Sage Journals: Discover world-class research

Abstract

Prior literature has suggested that following exposure to family violence, adolescents can develop internalizing and externalizing symptoms, including bullying perpetration and depressive symptoms. However, incongruities suggest that exposure to family violence is not a consistent precipitating event associated with bullying perpetration and depressive symptoms. Therefore, using latent growth models, the current study applied the intergenerational transmission of violence and emotional security theories to investigate the developmental co-occurrences of depressive symptoms and bullying perpetration and longitudinal effects of exposure to family violence on bullying perpetration and depressive symptoms across four time points in middle school (i.e., from grades 6 to 8; n = 1,585; Mage = 11.23 in grade 6; SD = .45). Consistent with expectations, results of the latent growth models indicated that exposure to family violence was positively associated with adolescents’ concurrent bullying perpetration and depressive symptoms. These effects were evident across the middle school years. Implications are discussed.

Keywords

exposure to family violence bullying behavior depressive symptoms adolescents

The adolescent developmental period is a critical time in which youth often develop externalizing and internalizing symptoms even if they did not display such patterns in childhood (Achenbach et al., 2016; Fleming & Offord, 1990; Moffitt, 1993). Externalizing symptoms include aggressive conduct such as bullying perpetration, which refers to repetitive intentionally aggressive behavior characterized by threatening to harm someone, and acts of physical aggression such as kicking, slapping, and punching (Bonanno & Hymel, 2013; Olweus et al., 2019; Smith et al., 2002). In contrast, internalizing symptoms include depression, mood disturbances, concurrent feelings of sadness, withdrawal, and lack of energy, as well as suicidal ideation, and sleep disturbances (Achenbach et al., 1987; Auerbach et al., 2011; Hughes & Gullone, 2010). Although bullying perpetration and depressive symptoms are typically viewed as distinct behavioral manifestations, reflecting unique disturbances in adolescents social and emotional development, they may also co-occur during the early adolescence developmental period (e.g., ages 10–13), and subsequently exacerbate and influence adverse outcomes including academic difficulties, loneliness, juvenile delinquency, social withdrawal, and substance use (Arango et al., 2016; Katz et al., 2011; Lahtinen et al., 2020; Willner et al., 2016).

Despite substantial individual differences in rates of bullying perpetration and depressive symptoms among adolescents, researchers have been interested in investigating normative developmental trends of depressive symptoms and bullying perpetration in early adolescence, as they may be related to typical emotional and physiological changes during this period (Reinke et al., 2012). For instance, Prince et al. (2023) found that normative hormonal changes and fluctuating emotional maladjustment to daily stressors may contribute to the development of increasing levels of depressive symptoms (e.g., chronic sadness, fatigue, and insomnia). There is also evidence that suggests during the early adolescent period, youth undergo cognitive changes and seek peer approval for identity formation, which are often connected to emotional disturbances and eventual depressive symptoms (Hamilton et al., 2014; Prinstein & Aikins, 2004). Pertaining to bullying perpetration, literature has highlighted that bullying perpetration often peaks during early adolescence due to attempts to establish a high peer status (Pellegrini & Long, 2002), fear of rejection (Boulton, 2013), and self-control difficulties (Kretschmer et al., 2017). These patterns can be considered normative and an adaptive mechanism to enhance status with peers who witness bullying and, unfortunately, often reinforced by peers in school settings (Salmivalli et al., 2011). Although these patterns appear to be typical in early adolescence, prior literature has noted that depressive symptoms and bullying perpetration tend to subside over time (Ellis et al., 2017; Espelage et al., 2018).

Prior studies have highlighted the potential co-occurrence of depressive symptoms and engaging in bullying perpetration (Lawrence et al., 2025; Siennick & Turanovic, 2024). Considering their co-occurrence, it may be informative from an etiological perspective to identify potential common risk factors. Consistent with principles of multifinality such that one risk factor may lead to multiple distinct outcomes (Cicchetti & Rogosch, 1996); a common risk factor is considered here to be one that may be predictive of both bullying perpetration and depressive symptoms. Scholars have suggested that environmental stressors, including adverse home (e.g., neglect, parental incarceration, rejecting and neglecting parenting styles) and school (e.g., bullying victimization and peer rejection) experiences may be common risk factors associated with the co-occurring development of adolescents internalizing and externalizing behaviors (Holfeld & Mishna, 2019; Jung et al., 2017; Shi et al., 2020). While the etiology of bullying perpetration and depressive symptoms are multifaceted, in the current study, we examined whether exposure to family violence, which refers to witnessing or hearing violent acts and behaviors within the home, may be a robust predictor of both outcomes (Hawkins et al., 2019; Lawrence, 2022b). During the transition to adolescence, not only are early adolescents facing biological changes associated with pubertal development, they are also encountering social and academic challenges as they are transitioning to a new middle school environment in which they are exposed to a more departmentalized classroom structure, and larger peer groups which are also becoming more influential. Family dynamics may be particularly important during this transitional period, as adolescents are navigating potentially unfamiliar and stressful, peer and school contexts.

