1. Introduction
Adolescence is a developmental stage characterized by the increased importance of relationships outside the family, given that peer groups and romantic relationships are essential challenges for youth development during this period [1]. Indeed, romantic relationships are crucial challenges for identity formation [2,3], the development of social competence and self-esteem [3], and mental health status and well-being [4].
In particular, adolescent relationships enable the acquisition of social competencies (such as affection, support, care, and intimacy) that promote their positive development [5]. Alongside normative developmental trajectories, however, adolescence could also be characterized by aggressive and antisocial trajectories with lifelong negative consequences for the mental health and well-being of both perpetrators and victims, such as teen dating violence (TDV) [6,7,8,9,10].
Teen dating violence (TDV) is any intentional hostility and aggression by one partner against another in adolescent or youth relationships [11]. Physical TDV occurs when a person intentionally injures or attempts to injure a partner by hitting, kicking, pulling hair, threatening to hit, pushing, shoving, shaking, ridiculing, and ruining or threatening to damage something of value to the victim. Verbal–emotional TDV is the use of verbal and non-verbal communication to emotionally hurt another person and/or exert control over them [12].
Given its great spreading worldwide in recent years, particularly in North America, some studies have investigated its diffusion and prevalence rates. For instance, Wincentak et al. [6] found an overall rate of 20.0% for physical TDV, with Herbert et al. [13] showing a prevalence of 33.09% for psychological TDV. Moving to European countries, a recent systematic review [8] measured the prevalence rates of TDV acted and suffered across ten European countries, showing prevalence rates of physical TDV perpetration ranging from 4.8% to 46.0% with psychological TDV perpetration ranging from 7.0% to 97.0%. Regarding victims, physical TDV victimization rates ranged from 0.8% to 32.9%. In comparison, psychological TDV victimization rates varied from 5.6% to 95.5%. However, the authors concluded that evidence in Europe and Italy on prevalence and risk factors associated with youth involvement as both perpetrators and victims of TDV is still scarce.
Furthermore, less is known about patterns of co-occurrence of peer aggressive be-haviors and their possible transfer to early dating situations among early adolescents. Among these studies, results highlighted that many middle school students were yet involved as both victims and perpetrators of TDV, thus supporting the importance of deepening and investigating the developmental trajectories of such partners of aggressive behaviors, especially involving early adolescents that are just beginning to be involved in romantic relationships [14,15,16].
Given the worldwide growing diffusion of TDV among early adolescents and youth and its adverse long-term behavioral and health consequences [17], studies in the last years tried to investigate risk factors associated with the involvement in TDV.
At the individual level, some studies underlined the co-occurrence of peer aggressive behaviors such as school bullying and cyberbullying and TDV [15,18,19,20,21,22].
Being involved in school bullying or victimization is associated with an increased likelihood of being a victim or perpetrator of TDV [21,23,24,25,26,27]. A recent systematic review and meta-analysis [21] found that school bullying is associated with the involvement as a perpetrator or victim of TDV and that school victimization was correlated with TDV victimization.
In this regard, research also examined the relationship between the involvement in deviant behaviors and TDV, showing positive associations between deviant behaviors and both TDV victimization [28] and perpetration [9]. Furthermore, a recent meta-analysis [29] found a co-occurrence between involvement in deviant behaviors, school victimization, and TDV victimization.
At the familial level, the leading risk factor associated with adolescents’ involvement in various aggressive behaviors is witnessing IPV, which is widely recognized as a complex traumatic experience linked to an increased risk of adverse physical, emotional/psychological, and sexual outcomes later in life [30,31]. Some studies found that witnessing IPV, in general, is one of the most robust and significant predictors of interpersonal violence among adults and adolescents [32].
Indeed, researchers have found an association between witnessing IPV and behavioral and relationship problems, such as involvement in aggressive behaviors against peers, bullying, and cyberbullying [24,33,34,35] both as perpetrators and victims [34]. Furthermore, recent studies have found that children exposed to IPV are likelier to engage in TDV [33,36]. Notably, Ruel et al. [37] reported a strong positive association between witnessing IPV and TDV victimization, while on the contrary, other studies found a positive association with the involvement as perpetrators in TDV perpetration [38,39]. Mixed results also emerged concerning the existence of gender differences in the relationship between witnessing IPV and being involved in TDV [33] (Evans et al., 2022).
