, p < 0.001, respectively), with males scoring higher than females.
As regards the social desirability bias, negative associations were found with violence victimization both in the family and in the neighborhood/community at each time point, at T1 (βs = -0.08 and -0.06, ps < 0.001 and < 0.05, respectively), T2 (βs = -0.07 and -0.06, ps < 0.001 and < 0.05, respectively), T3 (βs = -0.08 and -0.05, ps < 0.001 and < 0.05, respectively), and T4 (βs = -0.07 and -0.04, ps < 0.001 and < 0.05, respectively), as well as with CDs at T1 (β = -0.20, p < 0.001), and bullying perpetration at each time point, (all βs = -0.07, ps < 0.001), with youths more careful about their social image scoring lower on such constructs.

4. Discussion

Guided by the social-ecological model [7], we used a four-wave cross-lagged panel design to investigate the longitudinal and simultaneous pathways linking violence exposure within the family and neighborhood/community, through witnessing and direct victimization, self-serving CDs, with school bullying perpetration during adolescence. Furthermore, we tested the cognitive desensitization hypothesis that the literature has supposed to develop in response to chronically violent contexts [33,34,35,36], by considering the moral CDs as indicative of cognitive desensitization that would occur after repeated experiences of domestic and community violence exposure. Since the transactional developmental model is best equipped to describe the emergence of chronic antisocial behavior across time [67], we also examined the reciprocal associations between both domestic and community violence exposure with CDs and bullying perpetration, as well as between CDs and bullying perpetration over time. All the effects were examined controlling for adolescent gender and social desirability bias.
Taking into account the co-occurrence of multiple contexts and experiences through which youths are exposed to violence [47], a specific contribution of our study concerned the detection of differential effects of multiple contexts (i.e., the family and the neighborhood/community) and multiple forms (i.e., through witnessing and direct victimization) of violence exposure to bullying behaviors in adolescence.
Our findings showed that both exposure to violence within the family and community were associated to self-serving CDs and bullying perpetration over time although through different forms; more specifically, for domestic violence exposure through direct victimization (i.e., child maltreatment or abuse), whereas for community violence exposure through witnessing. Moreover, we found a significant association between self-serving CDs and bullying perpetration over time. Each of these longitudinal patterns was found to have a bidirectional direction.
Finally, as regards the cognitive desensitization hypothesis we tested, a significant mediating role of self-serving CDs in the relationship between domestic violence victimization and community violence witnessing, respectively, with school bullying perpetration was found.

4.1. Social-Environmental Risk Factors for School Bullying Perpetration: The Role of Domestic and Community Violence Exposure

Consistent with the concept of equifinality, we found that both domestic and community violence exposure were associated with bullying perpetration over time, confirming a basic postulate that “violence breeds violence” [48]. However, we found differential effects of multiple forms (i.e., through witnessing and direct victimization) of violence exposure to bullying perpetration in adolescence. More specifically, as regards domestic violence exposure, our findings that direct victimization rather than witnessing was associated with bullying perpetration are consistent with a previous cross-sectional study by Bacchini et al. [12] highlighting that being a victim of domestic violence had a stronger concurrent association with antisocial behavior than witnessing violence at home and, partially, in accordance with another study by Holt et al. [61] who found higher rates of child maltreatment for both bullies and victims.
Similarly to our findings, the review by Nocentini et al. [24] found that experiencing child abuse and maltreatment by parents was the most consistent family risk variables to be associated with bullying perpetration.
Conversely, other cross-sectional [22,59,60,62,63] and longitudinal [58] studies emphasized a stronger impact of domestic violence witnessing in promoting bullying perpetration whereas Bauer et al. [23], in their longitudinal study, found that, although witnessing domestic violence was related to increased problematic behaviors (i.e., externalizing behavior or physical aggression and internalizing behaviors), it was associated neither with child-reported bullying behaviors nor with victimization by peers.
The lack of effects of witnessing domestic violence on bullying perpetration could be explained by referring to the changes that occur in family influence during adolescence characterized by a significant increase of other socialization agents, such as peers [117] and by more time spent in environments outside the home, such as the neighborhood/community. For this reason, as Mrug and Windle [50] argued, the higher probability of being witnesses of community violence, especially when young people grow, may desensitize them to the effects of violence they witness at home, putting them at lower risk of development externalizing behaviors (e.g., delinquency; [56]) such as bullying.
Also, it should be noted that the model we tested considered the concurrent effects of both witnessing and victimization in the domestic setting. The observed correlations suggested that there could be an overlap between experiences of victimization and witnessing domestic violence. When both variables are considered simultaneously, only victimization shows a significant effect on bullying perpetration. This means that the effect of victimization on bullying behavior is independent of the experience of witnessing violence. On the other hand, the effect of witnessing violence does not hold independently of victimization. In other words, if an individual is a victim of domestic violence, he or she is likely to engage in bullying behavior regardless of whether he or she also witnessed domestic violence. Conversely, merely witnessing domestic violence, without experiencing direct victimization, does not lead to increased bullying perpetration.
However, it is important to note that witnessing domestic violence, such as frequent parental conflicts, may often coincide with being victimized at home [47]. However, a child can be victimized, for example, through harsh parental discipline, without necessarily witnessing domestic violence. Further research could explore the different profiles of exposure to domestic violence and the potential co-occurrence of multiple forms, as well as their effects on bullying perpetration.
Moving on to the effects of community violence exposure, consistent with our expectations and with previous literature (e.g., [43,64,118,119]) showing that youths who live in neighborhoods judged to be less safe (i.e., characterized by more violent behaviors, where access to guns and gang membership may be most likely) were more likely to engage in externalizing behaviors, we found that experiencing community violence witnessing predicted bullying perpetration over time. This finding is also in line with previous studies specifically focused on bullying behaviors [10,29,30] and, as Bowes et al. [58] argued, it could be interpreted by referring to the thesis according to which hostile interactions observed in local communities may provide youths with models of aggressive behaviors that they can reproduce in the context of peers relationships.
Conversely, although the correlations suggested a co-occurrence of violence witnessing and victimization within the community, the lack of victimization effects on bullying perpetration we found may be explained by taking into account those studies which highlighted that being a victim of violence within the community could be linked with other variables that we did not include in the current study, such as impairments in emotional self-regulation [30] and internalizing [56] rather than externalizing symptoms. A recent study by Esposito et al. [96] analyzing profiles of community violence exposure found that community violence victimization only occurred together with witnessing at average and high levels. When examining the association with bullying perpetration, only average exposure, and not also high exposure, as both victim and witness, had a significant direct effect on bullying perpetration, compared to the profile with low exposure, thus providing further support to the hypothesis that the impact of exposure to community violence on youth’s developmental outcomes can vary when considering the chronicity of violence exposure.
In addition, perpetrating bullying significantly predicted both domestic and community violence exposure over time, confirming the key assumption of the transactional approach to the development of conduct problems in adolescence [67], according to which it is plausible that a vicious circle takes place between violent contexts and youths’ behavior, so that youths who engage in aggressive behavior are more likely to put themselves in high-risk situations in which they are more likely to be witnesses of violence in the community [17,18] as well as to be victims of violence from their parents; the latter could recourse to aggressive behavior to discipline their children who bully peers, thus perpetuating the cycle of violence [25]. Indeed, as suggested by Sanders and Jenkins [25], the beliefs and behaviors modeled by parents through experiences of domestic violence are then internalized and accepted by their children, who then mimic these actions in their interactions with peers outside the home.