Although having a secure family environment may be an important resource for adolescents to navigate these complex changes, exposure to family violence taxes adolescents’ emotional coping abilities and disrupts the sense of safety within the home (Chester & Joscelyne, 2021). These stressors subsequently elicit emotions such as anger and fear and create a hypervigilance schema that guides the ways in which adolescents navigate their social and interpersonal relationships (Lawrence, Hong, Sopchak, & Voisin, 2023; Moses & Villodas, 2017; Reyes et al., 2015). Thus, exposure to family violence may create a context of stress and insecurity which may exacerbate adolescents’ maladjustment and contribute to increasing rates of bullying perpetration and depressive symptoms across the middle school grades.

Theoretical Framework

According to the Intergenerational Transmission of Violence (ITV) model, following exposure to family violence, children and adolescents develop aggressive scripts that shape and guide subsequent behavior in unrelated situations (Black et al., 2010; Widom, 1989; Widom & Wilson, 2015). Due to the socially reinforcing tenements that underlie this model, existing scholarship often explains this framework through the social learning model, which suggests that while individuals are exposed to violence, they learn that aggressive behavior is an acceptable tactic in resolving conflict (Chapple, 2003; Dunlap et al., 2002; Mihalic & Elliott, 1997). There is widespread support for the ITV model (Carr & VanDeusen, 2002; Heyman & Slep, 2002; Milletch et al., 2010), such that adolescents’ exposure to family violence has been associated with aggressive behavior consistent with bullying perpetration (Baek et al., 2019; Grant et al., 2019). Although the ITV model has robust empirical support linking exposure to family violence to bullying perpetration, this model does not adequately explain the pathogenesis of internalizing symptoms. Therefore, to explain the link between exposure to family violence and depressive symptoms, we drew on the Emotional Security Theory (EST).

Under the tenements of the EST, familial discord, including exposure to family violence disrupts the sense of safety among children and adolescents. These adverse family experiences contribute to increased emotion dysregulation and elicit intense anger and fear that undermines the sense of security in the home, negatively impairing the parent-child relationship (Cummings & Davies, 1996). A substantial body of research has demonstrated that these psychologically distressing events are likely to increase the risk for adolescents to develop both externalizing and internalizing symptoms, (Alegre et al., 2014; Cummings et al., 2013; Cummings & Miller-Graff, 2015; Davies et al., 2016a, 2016b).

Despite the theoretical utility of these models in explaining developmental pathways of bullying perpetration and depressive symptoms following exposure to family violence, there are several limitations that warrant further exploration. That is, these models have typically examined the consequences of exposure to family violence among early adolescents separately, which presents as a potential conceptual limitation. It is possible these theoretical frameworks provide competing or alternative viewpoints on the underlying processes or mechanisms by which family violence may exert an effect. For instance, because the ITV primarily focuses on how adolescents may model aggressive behaviors after exposure to family violence, this perspective may not adequately explain how family violence exposure could also trigger internalizing disturbances (e.g., depressive symptoms).

Applying this perspective, it could be the case that family violence is likely to have a stronger and more consistent effect on adolescents’ bullying perpetration compared to their depressive symptoms. Alternatively, when applying perspectives drawn from the EST, this model underscores how family violence exposure may also undermine adolescents’ affective regulation, which is likely to exacerbate their internalizing symptoms (e.g., depressive symptoms). Thus, in the current study we sought to simultaneously examine the effects of family violence on the co-occurring development of adolescent’s externalizing and internalizing behaviors to further assess whether its effects are potentially stronger on either of these outcomes, while controlling for their co-occurrence. This approach would allow us to make inferences in terms of whether the ITV model and EST provide complementary versus competing perspectives pertaining to the potential associations of family violence on changes in early adolescents’ internalizing and externalizing symptoms.

Empirical Evidence on the Associations of Family Violence With Adolescents’ Bullying Perpetration and Depressive Symptoms

As previously mentioned, literature has noted that exposure to family violence is a common risk factor for depressive symptoms and bullying perpetration. For example, Lawrence et al. (2022c) found that exposure to family violence positively predicted bullying perpetration. These results were further corroborated and extended by Gao et al. (2025), suggesting that exposure to family violence increases internal hostility and peer conflicts, which leads to externalizing conduct such as bullying perpetration. Furthermore, meta-analytic literature indicated that adolescents who were exposed to family violence were significantly more likely to engage in bullying perpetration than their non-exposed counterparts across various studies (Nocentini et al., 2019). Lee et al. (2022) found that across 12 studies, adolescents who were exposed to family violence were more likely to engage in bullying perpetration over time. In contrast, the current literature also suggests that exposure to family violence leads to emotional disturbances consistent with depressive symptoms (LaMontagne et al., 2023; Rivers et al., 2022). These studies were further supported by meta-analytic literature that shows exposure to family violence subsequently leads to a developmental pathway of depressive symptoms (Chen et al., 2023; Wang et al., 2025). More specifically, across fifty-three studies, adolescents who were exposed to some form of family violence, including witnessing physical fights and chronic screaming and yelling in the home, were more likely to endorse depressive symptoms (Whitten et al., 2024).