Concerning TDV victimization, some studies found that witnessing IPV predicted TDV victimization in girls but not boys [37,40], while contrasting results emerged about TDV perpetration. In contrast, other studies underlined the existence of a significant relationship between witnessing IPV and TDV perpetration for boys but not girls [41,42], while others found a significant association for girls but not boys [37,40,43].
Concerning school-level risk factors, as far as we know, no studies analyzed the relationship between involvement in TDV and violence against teachers. However, violence against teachers is considered the result of a perceived adverse school climate [44] and a significant risk factor for youth involvement in antisocial and other violent behaviors [45,46,47] such as school bullying [48,49].
To sum up, the studies mentioned above focused on individual, family, and school risk factors analyzing their role in the involvement in TDV as perpetrators and victims. Adopting the socio-ecological theory [50] and the social-learning [51] we aimed to test a comprehensive model that includes several risk factors (and their interplay) for verbal–emotional and physical TDV such as witnessing IPV, involvement in school bullying and victimization, cyberbullying and cybervictimization, deviant behaviors, and violence against teachers.
In particular, the social-learning theory [51] helps to understand how violent behaviors learned in one social context, such as witnessing IPV, could continue and be transferred in a different context [52], shaping early adolescents and youth involvement in peer aggressive, deviant, and intimate violent behaviors in dating situations.
On the other side, adopting the social-ecological framework [50] makes it possible to identify significant risk factors for TDV perpetration and victimization by collocating them in their respective ecological system, and to investigate how poly-involvement in peer aggressive, violent, and deviant behaviors operate and interact with each other, influencing early adolescents involvement in TDV both as perpetrators and victims.
In line with the above-mentioned theoretical frameworks, we formulated the following hypothesis:
(1). Witnessing IPV directly affected early adolescents involvement in both physical and verbal–emotional TDV acted and suffered at T2;
(2). Witnessing IPV directly affected early adolescents’ involvement in school bullying at T1;
(3). Involvement in school bullying predicts the poly-involvement in cyberbullying, deviant behaviors, and violence against teachers at T1;
(4). Involvement in cyberbullying, deviant behaviors, and violence against teachers at the baseline and physical and verbal–emotional TDV acted at T2;
(5). Witnessing IPV directly affected early adolescents’ involvement in school victimization at T1;
(6). Involvement in school victimization predicts cybervictimization at T1;
(7). Involvement in cybervictimization affects early adolescents’ physical and verbal–emotional TDV victimization at T2.
Considering the co-occurrence and the overlap between peer aggressive behaviors and TDV, we further hypothesized that:
(1). Involvement in school bullying predicts also cybervictimization, deviant behaviors, and violence against teachers at T1;
(2). Involvement in cybervictimization, deviant behaviors, and violence against teachers at the baseline and physical and verbal–emotional TDV both acted and suffered at T2;
(3). School victimization predicts cyberbullying, deviant behaviors, and violence against teachers at T1;
(4). Involvement in cyberbullying, deviant behaviors, and violence against teachers at the baseline and physical and verbal–emotional TDV both acted and suffered at T2.
As result of these hypotheses about the interrelationships among individual, familial, and school-based risk factors influencing teen dating violence, we hypothesized a path model in which witnessing IPV predicted both school bullying and victimization that in turn predicted cyberbullying, cybervictimization, deviant behaviors, and violence against teachers. Finally, these variables measured at baseline (T1) affected both emotional–verbal and physical TDV acted and suffered after 6 months (T2). We further hypothesized a direct relationship between witnessing IPV and verbal–emotional and physical TDV acted and suffered at T2. Similarly, we expected a direct association between school bullying and victimization with verbal–emotional and physical TDV acted and suffered at T2 (Figure 1).