4.2. Pathways Linking Exposure to Domestic and Community Violence, Self-Serving Cognitive Distortions, and Bullying Perpetration: The Cognitive Desensitization Process

In line with social learning theory [83] and its crime-related extension [67], our findings seem to corroborate the cognitive desensitization hypothesis [34] revealing that experiencing domestic violence victimization and community violence witnessing were associated to the individual tendency to make self-serving CDs which, in turn, promoted bullying perpetration over time.
Regarding domestic violence victimization, our findings suggested that intergenerational transmission of violence in adolescents who directly suffer maltreatment by their parents could be cognitively mediated [85] making youth more likely to perceive threats and attribute hostile intent to others rather than benign interpretations especially when faced with the ambiguous intentions of others in social situations [91]. Therefore, when chronically exposed to parental victimization, youths may develop cognitive schemas which could reflect a tendency towards hypervigilance to perceived threatening cues and a hostile attributional bias that emerged as an adaptive response to actual threats in the past [67,91] as well as could adopt normative beliefs about the social appropriateness of aggression [90].
However, contrary to our results, some empirical evidence (e.g., [86,87]) suggested a more relevant role of domestic violence witnessing to promote the development of justification schemas about violence, whereas direct victimization was linked to the schema of mistrust [87] or rejection as a result of family abuse and victimization [89].
Conversely, when examining the link between violence exposure within the neighborhood/community and social-cognitive processes, our results revealed an association between violence exposure as a witness, but not as a victim, and the individual tendency to make self-serving CDs over time, thus corroborating theoretical assumptions of social learning theory [83] and biopsychosocial perspective [67] according to which youths who are exposed to violence within their living environments learn and internalize via observational learning, a series of criminal/deviant models which take the form of social-cognitive schemas, beliefs and positive attitudes towards violence [33,35].
This findings are consistent with a cognitive desensitization to violence process [34] and in line with Anderson’s [120] “Code of the Street” perspective, which suggested that living in neighborhoods where macrostructural patterns of disadvantage are radicalized facilitates the access to street’s subculture, that shapes pro-violence values and beliefs such as the CDs to legitimatize the use of violence conceived as an acceptable problem-solving tool in neighborhoods where the street culture is widespread. Furthermore, our findings are consistent with the few studies [10,95] framed within the Gibbs and colleagues’ [73] theoretical formulation of CDs but expand them by exploring the environmental precursors of moral cognitions considering both the more proximal-, i.e., the family, and distal-, i.e., the community, microsystems of social ecology.
The lack of associations between community violence victimization and self-serving CDs over time is consistent with the findings by Schwartz and Proctor [30], which showed that only violence witnessing was associated with social-cognitive biases supporting positive evaluation of violent behavior, whereas experiencing violence victimization was more likely be associated with impairments in emotion regulation.
Taken together, the findings discussed above provide further support for a “pathologic adaptation” model [36], according to which chronic violence exposure leads to a normalization of violence through the neutralization of moral standards. Therefore, it allows us to speculate that growing up in violent contexts, both in the family and neighborhood/community, might undermine the normative process of moral development, thus, causing a moral delay, which consolidates into self-serving CDs [121].
Moreover, our findings highlighted also bidirectional relations between exposure to violence, as a victim and as a witness, within the family and community, respectively, and the development of self-serving CDs, such that the more youths are exposed to violence within the family and community, the more she or he develops biased cognitive processes to justify her or his immoral actions which, in turn, put themselves in high-risk situations in which they are more likely to be victims of parental violence or witnesses of community violence.
These results, along with those above-mentioned about the reciprocal effects of domestic and community violence exposure with bullying perpetration over time, seem to provide support for the lifestyle exposure theory [122], indicating that exposure to violence is not random but is closely related to the behavior (or lifestyle and attitudes) of individuals and with the transactional approach to the development of conduct problems in adolescence [67] according to which it is plausible to hypothesize that a vicious circle takes place between adolescents’ attitudes and behaviors and the risk to being victims and/or witnesses of violence in their daily life environments.
Finally, consistent with our expectations and with the social-cognitive approach (e.g., [69,70]) that links behavior to the way one thinks about situations, we found that the tendency to make self-serving CDs when interpreting social situations, promoted engagement in bullying perpetration over time, and vice-versa. In line with previous studies (e.g., [123]) developed within the theoretical framework of moral disengagement [69,70], our findings suggested that youths need to construct attitudes and beliefs that justify their immoral actions in order to maintain a positive self-concept. As expected, we also found a predictive role of bullying perpetration on CDs and the recursive association between cognition and behavior over time, such that cognitions affect behavior, and behavior feeds back into cognitions. This result is framed within the transactional developmental model [67] and suggests the possibility of a multidirectional causality between individual cognitions and behaviors, providing support to Gibbs’ conceptualization of secondary CDs as a form of post-rationalization or “excuses” serving to emotionally and cognitively overcome dissonance between individual moral standards and behavioral transgressions. Similarly, Aquilar et al. [81] found a reciprocal influence over time among values, moral judgment, considered similar to CDs as a moral motivator [82] of externalizing behaviors, and antisocial behaviors. Furthermore, our findings are consistent with those of Ribeaud and Eisner [79] who suggested that moral neutralization and aggression could be conceived as the cognitive and as the behavioral, respectively, expression of the same phenomenon. Specifically, in the process of (aggressive) decision making, moral neutralization might be envisaged as facilitating aggressive behavior by providing ex ante justifications, whereas aggressive behavior would in turn induce ex post legitimizations that allow a smooth integration of norm-breaking behavior into an apparently intact moral self-concept.
In addition, the mediation analyses confirmed a series of reciprocal associations between CDs and bullying perpetration over time, such that the later increase in bullying perpetration was mediated by earlier higher CDs, and vice-versa, the later increase in making use of CDs was mediated by earlier higher involvement in bullying perpetration, such that the more a youth makes use of CDs, the more she or he is inclined to perpetrate bullying and, vice-versa, the more a youth is involved in bullying perpetration, the more she or he is more likely to increase beliefs in the normative nature of violence [54].
Taken together, our findings, in line with a transactional developmental model [67], corroborate the view of a multidirectional causality between life experiences and individual cognitions and behaviors such that these constructs reinforce each other over time in the process of (aggressive) decision making [79].

4.3. Limitations and Future Directions

In interpreting these findings, several limitations need to be acknowledged. First, the evaluation of all constructs in the study relied exclusively on adolescent self-reporting, which may be subject to social desirability. Despite in the current study all the effects were controlled for social desirability bias, it is known (e.g., [99]) that adolescents are more careful about their social image than other age groups, and may be unlikely to report behavior that displays them in a negative light. Furthermore, more objective and comprehensive measures of violence in the everyday lives of adolescents, including official data from national census agencies and police departments, may provide a more complete assessment of violence exposure [124]. Future studies may benefit from utilizing a multi-informant approach (e.g., peers’ and teachers’ reports for behavioral constructs) jointly with self-report measures.
Another limitation concerns the generalizability of the results, as the study included a sample from a limited geographical area in Southern Italy characterized by serious social problems and high rates of violence exposure. Such experiences might shape culture-specific beliefs and values that, in turn, might influence an individual’s cognitions and behaviors [12]. For this reason, more research is needed to confirm that the explanatory model proposed in this study applies to populations from other, possibly differing, cultural contexts.
Furthermore, given the co-occurrence of different kinds of violence exposure from multiple contexts [47], future research could also investigate the cumulative and interactively effects of domestic and community violence exposure in exacerbating adjustment problems (e.g., [35]). In addition, the lack of witnessing and victimization effects of domestic and community violence, respectively, on self-serving CDs and bullying perpetration could suggest the need to consider other relevant variables, such as impairments in emotional self-regulation (e.g., [30]) and internalizing problems (e.g., [56]). Also, some other school or classroom-level variables (e.g., peers/teachers support, peer pressure, school climate, etc.) could be relevant to improve our understanding of the dynamics involved in bullying episodes considering that bullying is a complex social peer-group process [125].
Finally, because self-serving CDs might be perceived as relatively stable cognitive mechanisms [73] which could differ among individuals over time, further analytical strategies could be implemented in future studies, such as random intercept cross-lagged panel models which allow to partial out the between-person stability over time, such that the lagged coefficients represent within-person patterns of change [126].