Overall, although the literature has highlighted the detrimental effects of exposure to family violence on subsequent depressive symptoms and bullying perpetration, there are inconsistencies in the literature. For instance, some previous studies suggest that exposure to family violence does not significantly predict bullying perpetration and depressive symptoms (Chen et al., 2017; Sternberg et al., 2006). Additionally, Knous et al. (2012) suggested that after controlling for prior internalizing and externalizing symptoms, exposure to family violence did not statistically predict bullying perpetration. Some attribute these inconsistencies to measurement differences (e.g., cross-sectional, and longitudinal designs; Ingram et al., 2020). In addition to these inconsistencies, one central gap exists in the literature. More specifically, few studies have investigated how the relative onset and growth rate of depressive symptoms and bullying perpetration in early adolescence may be associated with family violence exposure. This is an important gap to address because prior literature has shown that as early adolescents undergo psychological and physical changes, they are susceptible to engaging in bullying behavior and endorsing depressive symptoms (Iyer et al., 2013), and family violence exposure may function as a common risk factor which undermines their capacity to effectively handle these changes. Furthermore, given that adolescents’ bullying perpetration and depressive symptoms are likely to co-occur, it may be important to examine the simultaneous associations of family violence exposure on both aspects of adjustment.

Study Aims and Hypotheses

The current study aims to estimate the predictive effects of exposure to family violence on the onset and rate of change of bullying perpetration and depressive symptoms in early adolescence as youth were progressing through middle school. Towards this end, we undertook three interrelated objectives. First, we examined the normative development (trajectories) of adolescents’ bullying perpetration and depressive symptoms across the middle school years, grades 6 to 8. We hypothesized that both bullying perpetration and depressive symptoms would exhibit a normative increase in early adolescence as youth adjusted to middle school, and would subsequently subside during this period (i.e., by Grade 8). Second, we examined the co-occurrence of adolescents’ bullying perpetration and depressive symptoms during this time. More specifically, we hypothesized that initial levels of adolescents’ bullying perpetration and depressive symptoms (i.e., intercepts in Grade 6) would be positively correlated. We also expected that rates of growth (i.e., slope effects) would be positively correlated (i.e., adolescents who exhibited an increase in bullying perpetration over time would be more likely to have an increase in depressive symptoms). Third, we examined the predictive effects of family violence exposure on adolescents’ bullying perpetration and depressive symptoms. Consistent with the ITV and EST perspectives, we posited that higher levels of family violence exposure would be disruptive for adolescents’ adjustment across the middle school years and that these experiences would create persistent challenges during this time. Consequently, we hypothesized that family violence exposure would be positively associated with both initial levels and increasing rates of change (i.e., intercept and slope effects, respectively), of bullying perpetration and depressive symptoms.

Method

Participants

Data for the current study came from a program evaluation of the Second Step program conducted in two Midwestern states in the United States (Espelage et al., 2013). Students from 36 middle schools were recruited into the longitudinal study, and schools were matched and randomly assigned into treatment (intervention) and control schools. For the purposes of the current study, we used data from the 18 schools which were part of the control condition. The current study relied on secondary data analysis using publicly available data from Inter-university Consortium for Political and Social Research (ICPSR; https://www.icpsr.umich.edu/web/ICPSR/studies/36726). The publicly available dataset included 4,718 cases. This included 3,616 students who were recruited into the longitudinal study in Grade 6 (see Espelage et al., 2013), as well as additional children who were added to the study in subsequent grade levels over the course of the longitudinal project. For the purposes of the current study, and given the longitudinal design, we used data for students who were recruited into the study in Grade 6, and part of the control condition. More specifically, at the baseline assessment (Wave 1 conducted in Fall of Grade 6), there were 1,697 students who were identified as being in the control condition. Over the course of the longitudinal project, some students (n = 112) changed schools and were reassigned to the intervention condition, and these students were excluded from the current analyses due to having exposure to the intervention program, resulting in 1,585 students as the sample size for the current study (at the baseline assessment, Mage = 11.23; SD = .45). The sample was evenly distributed in terms of gender (51.2% boys and 48.8% girls), and consisted of 26.3% African American, 19.6% White, 38.2% Hispanic/Latino, 3.4% Asian American, and 12.4% identified as biracial or other.