Considering the different patterns existing between witnessing IPV and involvement in TDV acted and suffered across gender [37,40,41,42,43] we also explored potential gender differences in the hypothesized paths.
2. Materials and Methods
2.1. Participants
The initial sample consisted of 235 students recruited from one middle school participating in a short-term longitudinal study.
Eventually, 195 students were included in the analyses as they had taken part and completed phases T1 and T2 (83% of the initial sample), and their questionnaire could be correctly matched.
The dropping out of 40 students was due to mistakes in filling in the matching ID code that students had to create to guarantee their anonymity or absence on the day of data collection.
Of all students, 47.2% were male and 52.8% female, aged between 10 and 14 years old (M = 11.62, SD = 0.63) (Table 1).
Regarding students’ involvement in peer aggressive behaviors at T1, respectively, 44.6% and 61.0% reported being school bullies and victims. In contrast, 22.6% reported cyberbullying others at least once in the past 6 months, and 31.3% were cybervictimized at least once in the past 6 months.
Concerning the involvement in violent behaviors against teachers, 13.8% of participants reported being violent against their teachers at least once, and 27.2% were involved in deviant behaviors, with the majority reporting they stole objects of small value (15.9%).
Of all participants, 35.8% were in a dating relationship within the last year. In this regard, respectively, 19.7% and 75.4% reported having perpetrated at least once physical and verbal–emotional TDV at the baseline (T1). A similar pattern emerged concerning TDV suffered, with 26.2% and 77.0% of participants having suffered at least once physical and verbal–emotional TDV.
2.2. Measures
To assess school bullying and victimization, 14 items from the Olweus Bully/Victim Questionnaire [53] were used. Seven items measured participants’ involvement in school bullying (α = 0.79) and the other seven assessed school victimization (α = 0.73) on a 5-point Likert scale (from 0 = “never” to 4 = “several times a week”).
Involvement in cyberbullying in the last 6 months was measured using the taxonomy developed by Willard [54] (2007) (e.g., “I created a fake profile to harm others” or “Have you experienced humiliation from someone online who sent or posted cruel gossip, rumours or other hurtful material about you?”). Five items assessed cyberbullying (α = 0.88) and five items measured participants’ involvement in cybervictimization (α = 0.85) on a 5-point Likert scale (from 0 = “never happened” to 4 = “several times a week”).
Similar to other studies involving a sample of early adolescents [16,25] (Taylor et al., 2017; Niolon et al., 2015), the Conflict in Adolescent Dating Relationships Inventory [55] (CADRI) was used to assess participants’ involvement in TDV. The “emotional/verbal abuse” subscale (e.g., “In the past year have you ever threatened to end the relationship to a boyfriend or girlfriend (someone that you have dated, gone out with, gone steady with?”) and the “physical abuse” subscale (e.g., “In the past year have you ever threw something at him/her?”), consisting of 10 and 4 items, respectively (αemotional/verbal abuse acted = 0.84; αemotional/verbal abuse suffered = 0.86; αphysical abuse acted = 0.52; αphysical abuse suffered = 0.90;). Participants rated their experience of TDV on a 4-point Likert scale (from 0 = “never” to 3 = “often”).
Involvement in deviant behaviors was assessed through five dichotomous item (e.g., “I have damaged or stolen others’ property”), with a total score ranging from 0 (absence of deviant behaviors) to 5 (α = 0.63).
To evaluate participants’ experience of witnessing IPV, an ad hoc dichotomous item was used (“Have you ever witnessed episodes of violence between the people you grew up with during childhood/adolescence?”).
Students’ aggressive behaviors against teachers were measured using 4 “yes” or “no” questions (e.g., “Have you teased your teachers online?”), with a total score ranging from 0 (absence of violent behaviors against teachers) to 4 (α = 0.70).
2.3. Procedure
Participants were recruited from a middle school located in Southern Italy. All students were invited to participate in a short-term longitudinal study with a 6 months follow-up through a letter. Students were enrolled in the study after parental written consent and individual oral assent. Students were asked to fill in an online anonymous questionnaire. The compilation took place in the school’s IT room during school hours, taking 40 min, under the research assistants’ supervision. To protect confidentiality, a unique code was derived for each individual who participated in the study. All procedures were by the Declaration of Helsinki and its later amendments [56] and approved by the Ethics Committee of the host institution.