5. Conclusions and Practical Implications

Notwithstanding these limitations, this study provides further corroboration, consistent with Bronfenbrenner’s [7] social-ecological framework, of the joint and reciprocal role of individual and contextual factors implicated in the enactment of school bullying behaviors. More specifically, the findings of the present study highlighted that youths who experience violence in their daily life contexts develop pro-aggressive moral cognitions taking the form of self-serving CDs, which, in turn, amplify the risk for involvement in bullying perpetration.
Overall, these results point out the need to consider the development of conduct problems (e.g., bullying) in adolescence as a process involving multiple levels of individual ecology and provide useful suggestions for designing and implementing appropriate interventions aimed at preventing and reducing adolescents’ involvement in bullying perpetration by reducing their biased moral cognitions, especially when exposed to violent environments.
An example of a school-based program developed within Gibbs’ theoretical framework and aimed at reducing adolescents’ “thinking errors” or CDs is the “Equipping Youth to Help One Another (EQUIP) for Educators” (EfE; [127]) whose effectiveness has been recently demonstrated in the Italian context [128]. In accordance with our findings highlighting the key role of CDs in promoting bullying perpetration in youths exposed to violence, Dragone and colleagues [128] provided empirical support for the effectiveness of such psychoeducational program in promoting a more responsible way of (inter)acting with peers in high-risk school contexts by equipping youths with skills for correcting their “thinking errors” or self-serving CDs when interpreting social events.

Author Contributions

Conceptualization, M.D., D.B., and C.E.; methodology, M.D. and D.B.; formal analysis, M.D. and C.E.; investigation, M.D., C.E., and G.D.A.; data curation, M.D., D.B., and C.E.; writing—original draft preparation, M.D. and D.B.; writing—review and editing, M.D., D.B., C.E., F.S. and R.D.L.P.; visualization, G.D.A.; supervision, D.B. and G.A.; project administration, D.B. and G.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethical Committee of Psychological Research of the Department of Humanities Studies of the University of Naples “Federico II” (protocol code n. 3/2020 dated 13 January 2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. No incentive was given.

Data Availability Statement

Data are available from the corresponding author on reasonable request.