Procedure

Parents of all students received a consent letter explaining the purpose and risk of the study. Youth whose guardians had not granted consent were removed from administration rooms once surveys were presented. Furthermore, for students who had received parental consent, an assent form was also signed and returned to trained assistants. School based data collection occurred over a period of three years, as students progressed through middle school (Grades 6 to 8). Data collection occurred across 4 waves including assessments conducted in Fall (Wave 1) and Spring (Wave 2) of Grade 6, Spring of Grade 7 (Wave 3) and Spring of Grade 8 (Wave 4). Prior publications with this current dataset (e.g., Espelage et al., 2018) appear to indicate that portions of this sample were also followed into high school (with three additional waves of data collection conducted); however, due to changes in the sampling design and availability of measures made after youth transitioned into high school, it was not possible in the current study to utilize data beyond the middle school years. Thus, the current study utilized waves 1 through 4 in which students completed several survey questionnaires with consistent (repeated) measures of adolescents’ bullying perpetration and depressive symptoms.

Measures

Exposure to Family Violence

Exposure to family violence was measured at Wave 1 using 3 items from the Family Conflict and Hostility Scale which were drawn from the Rochester Youth Development Study (Thornberry et al., 2003). This scale measured the degree to which adolescents’ perceived conflict and hostility in the household by assessing loss of temper, frequent arguing, and physical violence by family members at home. Items included, “How often are there physical fights in the household?”, “How often is there yelling, quarreling, or arguing in your household?”, and “How often do family members lose their temper or blow up for no good reason?”. Responses were scored on a 5-point Likert scale ranging from 0 (never) to 4 (always), and this measure exhibited adequate internal reliability within the current sample (α = 79).

Depressive Symptoms

Depressive symptoms were measured in Waves 1 to 4, using nine items from the Orpinas Modified Depression scale (Orpinas, 1993). This scale measured the frequency of various depressive symptoms including irritability, sadness, worrying, hopelessness, and nervousness within the past 30 days. Example items included, “Did you feel hopeless about the future?”, “Did you worry a lot?”, Were you very sad?”, and “Did you feel nervous or afraid that things won’t work out the way you would like them to?”. Items were scored on a 5-point Likert scale ranging from 0 (never) to 4 (almost always), with higher scores reflecting more depressive symptoms. This measure exhibited adequate internal reliability within the current sample (αs ranged from .81 to 86 in Waves 1 to 4).

Bullying Perpetration

Bullying perpetration was measured in Waves 1 to 4 using 7 items from the Illinois Bully Scale (Espelage & Holt, 2001). This measure assessed how often in the past 30 days students engaged in various forms of physical, relational and verbal bullying. Example items included, “I spread rumors about other students”, “I excluded other students from my clique (group) of friends”, “I helped harass other students”, and “I threatened to hurt or hit another student.” Items were scored on a 5-point Likert scale ranging from 0 (never) to 4 (seven or more times). Prior literature has supported the construct validity of this instrument, which was strongly correlated with peer nominations of bullying perpetration (Espelage et al., 2003). This measure exhibited adequate internal reliability within the current sample (αs ranged from .79 to .83 in Waves 1 to 4).

Data Analysis Plan

First, preliminary analyses were conducted in SPSS (version 28) to assess patterns of missing data, descriptive statistics, and bivariate correlations. Second, latent growth curve modeling was conducted in Mplus (version 8) to examine the longitudinal associations of family violence on adolescents’ bullying perpetration and depressive symptoms. To handle missing data, full information maximum likelihood (FIML) estimation was used which includes all cases with available data, and the maximum likelihood robust (MLR) estimator was used which accounts for non-normality when estimating standard errors. Model fit was assessed using the recommendations of Hu and Bentler (1999), such that models were deemed to have adequate fit if the RMSEA <.06, SRMR <.08, and CFI >.95.

Growth modeling was conducted in two parts. First, unconditional latent growth models were specified for bullying perpetration and depression to assess the growth patterns for each construct separately, across Waves 1 to 4. For each construct, a series of linear and non-linear (quadratic) growth models were specified, and nested model tests were used to compare models. Given the use of MLR, nested model comparisons consisted of Satorra-Bentler adjusted chi-square difference tests (Satorra, 2000). After determining the optimal growth pattern for each construct, a parallel process growth model was estimated for bullying perpetration and depressive symptoms simultaneously, across Waves 1 to 4. The latent growth parameters included estimates for the intercept factor, which reflected baseline (Wave 1) scores for bullying perpetration and depressive symptoms, and slope and quadratic factors which estimated linear and non-linear changes in bullying perpetration and depressive symptoms from Wave 1 to 4. These growth parameters were correlated with each other to account for co-occurring changes in bullying perpetration and depression over time. Family violence was specified as a predictor (exogenous variable) and included path estimates to the growth parameters for bullying perpetration and depressive symptoms. Race, age and gender were included as control variables predicting the growth factors and correlated with family violence. An example of the complete path model based on the parallel process growth model is illustrated in Figure 1.