2.4. Data Analyses
To check for potential auto-correlations between the TDV measurements at T1 and T2, we run the Durbin–Watson test [57] for each form of TDV.
In order to test our comprehensive model of the determinants measured at baseline (T1) of verbal–emotional and physical TDV at T2, a path analysis was conducted using AMOS version 21. Model fit was tested using maximum likelihood estimation and several fit indices [58,59,60]: root mean square error of approximation (RMSEA), comparative fit index (CFI), Normed Fit Index (NFI), and Tucker–Lewis index (TLI). RMSEA < 0.08 and RMSEA < 0.05 indicate acceptable and excellent model fit, respectively. CFI, NFI, and TLI > 0.90 indicates good model fit, while values > 0.95 indicate excellent model fit. In order to explore gender differences in the path model, we ran a multi-group path model inserting gender as grouping variable.
3. Results
The Durbin–Watson tests showed the absence of autocorrelations with all values between the cutoff of 2 and 2.5 (DW Verbal–Emotional TDV Acted: 2.4; DW Physical TDV Acted: 2.22, DW Verbal–Emotion TDV Suffered: 2.03, DW Physical TDV Suffered: 2.04).
The proposed model showed excellent fit to data: CFI: 1.00; TLI: 0.99; NFI: 0.99; RMSEA: 0.01, 95% C.I. = 0.00; 0.08, p = 0.72. Looking at the relationships among the variables of the model (Figure 2), as expected witnessing IPV predicted both involvement in school bullying (β = 0.22, SE = 0.50, p = 0.001) and in school victimization (β = 0.21, SE = 0.62, p = 0.003). In line with our hypothesis, school bullying predicted cyberbullying (β = 0.55, SE = 0.05, p < 0.001), cybervictimization, (β = 0.16, SE = 0.06, p = 0.015), violence against teachers (β = 0.70, SE = 0.01, p < 0.001), and deviant behaviors at T1 (β = 0.58, SE = 0.02, p < 0.001). Moving to school victimization at T1, it predicted only involvement in cyberbullying, (β = 0.12, SE = 0.04, p = 0.049) as well as cybervictimization at T1 (β = 0.42, SE = 0.05, p < 0.001). Contrary to our expectations, school victimization did not predict either violence against teachers, (β = −0.03, SE = 0.01, p = 0.571) or deviant behaviors at T1 (β = −0.06, SE = 0.02, p = 0.328).
Regarding our crucial variables, we observed a different pattern for the involvement as both perpetrator and victim in physical and verbal–emotional teen dating violence at T2. Starting from TDV victimization, physical TDV at T2 was predicted by cyberbullying at T1, (β = 0.88, SE = 0.06, p < 0.001) (i.e., high level of cyberbullying at T1 predicted high level of physical teen dating violence at T2) as well as by violence toward teachers a T1, (β = −0.44, SE = 0.17, p < 0.001) (i.e., high level of violence toward teachers at T1 predicted reduction in physical TDV at T2). Instead, verbal–emotional TDV at T2 was predicted only by cyberbullying at T1, (β = 0.39, SE = 0.38, p < 0.014) (i.e., high level of cyberbullying at T1 predicted high level of verbal–emotional TDV).
Moving to TDV perpetration, both physical and verbal–emotional TDV at T2 were predicted only by violence toward teachers at T1 but in an opposite way compared with physical TDV suffered: an increment of violence toward teachers at T1 predicted an increment of both physical (β = 0.58, SE = 0.17, p < 0.001) and verbal–emotional (β = 0.31, SE = 0.97, p = 0.012) TDV at T2.
Furthermore, in line with our hypothesis, school victimization at T1 showed a significant direct relationship with verbal–emotional TDV suffered at T2 (β = 0.34, SE = 0.20, p = 0.008).