Acknowledgments

The authors are grateful to the students who participated in this research as well as to the principals and teachers of the participating schools for their contribution in data collection.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Hong, J.S.; Espelage, D.L. A Review of Research on Bullying and Peer Victimization in School: An Ecological System Analysis. Aggress. Violent Behav. 2012, 17, 311–322. [Google Scholar] [CrossRef]
  2. Olweus, D. Bullying in School: What We Know and What We Can Do; Blackwell: Oxford, UK, 1993. [Google Scholar]
  3. Menesini, E.; Salmivalli, C. Bullying in Schools: The State of Knowledge and Effective Interventions. Psychol. Health Med. 2017, 22, 240–253. [Google Scholar] [CrossRef] [PubMed]
  4. 2020.
  5. Hymel, S.; Swearer, S.M. Four Decades of Research on School Bullying: An Introduction. Am. Psychol. 2015, 70, 293–299. [Google Scholar] [CrossRef]
  6. Pellegrini, A.D.; Long, J.D. A Longitudinal Study of Bullying, Dominance, and Victimization during the Transition from Primary School through Secondary School. Br. J. Dev. Psychol. 2002, 20, 259–280. [Google Scholar] [CrossRef]
  7. Bronfenbrenner, U. The Ecology of Human Development: Experiments by Nature and Design; Harvard University Press: Cambridge, MA, USA, 1979. [Google Scholar]
  8. Espelage, D.L. Ecological Theory: Preventing Youth Bullying, Aggression, and Victimization. Theory Pract. 2014. [Google Scholar] [CrossRef]
  9. Bronfenbrenner, U. Ecological Models of Human Development. In The International Encyclopedia of Education. Oxford: Elsevier 1994, 2nd, 1643–1647.
  10. Dragone, M.; Esposito, C.; De Angelis, G.; Affuso, G.; Bacchini, D. Pathways Linking Exposure to Community Violence, Self-Serving Cognitive Distortions and School Bullying Perpetration: A Three-Wave Study. Int. J. Environ. Res. Public Health 2020, 17, 188. [Google Scholar] [CrossRef]
  11. Gibbs, J.C. Moral Development and Reality: Beyond the Theories of Kohlberg, Hoffman, and Haidt, 3rd ed.; Oxford University Press: New York, NY, 2013. [Google Scholar]
  12. Bacchini, D.; Affuso, G.; Aquilar, S. Multiple Forms and Settings of Exposure to Violence and Values. J. Interpers. Violence 2015, 30, 3065–3088. [Google Scholar] [CrossRef]
  13. Cao, Y.; Li, L.; Zhao, X.; Zhang, Y.; Guo, X.; Zhang, Y.; Luo, X. Effects of Exposure to Domestic Physical Violence on Children’s Behavior: A Chinese Community-Based Sample. J. Child Adolesc. Trauma 2016, 9, 127–135. [Google Scholar] [CrossRef] [PubMed]
  14. Ehrensaft, M.K.; Cohen, P.; Brown, J.; Smailes, E.; Chen, H.; Johnson, J.G. Intergenerational Transmission of Partner Violence: A 20-Year Prospective Study. J. Consult. Clin. Psychol. 2003, 71, 741–753. [Google Scholar] [CrossRef]
  15. Evans, S.E.; Davies, C.; DiLillo, D. Exposure to Domestic Violence: A Meta-Analysis of Child and Adolescent Outcomes. Aggress. Violent Behav. 2008, 13, 131–140. [Google Scholar] [CrossRef]
  16. Chen, P.; Voisin, D.R.; Jacobson, K.C. Community Violence Exposure and Adolescent Delinquency. Youth Soc. 2016, 48, 33–57. [Google Scholar] [CrossRef]
  17. Esposito, C.; Bacchini, D.; Eisenberg, N.; Affuso, G. Effortful Control, Exposure to Community Violence, and Aggressive Behavior: Exploring Cross-lagged Relations in Adolescence. Aggress. Behav. 2017, 43, 588–600. [Google Scholar] [CrossRef] [PubMed]
  18. Mrug, S.; Windle, M. Mediators of Neighborhood Influences on Externalizing Behavior in Preadolescent Children. J. Abnorm. Child Psychol. 2009, 37, 265–280. [Google Scholar] [CrossRef] [PubMed]
  19. Barriga, A.Q.; Hawkins, M.A.; Camelia, C.R.T. Specificity of Cognitive Distortions to Antisocial Behaviours. Crim. Behav. Ment. Heal. 2008, 18, 104–116. [Google Scholar] [CrossRef] [PubMed]
  20. Gini, G. , Camodeca, M., Caravita, S. C. S., Onishi, A., & Yoshizawa, H. Cognitive Distortions and Antisocial Behaviour: An European Perspective. Konan Daigaku Kiyo. Bungaku-Hen (Journal Konan Univ. 2011, 161, 209–222. [Google Scholar] [CrossRef]
  21. Helmond, P.; Overbeek, G.; Brugman, D.; Gibbs, J.C. A Meta-Analysis on Cognitive Distortions and Externalizing Problem Behavior. Crim. Justice Behav. 2015, 42, 245–262. [Google Scholar] [CrossRef]
  22. Baldry, A.C. Bullying in Schools and Exposure to Domestic Violence. Child Abuse Negl. 2003, 27, 713–732. [Google Scholar] [CrossRef] [PubMed]
  23. Bauer, N.S.; Herrenkohl, T.I.; Lozano, P.; Rivara, F.P.; Hill, K.G.; Hawkins, J.D. Childhood Bullying Involvement and Exposure to Intimate Partner Violence. Pediatrics 2006, 118, e235–e242. [Google Scholar] [CrossRef] [PubMed]
  24. Nocentini, A.; Fiorentini, G.; Di Paola, L.; Menesini, E. Parents, Family Characteristics and Bullying Behavior: A Systematic Review. Aggress. Violent Behav. 2019, 45, 41–50. [Google Scholar] [CrossRef]
  25. Sanders, C.; Jenkins, S.R. Exploring Bullying, Partner Violence, and Social Information Processing. Int. J. Bullying Prev. 2024, 1–10. [Google Scholar] [CrossRef]
  26. Voisin, D.R.; Hong, J.S. A Meditational Model Linking Witnessing Intimate Partner Violence and Bullying Behaviors and Victimization Among Youth. Educ. Psychol. Rev. 2012, 24, 479–498. [Google Scholar] [CrossRef]
  27. Bacchini, D.; Esposito, G.; Affuso, G. Social Experience and School Bullying. J. Community Appl. Soc. Psychol. 2009, 19, 17–32. [Google Scholar] [CrossRef]
  28. Chaux, E.; Molano, A.; Podlesky, P. Socio-economic, Socio-political and Socio-emotional Variables Explaining School Bullying: A Country-wide Multilevel Analysis. Aggress. Behav. 2009, 35, 520–529. [Google Scholar] [CrossRef] [PubMed]
  29. Davis, J.P.; Ingram, K.M.; Merrin, G.J.; Espelage, D.L. Exposure to Parental and Community Violence and the Relationship to Bullying Perpetration and Victimization among Early Adolescents: A Parallel Process Growth Mixture Latent Transition Analysis. Scand. J. Psychol. 2020, 61, 77–89. [Google Scholar] [CrossRef]
  30. Schwartz, D.; Proctor, L.J. Community Violence Exposure and Children’s Social Adjustment in the School Peer Group: The Mediating Roles of Emotion Regulation and Social Cognition. J. Consult. Clin. Psychol. 2000, 68, 670–683. [Google Scholar] [CrossRef] [PubMed]
  31. Valdés Cuervo, A.A.; Tánori Quintana, J.; Carlos Martínez, E.A.; Wendlandt Amezaga, T.R. Challenging Behavior, Parental Conflict and Community Violence in Students with Aggressive Behavior. Int. J. Psychol. Res. 2018, 11, 50–57. [Google Scholar] [CrossRef] [PubMed]
  32. Owens, L.; Skrzypiec, G.; Wadham, B. Thinking Patterns, Victimisation and Bullying among Adolescents in a South Australian Metropolitan Secondary School. Int. J. Adolesc. Youth 2014, 19, 190–202. [Google Scholar] [CrossRef]