Figure 1.

Illustrative example of conditional growth model examining associations of family violence on adolescents’ bullying perpetration and depressive symptoms trajectories

Results

Preliminary Analyses (Missing Data, Descriptive Statistics, and Bivariate Correlations)

Missing data analyses were performed to assess patterns of attrition over the course of the longitudinal project. Results indicated that missing data increased over time, with an average of 4% missing data at Wave 1, 15% at Wave 2, 32% at Wave 3, and 42% at Wave 4. To assess whether there were observable patterns of attrition, a series of univariate t-tests were performed to examine whether rates of missing data differed by students’ gender, and ethnicity. Results indicated some significant differences across demographic groups, but the findings were inconsistent. More specifically, Hispanic students had significantly more missing data and Black students had significantly less missing data on the depression measure in Wave 1, but not on bullying perpetration. In Waves 2, 3, and 4, Hispanic students had significantly less missing data, and in Waves 3 and 4, Black students had significantly more missing data. Although these findings were inconsistent, to account for these demographic differences, race and ethnicity were included as covariates in all subsequent growth models.

Descriptive statistics (means, standard deviations, score ranges) and bivariate correlations are shown in Table 1. Observed means indicated an increase in depression over time (M = 1.19 in Wave 1 and M = 1.52 in Wave 4). Observed means for bullying perpetration appeared to be somewhat stable (ranging from M = .31, to .39), with a peak in wave 3. Bivariate correlations indicated that exposure to family violence was moderately positively correlated with adolescents’ depressive symptoms (r = .22 to .39; p < .01) and bullying perpetration (r = .14 to .24; p < .01) across all waves. Depressive symptoms and bullying perpetration were also positively correlated (r = .16 to .25; p < .01 for within time correlations).

Table 1.

Descriptive Statistics and Bivariate Correlations Among the Study Variables

Measures	1	2	3	4	5	6	7	8	9
Bivariate correlations
1. Family violence (W1)
2. Depression (W1)	.386^**
3. Depression (W2)	.319^**	.509^**
4. Depression (W3)	.219^**	.413^**	.503^**
5. Depression (W4)	.248^**	.390^**	.456^**	.569^**
6. Bullying perpetration (W1)	.209^**	.157^**	.202^**	.043	.082^*
7. Bullying perpetration (W2)	.236^**	.185^**	.245^**	.149^**	.103^**	.440^**
8. Bullying perpetration (W3)	.141^**	.148^**	.202^**	.188^**	.099^**	.356^**	.520^**
9. Bullying perpetration (W4)	.177^**	.169^**	.210^**	.229^**	.195^**	.288^**	.341^**	.417^**
Descriptive statistics
N	1,467	1,526	1,346	1,081	919	1,575	1,363	1,083	923
Min	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Max	4.00	4.00	4.00	4.00	4.00	4.00	4.00	4.00	4.00
Mean	0.78	1.19	1.44	1.53	1.52	0.31	0.36	0.39	0.32
Standard deviation (SD)	0.88	0.79	0.79	0.80	0.84	0.51	0.53	0.56	0.48

*p < .05; **p < .01.

W = wave.

Unconditional Latent Growth Model Results

Unconditional growth models were specified for bullying perpetration and depressive symptoms separately. For bullying perpetration, the model comparisons indicated that the non-linear growth model including a quadratic effect had adequate overall model fit (χ² = 2.60, df = 1, p = .11; RMSEA = .03, SRMR = .01, CFI = .995), and significantly better fit compared to the linear growth model (χ² = 39.46, df = 5, p < .001; RMSEA = .07, SRMR = .05, CFI = .884; scaled Δχ² = 34.23, df = 4, p < .001). Considering the mean estimates for the growth parameters, (M_intercept = .30, p < .001; M_slope = .13, p < .001; M_quadratic = −.04, p < .001), it appeared that bullying perpetration exhibited a significant increase over time with a sharper increase in earlier waves, peaked in Wave 3 (Grade 7), and then had a slow declining effect by the final wave (see Figure 2(a)).

Figure 2.

Unconditional growth models examining adolescents’ bullying perpetration (a) and depressive (b) trajectories

For depression, the model comparisons indicated that the non-linear growth model including a quadratic effect had adequate overall model fit (χ² = 3.11, df = 1, p = .08; RMSEA = .04, SRMR = .01, CFI = .997), and significantly better fit compared to the linear growth model (χ² = 67.87, df = 5, p < .001; RMSEA = .09, SRMR = .05, CFI = .916; scaled Δχ² = 62.47, df = 4, p < .001). Considering the mean estimates for the growth parameters, (M_intercept = 1.20, p < .001; M_slope = .36, p < .001; M_quadratic = −.09, p < .001), it appeared that adolescents’ depressive symptoms exhibited a significant increase over time (from Waves 1 to 4), however the rate of change varied over time with a sharper increase in earlier waves, followed by a tapering effect in later waves (see Figure 2(b)).