Surprisingly a non-significant direct relationship emerged between witnessing IPV at T1 and verbal–emotional and physical TDV both acted and suffered at T2, as well as between school bullying at T1 and verbal–emotional and physical TDV both acted and suffered at T2.
The multi-group path model showed different paths for females and males.
3.1. TDV Paths for Females
In females, as expected, witnessing IPV predicted both involvement in school bullying (β = 0.35, SE = 0.68, p < 0.001) and in school victimization (β = 0.27, SE = 0.85, p = 0.005). School bullying predicted cyberbullying (β = 0.62, SE = 0.05, p < 0.001), violence against teachers (β = 0.77, SE = 0.02, p < 0.001), and deviant behaviors at T1 (β = 0.72, SE = 0.02, p < 0.001), but not cybervictimization, (β = 0.11, SE = 0.07, p = 0.182). School victimization at T1, in females predicted the involvement in cyberbullying, (β = 0.18, SE = 0.05, p = 0.017), as well as cybervictimization at T1 (β = 0.61, SE = 0.06, p < 0.001).
Contrary to our expectations, school victimization did not predict either violence against teachers (β = −0.01, SE = 0.01, p = 0.871) or deviant behaviors at T1 (β = −0.01, SE = 0.01, p = 0.646). Regarding TDV, we observed a different pattern for the involvement as both perpetrator and victim in physical and verbal–emotional TDV at T2.
Physical TDV victimization at T2 was predicted by cyberbullying at T1 (β = 1.27, SE = 0.06, p < 0.001) (i.e., high level of cyberbullying at T1 predicted high level of physical TDV suffered at T2) as well as by violence toward teachers a T1, (β = −0.48, SE = 0.13, p < 0.001) (i.e., high level of violence toward teachers at T1 predicted reduction in physical teen dating violence suffered at T2). Instead, verbal–emotional TDV victimization at T2 was predicted only by cyberbullying at T1 (β = 0.52, SE = 0.40, p = 0.002) (i.e., high level of cyberbullying at T1 predicted high level of verbal–emotional TDV suffered).
Moving to TDV perpetration, physical TDV at T2 was predicted by both violence against teachers (β = 0.77, SE = 0.27, p < 0.001, i.e., high level of violence toward teachers at T1 predicted increment of physical teen dating violence at T2) and by cybervictimization at T1 (β = −0.36, SE = 0.27, p = 0.06, i.e., high level of cybervictimization at T1 predicted reduction in physical teen dating violence at T2). Furthermore, in females, there emerged a significant direct relationship between school bullying at T1 and physical TDV at T2 both acted (β = −0.50, SE = 0.06, p = 0.012) and suffered (β = −0.57, SE = 0.03, p < 0.001), as well as between school victimization at T1 and verbal–emotional TDV suffered at T2 (β = 0.41, SE = 0.58, p = 0.007). Interestingly, in females, the verbal–emotional TDV at T2 was predicted only by school bullying at T1, so that a higher level of school bullying predicted an increment of verbal–emotional TDV acted at T2 (β = 0.54, SE = 0.52, p = 0.014).
3.2. TDV Paths for Males
Moving to the males, it is interesting to note that, contrary to what occurred for females, witnessing IPV did not predict either involvement in school bullying (β = 0.06, SE = 0.73, p = 0.584) or in school victimization (β = 0.13, SE = 0.91, p = 0.197). In line with our hypothesized model, school bullying predicted violence against teachers (β = 0.59, SE = 0.02, p < 0.001), deviant behaviors (β = 0.40, SE = 0.03, p < 0.001), cyberbullying (β = 0.46, SE = 0.09, p < 0.001), and cybervictimization (β = 0.28, SE = 0.10, p = 0.010). School victimization at T1 did not predict any variable of our hypothesized model (i.e., violence against teacher, deviant behaviors, cyberbullying, and cybervictimization). Moving to the crucial variable of our model, for both females and males we observed different pattern for the involvement as both perpetrator and victim in physical and verbal–emotional TDV at T2.