  33. Dodge, K.A.; Bates, J.E.; Pettit, G.S. Mechanisms in the Cycle of Violence. Science 1990, 250, 1678–1683. [Google Scholar] [CrossRef] [PubMed]
  34. Huesmann, L.R.; Kirwil, L. Why Observing Violence Increases the Risk of Violent Behavior in the Observer. In The Cambridge Handbook of Violent Behavior and Aggression; Flannery, D.J., Vazsonyi, A.T., Waldman, I.D., Eds.; Cambridge University Press: Cambridge, UK; pp. 545–570.
  35. Mrug, S.; Loosier, P.S.; Windle, M. Violence Exposure across Multiple Contexts: Individual and Joint Effects on Adjustment. Am. J. Orthopsychiatry 2008, 78, 70–84. [Google Scholar] [CrossRef]
  36. Ng-Mak, D.S.; Salzinger, S.; Feldman, R.; Stueve, A. Normalization of Violence among Inner-City Youth: A Formulation for Research. Am. J. Orthopsychiatry 2002, 72, 92–101. [Google Scholar] [CrossRef]
  37. Allwood, M.A.; Bell, D.J. A Preliminary Examination of Emotional and Cognitive Mediators in the Relations between Violence Exposure and Violent Behaviors in Youth. J. Community Psychol. 2008, 36, 989–1007. [Google Scholar] [CrossRef]
  38. Bradshaw, C.P.; Rodgers, C.R.R.; Ghandour, L.A.; Garbarino, J. Social–Cognitive Mediators of the Association between Community Violence Exposure and Aggressive Behavior. Sch. Psychol. Q. 2009, 24, 199–210. [Google Scholar] [CrossRef]
  39. Calvete, E.; Orue, I. The Impact of Violence Exposure on Aggressive Behavior through Social Information Processing in Adolescents. Am. J. Orthopsychiatry 2011, 81, 38–50. [Google Scholar] [CrossRef] [PubMed]
  40. Hyde, L.W.; Shaw, D.S.; Moilanen, K.L. Developmental Precursors of Moral Disengagement and the Role of Moral Disengagement in the Development of Antisocial Behavior. J. Abnorm. Child Psychol. 2010, 38, 197–209. [Google Scholar] [CrossRef] [PubMed]
  41. Swearer, S.M.; Espelage, D.L. Bullying in American Schools; Espelage, D.L., Swearer, S.M., Eds.; Routledge, 2004; ISBN 9781135624422.
  42. Swearer, S.M.; Espelage, D.L.; Vaillancourt, T.; Hymel, S. What Can Be Done About School Bullying? Educ. Res. 2010, 39, 38–47. [Google Scholar] [CrossRef]
  43. Swearer, S.M.; Hymel, S. Understanding the Psychology of Bullying: Moving toward a Social-Ecological Diathesis–Stress Model. Am. Psychol. 2015, 70, 344–353. [Google Scholar] [CrossRef] [PubMed]
  44. Cyr, K.; Chamberland, C.; Lessard, G.; Clément, M.-È.; Wemmers, J.-A.; Collin-Vézina, D.; Gagné, M.-H.; Damant, D. Polyvictimization in a Child Welfare Sample of Children and Youths. Psychol. Violence 2012, 2, 385–400. [Google Scholar] [CrossRef]
  45. Finkelhor, D.; Ormrod, R.; Turner, H.; Hamby, S.L. The Victimization of Children and Youth: A Comprehensive, National Survey. Child Maltreat. 2005, 10, 5–25. [Google Scholar] [CrossRef] [PubMed]
  46. Kilpatrick, D.G.; Ruggiero, K.J.; Acierno, R.; Saunders, B.E.; Resnick, H.S.; Best, C.L. Violence and Risk of PTSD, Major Depression, Substance Abuse/Dependence, and Comorbidity: Results from the National Survey of Adolescents. J. Consult. Clin. Psychol. 2003, 71, 692–700. [Google Scholar] [CrossRef]
  47. Finkelhor, D. , Turner, H., Hamby, S., & Ormrod, R. Poly-Victimization: Children’s Exposure to Multiple Types of Violence, Crime, and Abuse. Juv. Justice Bull. – NCJ 235504.
  48. Bacchini, D.; Esposito, C. Growing up in Violent Contexts: Differential Effects of Community, Family, and School Violence on Child Adjustment; Balvin, N., Christie, D., Eds.; Children and peace. peace psychology book series; Springer: Cham, 2020.
  49. Margolin, G.; Vickerman, K.A.; Oliver, P.H.; Gordis, E.B. Violence Exposure in Multiple Interpersonal Domains: Cumulative and Differential Effects. J. Adolesc. Heal. 2010, 47, 198–205. [Google Scholar] [CrossRef]
  50. Mrug, S.; Windle, M. Prospective Effects of Violence Exposure across Multiple Contexts on Early Adolescents’ Internalizing and Externalizing Problems. J. Child Psychol. Psychiatry 2010, 51, 953–961. [Google Scholar] [CrossRef] [PubMed]
  51. Guerra, N.G.; Huesmann, L.R.; Spindler, A. Community Violence Exposure, Social Cognition, and Aggression among Urban Elementary School Children. Child Dev. 2003. [Google Scholar] [CrossRef] [PubMed]
  52. Horowitz, K.; McKay, M.; Marshall, R. Community Violence and Urban Families: Experiences, Effects, and Directions for Intervention. Am. J. Orthopsychiatry 2005, 75, 356–368. [Google Scholar] [CrossRef] [PubMed]
  53. GORMAN–SMITH, D.; TOLAN, P. The Role of Exposure to Community Violence and Developmental Problems among Inner-City Youth. Dev. Psychopathol. 1998, 10, 101–116. [Google Scholar] [CrossRef] [PubMed]
  54. Crick, N.R.; Dodge, K.A. A Review and Reformulation of Social Information-Processing Mechanisms in Children’s Social Adjustment. Psychol. Bull. 1994, 115, 74–101. [Google Scholar] [CrossRef]
  55. Shahinfar, A.; Kupersmidt, J.B.; Matza, L.S. The Relation between Exposure to Violence and Social Information Processing among Incarcerated Adolescents. J. Abnorm. Psychol. 2001, 110, 136–141. [Google Scholar] [CrossRef] [PubMed]
  56. Cooley-Quille, M.; Boyd, R.C.; Frantz, E.; Walsh, J. Emotional and Behavioral Impact of Exposure to Community Violence in Inner-City Adolescents. J. Clin. Child Adolesc. Psychol. 2001, 30, 199–206. [Google Scholar] [CrossRef] [PubMed]
  57. Aisenberg, E.; Ayón, C.; Orozco-Figueroa, A. The Role of Young Adolescents’ Perception in Understanding the Severity of Exposure to Community Violence and PTSD. J. Interpers. Violence 2008, 23, 1555–1578. [Google Scholar] [CrossRef]
  58. Bowes, L.; Arseneault, L.; Maughan, B.; Taylor, A.; Caspi, A.; Moffitt, T.E. School, Neighborhood, and Family Factors Are Associated With Children’s Bullying Involvement: A Nationally Representative Longitudinal Study. J. Am. Acad. Child Adolesc. Psychiatry 2009, 48, 545–553. [Google Scholar] [CrossRef]
  59. Cluver, L.; Bowes, L.; Gardner, F. Risk and Protective Factors for Bullying Victimization among AIDS-Affected and Vulnerable Children in South Africa. Child Abuse Negl. 2010, 34, 793–803. [Google Scholar] [CrossRef]
  60. Ferguson, C.J.; San Miguel, C.; Hartley, R.D. A Multivariate Analysis of Youth Violence and Aggression: The Influence of Family, Peers, Depression, and Media Violence. J. Pediatr. 2009, 155, 904–908. [Google Scholar] [CrossRef]
  61. Holt, S.; Buckley, H.; Whelan, S. The Impact of Exposure to Domestic Violence on Children and Young People: A Review of the Literature. Child Abuse Negl. 2008, 32, 797–810. [Google Scholar] [CrossRef] [PubMed]