Parallel Process Latent Growth Model Results

Based on the unconditional growth modeling results, the non-linear (quadratic) models for both bullying perpetration and depression were included in the parallel process growth model that was subsequently estimated. Model results indicated that the parallel process latent growth model exhibited adequate model fit (χ² = 43.30, df = 24, p < .01; RMSEA = .02, SRMR = .01, CFI = .99). To more closely evaluate the model results, we examined the effects of family violence on the bullying perpetration and depression growth parameters, the covariate effects of age, race, ethnicity and gender on the growth parameters, and the covariances (correlations) among the growth parameters.

The model results for the effects of family violence and the covariates on the bullying perpetration and depressive symptoms growth parameters are presented in Table 2. Results indicated that family violence had a significant effect on the bullying perpetration intercept factor (b_intercept = .12, p < .001), and depressive symptoms growth factors (b_intercept = .35, p < .001; b_slope = −.14, p < .001; b_quadratic = .03, p = .02). To facilitate the interpretation of these effects, we estimated plots for the conditional effects of high (+1 standard deviation, SD), moderate (mean level) and low family violence (−1 SD) on adolescents’ bullying perpetration (Figure 3(a)), and depressive symptoms trajectories (Figure 3(b)). For bullying perpetration, the results indicated that higher family violence was associated with more elevated baseline levels beginning at the start of middle school, and these effects generally persisted over time. For depressive symptoms, the results indicated that higher family violence was associated with more elevated baseline (intercept) levels, but gradually over time, its effects were somewhat attenuated (as indicated by the significant growth parameters).

Table 2.

Effects of Family Violence and Control Variables on Adolescents’ Bullying Perpetration and Depressive Trajectories

Effects	Intercept				Linear slope				Quadratic slope
Effects	b	SE	p	B	b	SE	p	B	b	SE	p	B
Bullying perpetration
Family violence	0.12	0.02	.000	0.33	0.01	0.03	.834	0.02	−0.01	0.01	.500	−0.05
Age	0.16	0.03	.000	0.22	−0.14	0.05	.006	−0.20	0.04	0.02	.056	0.12
African American	0.25	0.04	.000	0.34	−0.04	0.07	.565	−0.05	0.01	0.02	.551	0.05
Hispanic/Latino	0.09	0.03	.001	0.14	0.01	0.05	.839	0.02	−0.02	0.02	.322	−0.06
Asian	−0.06	0.04	.089	−0.04	0.00	0.11	.982	0.00	0.03	0.05	.495	0.04
Biracial	0.11	0.04	.011	0.11	0.06	0.07	.424	0.06	−0.01	0.03	.584	−0.04
Gender (girls)	−0.06	0.02	.021	−0.09	−0.03	0.04	.418	−0.05	0.01	0.02	.456	0.04
Depression
Family violence	0.35	0.03	.000	0.52	−0.14	0.04	.000	−0.37	0.03	0.01	.022	0.30
Age	0.02	0.04	.720	0.01	0.05	0.07	.421	0.07	−0.02	0.02	.366	−0.11
African American	0.01	0.06	.828	0.01	−0.06	0.09	.487	−0.08	0.00	0.03	.967	0.01
Hispanic/Latino	0.09	0.05	.070	0.08	−0.07	0.08	.357	−0.10	0.00	0.03	.918	0.01
Asian	0.04	0.10	.721	0.01	0.06	0.16	.716	0.03	−0.05	0.06	.441	−0.09
Biracial	0.01	0.07	.859	0.01	0.15	0.10	.147	0.15	−0.07	0.04	.064	−0.24
Gender (girls)	0.18	0.04	.000	0.15	0.14	0.06	.016	0.21	0.01	0.02	.656	0.05

Note. Estimates included unstandardized (b) and standardized (B) effects.

Figure 3.

Conditional growth models examining effects of family violence on adolescents’ bullying perpetration (a) and depressive (b) trajectories

With respect to the covariate effects (see Table 2), the model results for bullying perpetration indicated a significant gender effect, such that girls had significantly lower baseline levels than boys. The significant intercept effects indicated that age was positively associated with higher baseline levels of bullying, and race and ethnicity effects indicated that Black/African American, Hispanic/Latino and biracial youth had higher baseline levels of bullying perpetration compared to White youth. The model results for depressive symptoms indicated a significant gender effect, such that girls had significantly higher intercept and slope effects than boys. Covariate effects for race, ethnicity and age were not significantly associated with depressive symptoms.

With respect to the correlations among the growth factors, the model results indicated that the latent intercept factors for bullying perpetration and depressive symptoms were positively correlated with each other (r = 23, p = .01). The intercept factors for depression and bullying perpetration were not significantly correlated with the slope and quadratic factors.