Physical TDV victimization at T2 was predicted by cyberbullying, (β = −1.06, SE = 0.06, p < 0.001, i.e., high level of cyberbullying at T1 predicted lower level of physical teen dating violence suffered at T2), cybervictimization (β = 1.08, SE = 0.08, p < 0.001, i.e., high level of cybervictimization at T1 predicted higher level of physical TDV suffered at T2) as well as by violence toward teachers a T1, (β = 0.26, SE = 0.31, p = 0.044), i.e., high level of violence toward teachers at T1 predicted an increment of physical teen dating violence suffered at T2), but not by deviant behaviors, (β = 0.08, SE = 0.18, p = 0.734).
Emotional–verbal TDV suffered at T2 was predicted by cybervictimization at T1 (β = 0.53, SE = 0.63, p < 0.001) (i.e., high level of cybervictimization at T1 predicted high level of verbal–emotional TDV suffered) and marginally by cyberbullying (β = −0.47, SE = 0.73, p = 0.056, i.e., high level of cyberbullying at T1 predicted a lower level of verbal–emotional TDV suffered).
Moving to TDV perpetration, in males physical TDV at T2 was predicted only by cyberbullying (β = −0.53, SE = 0.03, p = 0.031), whereas verbal–emotional TDV was predicted by school victimization (β = 0.55, SE = 0.36, p = 0.018) and marginally by cyberbullying (β = −0.46, SE = 0.06, p = 0.06). Furthermore, in males, involvement in school victimization at T1 predicted a reduction in physical TDV acted at T2, (β = −0.44, SE = 0.02, p = 0.017).
4. Discussion
The aim of the present study was to test a path model able to investigate individual (i.e., school bullying, school victimization, cyberbullying, cybervictimization, and deviant behaviors), familial (i.e., witnessing IPV), and school (i.e., violence against teachers) risk factors (and their interplay) for verbal–emotional and physical TDV after 6 months (T2).
First, our results showed that witnessing IPV significantly predicted both participants’ involvement in school bullying and victimization, confirming that family and youth violence are interconnected [61].
Moreover, adolescents’ involvement in school bullying and victimization is associated with cyberbullying; specifically, in our study, school bullying at T1 predicts both cybervictimization and cyberbullying at T1, while school victimization predicts only cyberbullying at T1, supporting the bidirectional relationship between victimization and perpetration [62].
In line with this result, several empirical studies have shown that the strongest predictors of cyberbullying were involvement in both school bullying and victimization [63,64,65,66]. In other words, school victims can exhibit the same negative behavioral pattern in cyberspace, resulting in involvement in cyberbullying [67].
Our findings on the temporal relationship between witnessing IPV, involvement in peer aggressive and deviant behaviors, and TDV led to several important conclusions about predictors of TDV, contributing to a scant and scarce literature [21].
In particular, cyberbullying at T1 predicted verbal–emotional and physical TDV victimization after 6 months (T2), consistent with a meta-analysis showing that school bullying was associated with TDV victimization [21], thus supporting the hypothesis that peer violence may be considered an earlier stage of TDV [68,69].
Interestingly according to our results, only the involvement in violence against teachers at T1 positively predicted the involvement as a perpetrator in physical and verbal–emotional TDV at T2, and negatively physical TDV suffered. Furthermore, participants’ involvement in violence against teachers was significantly associated with school bullying, as found by a recent meta-analysis [70] underlining that school bullying increases conflict in teacher–student relationships.
Even though, to the best of our knowledge, no studies investigated the relationship between violence against teachers and TDV, it is possible to speculate that youth involved in aggressive behaviors replicate a similar negative pattern of behaviors in various contexts. These multiplicities of aggressive behaviors in various interpersonal relationships are defined as poly-aggression and poly-victimization [71]. In line with this, and with Malik, Sorenson, Sorenson, and Aneshensel [52], we can hypothesize the existence of a significant association between exposure to violence and its perpetration in various contexts and TDV perpetration and victimization.
In sum, considering the development of aggressive behavior throughout life, it is plausible to assume that witnessing IPV, bullying, cyberbullying, TDV, and violence against teachers are all linked. Consequently, aggressive behaviors learned and established in one social environment (peers) can be generalized at school (violence toward teachers) and easily transferred to a new social context (romantic relationships).