  62. Moretti, M.M.; Obsuth, I.; Odgers, C.L.; Reebye, P. Exposure to Maternal vs. Paternal Partner Violence, PTSD, and Aggression in Adolescent Girls and Boys. Aggress. Behav. 2006, 32, 385–395. [Google Scholar] [CrossRef]
  63. Mustanoja, S.; Luukkonen, A.-H.; Hakko, H.; Räsänen, P.; Säävälä, H.; Riala, K. Is Exposure to Domestic Violence and Violent Crime Associated with Bullying Behaviour Among Underage Adolescent Psychiatric Inpatients? Child Psychiatry Hum. Dev. 2011, 42, 495–506. [Google Scholar] [CrossRef]
  64. Espelage, D.L.; Bosworth, K.; Simon, T.R. Examining the Social Context of Bullying Behaviors in Early Adolescence. J. Couns. Dev. 2000, 78, 326–333. [Google Scholar] [CrossRef]
  65. Swearer, S.M.; Doll, B. Bullying in Schools. J. Emot. Abus. 2001, 2, 7–23. [Google Scholar] [CrossRef]
  66. Andershed, H.; Kerr, M.; Stattin, H. Bullying in School and Violence on the Streets: Are the Same People Involved? J. Scand. Stud. Criminol. Crime Prev. 2001, 2, 31–49. [Google Scholar] [CrossRef]
  67. Dodge, K.A.; Pettit, G.S. A Biopsychosocial Model of the Development of Chronic Conduct Problems in Adolescence. Dev. Psychol. 2003, 39, 349–371. [Google Scholar] [CrossRef]
  68. Bronfenbrenner, U.; Morris, P.A. The Ecology of Developmental Processes. Vol. 1: Theoretical Models of Human Development. In Handbook of child psychology; 1998.
  69. Bandura, A. Self-Efficacy: Toward a Unifying Theory of Behavioral Change. Psychol. Rev. 1977, 84, 191–215. [Google Scholar] [CrossRef]
  70. Bandura, A. Social Foundations of Thought and Action: A Social Cognitive Theory.; Prentice-Hall: Englewood Cliffs, 1986. [Google Scholar]
  71. De Angelis, G.; Bacchini, D.; Affuso, G. The Mediating Role of Domain Judgement in the Relation between the Big Five and Bullying Behaviours. Pers. Individ. Dif. 2016, 90, 16–21. [Google Scholar] [CrossRef]
  72. Barriga, A. Q. , Gibbs, J. C., Potter, G., & Liau, A.K. The How I Think Questionnaire Manual; Research Press: Champaign, IL, 2001. [Google Scholar]
  73. Gibbs, J. C. , Potter, G. B., & Goldstein, A.P. The EQUIP Program: Teaching Youth to Think and Act Responsibly through a Peer-Helping Approach; Reserch Press: Champaign, IL, 1995. [Google Scholar]
  74. Nas, C.N.; Brugman, D.; Koops, W. Effects of the EQUIP Programme on the Moral Judgement, Cognitive Distortions, and Social Skills of Juvenile Delinquents. Psychol. Crime Law 2005, 11, 421–434. [Google Scholar] [CrossRef]
  75. Gibbs, J.C.; Potter, G.B.; Barriga, A.Q.; Liau, A.K. Developing the Helping Skills and Prosocial Motivation of Aggressive Adolescents in Peer Group Programs. Aggress. Violent Behav. 1996, 1, 283–305. [Google Scholar] [CrossRef]
  76. Kohlberg, L. Essays on Moral Development; Harper & Row: San Francisco, CA; Vol. 2;
  77. Bandura, A. Social Cognitive Theory of Self-Regulation. Organ. Behav. Hum. Decis. Process. 1991, 50, 248–287. [Google Scholar] [CrossRef]
  78. Sykes, G.M.; Matza, D. Techniques of Neutralization: A Theory of Delinquency. Am. Sociol. Rev. 1957, 22, 664. [Google Scholar] [CrossRef]
  79. Ribeaud; Eisner The Nature of the Association Between Moral Neutralization and Aggression: A Systematic Test of Causality in Early Adolescence. Merrill. Palmer. Q. 2015, 61, 68. [CrossRef]
  80. Maruna, S.; Copes, H. What Have We Learned from Five Decades of Neutralization Research? Crime and Justice 2005, 32, 221–320. [Google Scholar] [CrossRef]
  81. Aquilar, S.; Bacchini, D.; Affuso, G. Three-year Cross-lagged Relationships among Adolescents’ Antisocial Behavior, Personal Values, and Judgment of Wrongness. Soc. Dev. 2018, 27, 381–400. [Google Scholar] [CrossRef]
  82. Yuchtman-Ya’Ar, E. Value Priorities in Israeli Society: An Examination of Inglehart’s Theory of Modernization and Cultural Variation. Comp. Sociol. 2002, 1, 347–367. [Google Scholar] [CrossRef]
  83. Bandura, A. Social Learning Theory of Aggression. J. Commun. 1978, 28, 12–29. [Google Scholar] [CrossRef]
  84. Dodge, K. A., Coie, J. D. & Lynam, D. Aggression and Antisocial Behaviour in Youth. In Handbook of Child Psychology: Social, Emotional, and Personality Development; N. Eisenberg, W. Damon, & R.M.L., Ed.; John Wiley & Sons: Hoboken, NJ, 2006; Vol. 3, pp. 719–788.
  85. Nickoletti, P.; Taussig, H.N. Outcome Expectancies and Risk Behaviors in Maltreated Adolescents. J. Res. Adolesc. 2006, 16, 217–228. [Google Scholar] [CrossRef]
  86. Calvete, E. Justification of Violence Beliefs and Social Problem-Solving as Mediators between Maltreatment and Behavior Problems in Adolescents. Span. J. Psychol. 2007, 10, 131–140. [Google Scholar] [CrossRef]
  87. Calvete, E.; Orue, I. Cognitive Mechanisms of the Transmission of Violence: Exploring Gender Differences among Adolescents Exposed to Family Violence. J. Fam. Violence 2013, 28, 73–84. [Google Scholar] [CrossRef]
  88. Herrenkohl, T.I.; Huang, B.; Tajima, E.A.; Whitney, S.D. Examining the Link between Child Abuse and Youth Violence. J. Interpers. Violence 2003, 18, 1189–1208. [Google Scholar] [CrossRef]
  89. Calvete, E.; Fernández-González, L.; González-Cabrera, J.M.; Gámez-Guadix, M. Continued Bullying Victimization in Adolescents: Maladaptive Schemas as a Mediational Mechanism. J. Youth Adolesc. 2018, 47, 650–660. [Google Scholar] [CrossRef] [PubMed]
  90. Huesmann, L.R.; Guerra, N.G. Children’s Normative Beliefs about Aggression and Aggressive Behavior. J. Pers. Soc. Psychol. 1997, 72, 408–419. [Google Scholar] [CrossRef]
  91. Lee, V.; Hoaken, P.N.S. Cognition, Emotion, and Neurobiological Development: Mediating the Relation Between Maltreatment and Aggression. Child Maltreat. 2007, 12, 281–298. [Google Scholar] [CrossRef]
  92. Ponce, A.N.; Williams, M.K.; Allen, G.J. Experience of Maltreatment as a Child and Acceptance of Violence in Adult Intimate Relationships: Mediating Effects of Distortions in Cognitive Schemas. Violence Vict. 2004, 19, 97–108. [Google Scholar] [CrossRef]
  93. Wilkinson, D.L.; Carr, P.J. Violent Youths’ Responses to High Levels of Exposure to Community Violence: What Violent Events Reveal about Youth Violence. J. Community Psychol. 2008, 36, 1026–1051. [Google Scholar] [CrossRef]
  94. Bacchini, D.; Affuso, G.; De Angelis, G. Moral vs. Non-Moral Attribution in Adolescence: Environmental and Behavioural Correlates. Eur. J. Dev. Psychol. 2013, 10, 221–238. [Google Scholar] [CrossRef]
  95. Esposito, C.; Affuso, G.; Dragone, M.; Bacchini, D. Effortful Control and Community Violence Exposure as Predictors of Developmental Trajectories of Self-Serving Cognitive Distortions in Adolescence: A Growth Mixture Modeling Approach. J. Youth Adolesc. 2020, 49, 2358–2371. [Google Scholar] [CrossRef]