Discussion

The present study estimated the longitudinal onset and growth, and the co-occurrence, of bullying perpetration and depressive symptoms while applying the ITV and EST models to understand the pernicious effects of exposure to family violence on the developmental trajectories of depressive symptoms and bullying perpetration across four-time points. Our findings provided novel information and advanced the literature by highlighting normative trends in the developmental onset and growth rate of bullying perpetration and depressive symptoms in early adolescence and underscoring the detrimental effects of exposure to family violence during this developmental period. More specifically, youth with higher levels of family violence exposure in grade 6 exhibited higher concurrent levels of bullying perpetration and depressive symptoms, and these deleterious effects persisted across the middle school years (through grade 8).

Development and Co-Occurrence of Bullying Perpetration and Depressive Symptoms

Our first aim was to investigate the normative developmental growth patterns of bullying perpetration and depressive symptoms as adolescents progressed through middle school. The results were mostly consistent with our hypotheses regarding normative developmental trends but also underscored the differential developmental trajectories of bullying perpetration and depressive symptoms among early adolescents. More specifically, with respect to bullying perpetration, adolescents exhibited a significant increase during the early stages of measurement (i.e., from grades 6 to 7), with a peak in bullying perpetration in Grade 7, followed by a declining pattern thereafter (from grades 7 to 8). With respect to depressive symptoms, adolescents exhibited a significant increase during the early stages of measurement (i.e., from grades 6 to 7), followed by a tapering effect in which it appeared that rates of depression stabilized. These findings support the notion that during the early adolescent developmental period, there is a normative onset of both bullying perpetration and depressive symptoms in that both increased and declined or stabilized thereafter (Ellis et al., 2017; Espelage et al., 2018). These findings highlight how the simultaneous developmental transition to adolescence and contextual transition to middle school may serve as an important period in which there are significant shifts in adolescents’ behavioral and emotional adjustment. Although the biological and social changes that are occurring during this time can be viewed as being normative, they may also present challenges and introduce stressors to adolescents that may compromise their healthy adaptation. Toward this end, prior literature has shown that during the transition to middle school, adolescents often experience stress related to adjusting to the new environment and may experience anxiety about their social status among peers, which could lead to bullying behaviors (Shell et al., 2014; Wei & Chen, 2012).

Expanding on these normative developmental patterns, the results also revealed a positive correlation between the bullying perpetration and depressive symptoms intercept factors. This finding indicated that bullying preparation and depressive symptoms were co-occurring among adolescents, and that adolescents who were engaging in higher levels of bullying perpetration were also exhibiting higher levels of depressive symptoms, relative to other youth. Unlike prior studies that have separately examined bullying perpetration and depressive symptoms, these results advance previous findings by suggesting that these two particular forms of externalizing (bullying perpetration) and internalizing behaviors (depressive symptoms) are likely to co-occur as youth are transitioning into middle school. Notably, within the parallel process growth models, this correlation reflected a residual correlation, after accounting for the effects of family violence and other potential confounding demographic effects including gender, age, race, and ethnicity. This suggests that the co-occurrence of bullying perpetration and depressive symptoms was beyond what could be accounted for by family violence. On the one hand, it is possible that the social-contextual changes that are occurring during this period may exacerbate adolescents’ adjustment across multiple developmental outcomes. That is, adolescents who are having challenges adapting to middle school are likely to exhibit a combination of emotional disturbances characterized as irritability and psychomotor agitation and bullying perpetration behavior, such as name calling and physical violence. On the other hand, it is possible that there are underlying biological or dispositional factors (e.g., a general psychopathology or p factor) that are associated with the co-occurrence of multiple forms of externalizing and internalizing factors (Allegrini et al., 2020).

Exposure to Family Violence and Effects on Bullying Perpetration and Depressive Symptoms

We predicted that, following exposure to family violence, adolescents would be more likely to endorse depressive symptoms and engage in bullying perpetration. In general, the findings provided support for this assertion but also revealed insights with respect to the timing of these associations (i.e., differential effects on the growth parameters). More specifically, higher levels of family violence as adolescents were beginning middle school were associated with more elevated rates of bullying perpetration and depressive symptoms, which then persisted over the course of middle school. There are several possible theoretical and translational explanations for these associations. Theoretically, this result is consistent with the ITV model, which suggests that individuals who are exposed to family violence learn that violence and aggressive behavior are appropriate methods of handling conflict, which increases their chances of engaging in aggressive behavior (Black et al., 2010; Lawrence, 2022a; Tracy et al., 2018).