It is important to note that, in our hypothesized model, a direct relationship did not emerge between witnessing IPV at T1 and verbal–emotional and physical TDV both acted and suffered at T2, as well as between school bullying at T1 and verbal–emotional and physical TDV both acted and suffered at T2. These results suggest the existence of an indirect path, and they highlight the importance of taking into account the interaction of the different individual, familial, and school risk factors affecting the involvement in TDV.
Concerning gender differences, our results showed the existence of a different pattern of individual, familial, and school-level risk factors at T1 affecting the involvement in physical and verbal–emotional TDV both acted and suffered at T2. In particular, differently from the female counterpart, witnessing IPV in males did not predict involvement in school bullying and victimization. Paths of TDV involvement seem to begin for males with their involvement in peer aggressive behaviors such as school bullying and cybervictimization that significantly increase males’ physical and verbal–emotional TDV victimization at T2. Furthermore, males’ physical TDV acted at T2 was predicted by cyberbullying while school victimization affected verbal–emotional TDV perpetration at T2. In other words, among males the involvement in TDV originates from their involvement in peer aggressive behaviors, thus supporting the hypothesis that males and females should exhibit different motivation to engage in TDV [72].
Our gender analyses proved to be consistent with the social-learning theory [51] that witnessing IPV directly affects females to be involved both as perpetrators and victims in peer aggressive behaviors and then in violent behaviors related to dating, transferring a relational model learned by parents [37,40,43].
In conclusion, we should speculate that females’ involvement in TDV could be affected by witnessing IPV and experiencing and acting different types of aggressive and violent behaviors. By contrast, the involvement in TDV for males could be explained by referring to their need for control, social dominance, and social status maintenance related to their previous involvement in school bullying [72]. In this regard, future studies should investigate and deepen the existence of possible gender differences concerning motivation affecting the involvement as both perpetrators and victims of TDV.
This study has some limitations that should be addressed in future studies.
The first possible limitation of our study is related to sample size, thus affecting the generalizability of our results. Despite this limitation, the longitudinal design of our study allowed us to evaluate the causal relationship between some individual, familial, and school risk factors and the involvement of youth both as perpetrators and victims in emotional–verbal and physical TDV after 6 months.
A further limitation of our study was the participants’ age, which was a sample mainly of pre-adolescents. This affects the prevalence of participants engaged in a dating relationship. Despite this limitation, our model shows interesting associations between aggressive behaviors in an age group that has been understudied concerning risk factors associated with TDV.
Even if the reliability coefficient of some scales were lower than 0.60, these values should be considered acceptable given the short scale dimension [73]. Furthermore, the lower reliability of physical TDV acted could be the result of participants’ social desirability, as also underlined by studies finding that for adolescent the most severe type of TDV perpetrated is physical TDV [6,74].
Another limitation of our study was the short-term interval of measurements between T1 and T2 (6 months), given that instances of TDV observed at T2 may have already manifested at T1. Future studies can replicate our results with a long-term design involving cohorts of both early adolescents and adolescents.
5. Conclusions
Our short-term longitudinal study showed that adolescents’ involvement in physical and emotional–verbal TDV was affected by different patterns of individual, familial, and school risk factors. In particular, our findings show an indirect path that starting from witnessing IPV leads adolescents to be involved in different forms of peer aggressive behaviors (bullying and cyberbullying) and violence against teachers that, in turn, result in the involvement as a perpetrator of both physical and verbal–emotional TDV. Conversely, a different pattern emerged for TDV victimization, highlighting that physical TDV was predicted by participants’ involvement in both cyberbullying and violence against teachers. In contrast, violent behaviors against teachers were associated with a lower involvement in verbal–emotional TDV suffered.
Our results could have several implications for preventive and intervention programs and activities.