  96. Esposito, C.; Spadari, E.M.; Caravita, S.C.S.; Bacchini, D. Profiles of Community Violence Exposure, Moral Disengagement, and Bullying Perpetration: Evidence from a Sample of Italian Adolescents. J. Interpers. Violence 2022, 37, 5887–5913. [Google Scholar] [CrossRef]
  97. Ahlin, E.M.; Lobo Antunes, M.J. Levels of Guardianship in Protecting Youth Against Exposure to Violence in the Community. Youth Violence Juv. Justice 2017, 15, 62–83. [Google Scholar] [CrossRef]
  98. Salmivalli, C.; Lagerspetz, K.; Björkqvist, K.; Österman, K.; Kaukiainen, A. Bullying as a Group Process: Participant Roles and Their Relations to Social Status within the Group. Aggress. Behav. 1998, 22, 1–15. [Google Scholar] [CrossRef]
  99. Wang, C.; Ryoo, J.H.; Swearer, S.M.; Turner, R.; Goldberg, T.S. Longitudinal Relationships between Bullying and Moral Disengagement among Adolescents. J. Youth Adolesc. 2017, 46, 1304–1317. [Google Scholar] [CrossRef] [PubMed]
  100. CNEL, I. (2014) Bes 2016. Il Benessere Equo e Sostenibile in Italia [Bes 2016. The Equitable and Sustainable Welfare in Italy]. Available online: https://www.istat.it/it/files/2016/12/BES-2016.pdf (accessed on 25 April 2024).
  101. Istituto Nazionale di Statistica (ISTAT) Ciclo Di Audizioni Sul Tema Della Dispersione Scolastica [Series of Hearings on Early School Leaving]. Available online: https://www.istat.it/it/files/2021/07/Istat-Audizione-Dispersione-scolastica_18-giugno-2021.pdf (accessed on 25 April 2024).
  102. Little, R.J.A.; Rubin, D.B. Statistical Analysis with Missing Data; Wiley: New York, NY, 2002; ISBN 0471183865. [Google Scholar]
  103. Straus, M.A. Measuring Intrafamily Conflict and Violence: The Conflict Tactics (CT) Scales. J. Marriage Fam. 1979, 41, 75. [Google Scholar] [CrossRef]
  104. Bacchini, D.; De Angelis, G.; Affuso, G.; Brugman, D. The Structure of Self-Serving Cognitive Distortions. Meas. Eval. Couns. Dev. 2016, 49, 163–180. [Google Scholar] [CrossRef]
  105. Palladino, B.E.; Nocentini, A.; Menesini, E. Evidence-based Intervention against Bullying and Cyberbullying: Evaluation of the NoTrap! Program in Two Independent Trials. Aggress. Behav. 2016, 42, 194–206. [Google Scholar] [CrossRef] [PubMed]
  106. Caprara, G.V.; Barbaranelli, C.; Borgogni, L.; Perugini, M. The “Big Five Questionnaire”: A New Questionnaire to Assess the Five Factor Model. Pers. Individ. Dif. 1993, 15, 281–288. [Google Scholar] [CrossRef]
  107. Cole, D.A.; Maxwell, S.E. Testing Mediational Models With Longitudinal Data: Questions and Tips in the Use of Structural Equation Modeling. J. Abnorm. Psychol. 2003, 112, 558–577. [Google Scholar] [CrossRef] [PubMed]
  108. MacKinnon, D.P. Introduction to Statistical Mediation Analysis; Routledge, 2012; ISBN 9780203809556.
  109. Muthén, L.K.; Muthén, B.O. Mplus User’s Guide; 8th ed.; Muthén & Muthén: Los Angeles, CA, USA, 2017. [Google Scholar]
  110. Mo Wang; Bodner, T. E. Growth Mixture Modeling. Organ. Res. Methods 2007, 10, 635–656. [Google Scholar] [CrossRef]
  111. Bentler, P.M.; Yuan, K.-H. On Adding a Mean Structure to a Covariance Structure Model. Educ. Psychol. Meas. 2000, 60, 326–339. [Google Scholar] [CrossRef]
  112. Bentler, P.M. Comparative Fit Indexes in Structural Models. Psychol. Bull. 1990, 107, 238–246. [Google Scholar] [CrossRef] [PubMed]
  113. Tucker, L.R.; Lewis, C. A Reliability Coefficient for Maximum Likelihood Factor Analysis. Psychometrika 1973, 38, 1–10. [Google Scholar] [CrossRef]
  114. Browne, M.W.; Cudeck, R. Alternative Ways of Assessing Model Fit. Sociol. Methods Res. 1993, 21, 230–258. [Google Scholar] [CrossRef]
  115. Hu, L.; Bentler, P.M. Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Struct. Equ. Model. A Multidiscip. J. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  116. Satorra, A. Scaled and Adjusted Restricted Tests in Multi-Sample Analysis of Moment Structures BT - Innovations in Multivariate Statistical Analysis. In Innovations in Multivariate Statistical Analysis; 2000 ISBN 978-1-4613-7080-2.
  117. Garcia, O.F.; Serra, E.; Zacares, J.J.; Calafat, A.; Garcia, F. Alcohol Use and Abuse and Motivations for Drinking and Non-Drinking among Spanish Adolescents: Do We Know Enough When We Know Parenting Style? Psychol. Health 2020, 35, 645–664. [Google Scholar] [CrossRef] [PubMed]
  118. Bradshaw, C.P.; Waasdorp, T.E.; Goldweber, A.; Johnson, S.L. Bullies, Gangs, Drugs, and School: Understanding the Overlap and the Role of Ethnicity and Urbanicity. J. Youth Adolesc. 2013, 42, 220–234. [Google Scholar] [CrossRef]
  119. Youngblade, L.M.; Theokas, C.; Schulenberg, J.; Curry, L.; Huang, I.-C.; Novak, M. Risk and Promotive Factors in Families, Schools, and Communities: A Contextual Model of Positive Youth Development in Adolescence. Pediatrics 2007, 119, S47–S53. [Google Scholar] [CrossRef]
  120. Anderson, E. The Code of the Streets. In Anomie, Strain and Subcultural Theories of Crime; Anderson, E., Ed.; Routledge: New York, NY, 2017; pp. 303–312. [Google Scholar]
  121. Gibbs, J.C. Moral Reasoning Training: The Values Component. In New perspectives on aggression replacement training: practice, research, and application; Goldstein, P., Nensén, R., Daleflod, B., Kalt, M., Eds.; Wiley: West Sussex, 2004; pp. 50–72. [Google Scholar]
  122. Hindelang, M.J.; Gottfredson, M.R.; Garofalo, J. Victims of Personal Crime: An Empirical Foundation for a Theory of Personal Victimization; Ballinger Publishing Company: Cambridge, MA, 1978. [Google Scholar]
  123. Gini, G.; Pozzoli, T.; Hymel, S. Moral Disengagement among Children and Youth: A Meta-Analytic Review of Links to Aggressive Behavior. Aggress. Behav. 2014, 40, 56–68. [Google Scholar] [CrossRef]
  124. Curry, A.; Latkin, C.; Davey-Rothwell, M. Pathways to Depression: The Impact of Neighborhood Violent Crime on Inner-City Residents in Baltimore, Maryland, USA. Soc. Sci. Med. 2008, 67, 23–30. [Google Scholar] [CrossRef]
  125. Salmivalli, C. Bullying and the Peer Group: A Review. Aggress. Violent Behav. 2010, 15, 112–120. [Google Scholar] [CrossRef]
  126. Hamaker, E.L.; Kuiper, R.M.; Grasman, R.P.P.P. A Critique of the Cross-Lagged Panel Model. Psychol. Methods 2015, 20, 102–116. [Google Scholar] [CrossRef] [PubMed]
  127. DiBiase, A.M.; Gibbs, J.C.; Potter, G.B.; Spring, B. EQUIP for Educators: Teaching Youth (Grades 5-8) to Think and Act Responsibly; Research Press: Champaign, IL, 2005. [Google Scholar]
  128. Dragone, M.; Esposito, C.; De Angelis, G.; Bacchini, D. Equipping Youth to Think and Act Responsibly: The Effectiveness of the “EQUIP for Educators” Program on Youths’ Self-Serving Cognitive Distortions and School Bullying Perpetration. Eur. J. Investig. Heal. Psychol. Educ. 2022, 12, 814–834. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Cross-lagged mediational model with domestic violence exposure as a witness and as a victim. Reported coefficients refer to standardized estimates. In bold lines represent significant indirect paths. For the sake of simplicity, nonsignificant paths, and relations with control variables are omitted. * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 1. Cross-lagged mediational model with domestic violence exposure as a witness and as a victim. Reported coefficients refer to standardized estimates. In bold lines represent significant indirect paths. For the sake of simplicity, nonsignificant paths, and relations with control variables are omitted. * p < 0.05, ** p < 0.01, *** p < 0.001.
Preprints 107819 g001
Figure 2. Cross-lagged mediational model with community violence exposure as a witness and as a victim. Reported coefficients refer to standardized estimates. In bold lines represent significant indirect paths. For the sake of simplicity, nonsignificant paths, and relations with control variables are omitted. * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 2. Cross-lagged mediational model with community violence exposure as a witness and as a victim. Reported coefficients refer to standardized estimates. In bold lines represent significant indirect paths. For the sake of simplicity, nonsignificant paths, and relations with control variables are omitted. * p < 0.05, ** p < 0.01, *** p < 0.001.
Preprints 107819 g002
Table 1. (a) Correlations among the study’s variables; (b) Correlations among the study’s variables.
Table 1. (a) Correlations among the study’s variables; (b) Correlations among the study’s variables.
(a)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1. T1 DVW 1
2. T1 DVV 0.61*** 1
3. T1 CDs 0.18*** 0.26*** 1
4. T1 BP 0.18*** 0.28*** 0.34*** 1
5. T2 DVW 0.54*** 0.36*** 0.17*** 0.24*** 1
6. T2 DVV 0.34*** 0.51*** 0.21*** 0.24*** 0.67*** 1
7. T2 CDs 0.08* 0.19*** 0.62*** 0.32*** 0.24*** 0.32*** 1
8. T2 BP 0.04 0.13*** 0.23*** 0.37*** 0.20*** 0.23*** 0.33*** 1
9. T3 DVW 0.47*** 0.33*** 0.14*** 0.16*** 0.48*** 0.39*** 0.17*** 0.11** 1
10. T3 DVV 0.32*** 0.45*** 0.19*** 0.20*** 0.40*** 0.58*** 0.30*** 0.21*** 0.66*** 1
11. T3 CDs 0.06 0.17*** 0.43*** 0.23*** 0.14*** 0.24*** 0.53*** 0.30*** 0.19*** 0.31*** 1
12. T3 BP 0.11** 0.23*** 0.24*** 0.29*** 0.16*** 0.29*** 0.34*** 0.42*** 0.29*** 0.35*** 0.44*** 1
13. T4 DVW 0.37*** 0.28*** 0.16*** 0.22*** 0.46*** 0.32*** 0.13*** 0.16*** 0.36*** 0.28*** 0.14*** 0.19*** 1
14. T4 DVV 0.30*** 0.43*** 0.20*** 0.26*** 0.38*** 0.48*** 0.20*** 0.19*** 0.27*** 0.41*** 0.26*** 0.24*** 0.71*** 1
15. T4 CDs 0.07 0.14*** 0.43*** 0.26*** 0.10* 0.17*** 0.47*** 0.32*** 0.14*** 0.22*** 0.54*** 0.36*** 0.19*** 0.22*** 1
16. T4 BP 0.06 0.12** 0.23*** 0.39*** 0.12** 0.14*** 0.29*** 0.32*** 0.15*** 0.23*** 0.36*** 0.39*** 0.19*** 0.23*** 0.41*** 1
M 1.33 1.62 2.31 1.30 1.35 1.58 2.19 1.32 1.33 1.47 2.16 1.32 1.33 1.46 2.03 1.25
SDs 0.62 0.81 0.86 0.53 0.65 0.79 0.84 0.54 0.66 0.76 0.91 0.56 0.70 0.79 0.91 0.51
Scores range 1-5 1-6 1-5 1-5 1-6 1-5 1-5 1-6 1-5 1-5 1-6 1-5
Note. DVW = Domestic Violence Witnessing; DVV = Domestic Violence Victimization; CDs = Cognitive Distortions; BP = Bullying Perpetration. * p < 0.05, ** p < 0.01, *** p < 0.001.
(b)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1. T1 CVW 1
2. T1 CVV 0.48*** 1
3. T1 CDs 0.34*** 0.21*** 1
4. T1 BP 0.33*** 0.23*** 0.34*** 1
5. T2 CVW 0.45*** 0.26*** 0.22*** 0.29*** 1
6. T2 CVV 0.18*** 0.28*** 0.10** 0.16*** 0.51*** 1
7. T2 CDs 0.23*** 0.15*** 0.62*** 0.32*** 0.33*** 0.17*** 1
8. T2 BP 0.19*** 0.09* 0.23*** 0.37*** 0.28*** 0.19*** 0.33*** 1
9. T3 CVW 0.30*** 0.24*** 0.11** 0.20*** 0.41*** 0.25*** 0.18*** 0.13*** 1
10. T3 CVV 0.12** 0.22*** 0.09* 0.12** 0.16*** 0.24*** 0.12** 0.09* 0.64*** 1
11. T3 CDs 0.22*** 0.12*** 0.43*** 0.23*** 0.29*** 0.18*** 0.53*** 0.30*** 0.22*** 0.16*** 1
12. T3 BP 0.20*** 0.15*** 0.24*** 0.29*** 0.25*** 0.22*** 0.34*** 0.42*** 0.27*** 0.25*** 0.44*** 1
13. T4 CVW 0.28*** 0.16*** 0.20*** 0.19*** 0.36*** 0.24*** 0.18*** 0.17*** 0.36*** 0.19*** 0.24*** 0.22*** 1
14. T4 CVV 0.09* 0.10* 0.14*** 0.13*** 0.12** 0.17*** 0.09* 0.09* 0.16*** 0.23*** 0.09* 0.11** 0.64*** 1
15. T4 CDs 0.29*** 0.14*** 0.43*** 0.26*** 0.19*** 0.08* 0.47*** 0.32*** 0.14*** 0.07 0.54*** 0.36*** 0.26*** 0.14*** 1
16. T4 BP 0.17*** 0.10* 0.23*** 0.39*** 0.21*** 0.15*** 0.29*** 0.32*** 0.20*** 0.11** 0.36*** 0.39*** 0.25*** 0.13** 0.41*** 1
M 1.72 1.46 2.31 1.30 1.75 1.40 2.19 1.32 1.65 1.38 2.16 1.32 1.64 1.40 2.03 1.25
SDs 0.74 0.62 0.86 0.53 0.80 0.54 0.84 0.54 0.89 0.71 0.91 0.56 0.86 0.77 0.91 0.51
Scores range 1-5 1-6 1-5 1-5 1-6 1-5 1-5 1-6 1-5 1-5 1-6 1-5
Note. CVW = Community Violence Witnessing; CVV = Community Violence Victimization; CDs = Cognitive Distortions; BP = Bullying Perpetration. * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 2. Indirect effects of violence exposure on bullying perpetration via self-serving cognitive distortion (CDs) and reciprocal indirect associations between self-serving CDs and bullying perpetration.
Table 2. Indirect effects of violence exposure on bullying perpetration via self-serving cognitive distortion (CDs) and reciprocal indirect associations between self-serving CDs and bullying perpetration.
Domestic Violence Exposure
Indirect Paths β (SE) 95% C.I.s
LL UL
T1 Victimization → T2 CDs → T3 Bullying Perpetration 0.01* (0.00) 0.002 0.017
T2 Victimization → T3 CDs → T4 Bullying Perpetration 0.01* (0.00) 0.002 0.018
T1 CDs → T2 Bullying Perpetration → T3 CDs 0.02*** (0.00) 0.009 0.026
T2 CDs → T3 Bullying Perpetration → T4 CDs 0.02*** (0.00) 0.008 0.025
T1 Bullying Perpetration → T2 CDs → T3 Bullying Perpetration 0.02*** (0.01) 0.009 0.028
T2 Bullying Perpetration → T3 CDs → T4 Bullying Perpetration 0.02*** (0.01) 0.010 0.029
Community Violence Exposure
T1 Witnessing → T2 CDs → T3 Bullying Perpetration 0.01* (0.00) 0.001 0.018
T2 Witnessing → T3 CDs → T4 Bullying Perpetration 0.01* (0.01) 0.001 0.020
T1 CDs → T2 Bullying Perpetration → T3 CDs 0.02*** (0.00) 0.009 0.025
T2 CDs → T3 Bullying Perpetration → T4 CDs 0.02*** (0.00) 0.008 0.024
T1 Bullying Perpetration → T2 CDs → T3 Bullying Perpetration 0.02*** (0.01) 0.008 0.026
T2 Bullying Perpetration → T3 CDs → T4 Bullying Perpetration 0.02*** (0.01) 0.010 0.028
Note. β = Standardized estimates; SE = Standard error; C.I.s = Confidence intervals; LL = Lower limit, UL = Upper limit; * p < 0.05, *** p < 0.001.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

Subscribe

© 2024 MDPI (Basel, Switzerland) unless otherwise stated