First, the direct association between witnessing IPV and school bullying and victimization highlights the need to address youth violence as early as possible. It could be desirable to develop prevention activities since primary school by implementing programs to empower pupils’ emotional and social skills as a counterweight for the chronicity of learned family relationship patterns. Indeed, strengthening pupils’ positive relationships with other significant adults, such as teachers, should be crucial in preventing youth violence [75].
Second, our findings highlight the importance of continuing to investigate TDV prevalence and diffusion, including early adolescent samples. Furthermore, the investigation of the co-occurrence of school bullying, cyberbullying, and TDV needs to be deepened, also considering the possible roles overlap (or roles-inversion) in the involvement in such phenomena [20,76].
Finally, our results stress the need to develop, implement, and evaluate the effectiveness of holistic violence prevention programs that include specific curricula addressing bullying and cyberbullying [23] and violence against teachers, as violence in the peer and school context and the romantic context occur together [21,61] and are also predictive of each other. To our knowledge, few holistic programs have been developed to prevent and reduce early adolescents’ involvement in several aggressive behaviors [23]. Such integrated and holistic programs should also consider targeting possible differential path of risk factors related to gender differences and include specific curricula targeting verbal–emotional TDV, which is the most prevalent type of TDV.
Considering youth and adolescents’ risk of being involved in multiple forms of aggressive behaviors both as authors and perpetrators, the development of comprehensive preventive programs addressing different forms of child and adolescent violence, and strengthening protective factors at the individual, familial, peer, and school level, should promote children and adolescents’ well-being and safe peer and intimate relationships.
Conceptualization, A.A. and A.S.; methodology, A.A. and A.S., formal analysis, A.A. and A.S.; investigation, A.S. and M.S.; data curation, A.A., A.S. and M.S.; writing—original draft preparation, A.A., A.S. and M.S.; writing—review and editing, A.A., A.S. and M.S.; supervision, A.S.; project administration, A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Department of Psychology of University of Campania “Luigi Vanvitelli” (protocol code 29/2015 approved on 27 October 2015) for studies involving humans.
Informed consent was obtained from all subjects involved in the study.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 2. Significant paths of risk factors for teen dating violence (TDV). For each path, standardized coefficients are reported.
Descriptive statistics of the sample.
| % | M (SD) | Min | Max | ||
|---|---|---|---|---|---|
| Sex | 47.2% M | 0 | 1 | ||
| Age | 11.62 (0.63) | 10 | 14 | ||
| School bullying | Never | 55.4% | 1.48 (2.92) | 0 | 22 |
| At least once | 44.6% | ||||
| School victimization | Never | 39.0% | 2.85 (3.60) | 0 | 15 |
| At least once | 61.0% | ||||
| Cyberbullying | Never | 77.4% | 0.72 (2.32) | 0 | 20 |
| At least once | 22.6% | ||||
| Cybervictimization | Never | 69.7% | 1.12 (2.73) | 0 | 20 |
| At least once | 31.3% | ||||
| Deviant behaviors | Never | 72.8% | 0.45 (0.90) | 0 | 5 |
| At least once | 27.2% | ||||
| Witnessing IPV | Yes | 20.5% | 0 | 1 | |
| Violence against teachers | Never | 86.2% | 0.24 (0.68) | 0 | 4 |
| At least once | 13.8% | ||||
| Physical TDV acted | Never | 80.3% | 0.88 (2.49) | 0 | 12 |
| At least once | 19.7% | ||||
| Verbal–emotional TDV acted | Never | 24.6% | 6.06 (5.30) | 0 | 28 |
| At least once | 75.4% | ||||
| Physical TDV suffered | Never | 73.8% | 0.61 (1.42) | 0 | 8 |
| At least once | 26.2% | ||||
| Verbal–emotional TDV suffered | Never | 33.0% | 5.52 (6.21) | 0 | 26 |
| At least once | 77.0% |
Note: TDV = teen dating violence.
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; Santamato, Margherita 1 ; Aquino, Antonio 2
1 Department of Psychology, University of Campania, Luigi Vanvitelli, 81100 Caserta, Italy;
2 Department of Neuroscience, Imaging and Clinical Sciences, Chieti-Pescara University, 66110 Chieti, Italy;