Consistent with the EST, these findings also indicated that exposure to family violence could have undermined the sense of safety and emotion regulation that creates the vulnerability for the onset of depressive symptoms. Translationally, these results highlight that as adolescents were exposed to high levels of family violence, they were more likely to engage in aggressive acts characterized as bullying perpetration at school while also endorsing depressive symptoms. Consistent with these findings, it is plausible that exposure to traumatic events (such as family violence) could impede self-regulatory practices that are necessary when navigating and maintaining interpersonal relationships (Evans et al., 2015; Kim et al., 2021; Lawrence, Hong, Espelage, & Voisin, 2023), and ineffective coping strategies and prolonged affective disturbances, which is consistent with prior studies (Espelage et al., 2014; Grant et al., 2019). Because adolescents who were exposed to family violence may exhibit increased hypervigilance, they may also misinterpret ambiguous situations and have difficulty recognizing emotional expressions (McCrory et al., 2011). Taken together, these findings highlight how exposure to family violence may be particularly detrimental for adolescents as it may compromise multiple developmental processes pertaining to adolescents’ self-regulatory mechanisms, coping strategies, attributional styles, and emotional security and functioning, which collectively appear to exacerbate their co-occurring externalizing and internalizing behaviors.

Contrary to expectations, the effects of family violence on the bullying perpetration growth parameters were not significant, and family violence was negatively associated with the depressive symptoms linear slope parameter (although it was positively associated with the quadratic factor). These findings indicated that family violence was more consistently associated with adolescents’ initial levels of bullying perpetration and depressive symptoms but was not associated with increasing symptomology. Although the reasoning for these non-significant findings are unclear, we consider several possible explanations. These findings may speak to the importance of assessing family violence over time by including repeated (i.e., time varying) assessments. To the extent that there are fluctuations or changes in adolescents’ family violence exposure as they progress through middle school, it is possible that the effects of family violence exposure may have diminished, or not persisted as strongly, over time. Expanding on this point, the transition to early adolescence, and middle school, is a period in which early adolescents are likely to experience multiple contextual stressors which extend beyond the family structure (e.g., larger and more bureaucratic school structures; multiple teachers which may reduce their teacher-student relationship quality; changes in their peer groups and social hierarchies which may contribute to increasing peer victimization), and how adolescents’ adapt to these changes are likely to contribute to changes (e.g., increases) in their depressive symptoms and bullying perpetration. An interesting direction for future research may be to examine its potential moderating effects to assess whether family violence exposure exacerbates other contextual stressors that adolescents may encounter during this time.

Study Strengths and Limitations

Among the strengths of the current study were a diverse sample and multi-year (4 wave) longitudinal examination of the co-occurring developmental trajectories of depressive symptoms and bullying perpetration in early adolescence. This approach has several benefits over cross-sectional studies and provides a more nuanced lens for better understanding the effects of exposure to family violence on adolescent internalization and externalization of disturbances. Another strength is that this study supported the robustness of the ITV and EST models that could promote future studies to further examine the utility of these models using complex methods and diverse samples. Lastly, a notable strength is that our findings underscored the co-occurrence of bullying perpetration and depressive symptoms and the conditional effects of exposure to family violence on subsequent maladaptive adolescent externalizing and internalizing symptoms.

Despite these strengths, there were also several limitations. First, this study utilized data from self-reported measures, which could serve as a limitation because adolescents may omit relevant details that could be mitigated by gathering information from multiple sources, including parents, teachers, and peers. Therefore, future studies should include other sources of information, which could reduce threats to validity. Second, this study only measured two outcomes (i.e., depressive symptoms and bullying perpetration) of exposure to family violence. Because there are several consequences of exposure to family violence that were not included in this study, future research should include relevant internalizing and externalizing consequences of exposure to family violence, using standardized measures such as The Multidimensional Anxiety Scale for Children (March et al., 1997) Proactive and Reactive Aggression Questionnaire (Raine et al., 2006) and the Problem Behavior Frequency Scale (Farrell et al., 2016) amongst others.

Third, prior literature has shown that the typology of bullying perpetration often includes cyberbullying, which was not included in the current study (McField et al., 2023). Thus, future studies should incorporate additional forms of bullying perpetration when assessing its onset and growth. Fourth, prior literature has shown that the occurrence of family violence often co-occurs with parental psychopathology and influences adolescents’ internalizing and externalizing behavior (Ehrensaft & Cohen, 2012). However, the current study only estimated the effects of exposure to family violence, but not parental psychopathology, on the onset of internalizing and externalizing symptoms. Future studies could address this limitation by robustly estimating how family violence and various parental psychopathologies (e.g., anxiety, depression, antisocial behavior) impact adolescent maladaptive functioning. Lastly, prior literature has suggested that bullying victimization often associates with adolescent internalizing and externalizing symptoms (Lawrence, 2023a, 2023b), which was not included in the current study. Therefore, future studies can address this limitation by measuring whether prior bullying experiences could lead to bullying perpetration and depressive symptoms over time.

Practice Implications

This study provided preventive and therapeutic implications. The occurrence of family violence is often interwoven with parental psychopathology, environmental strain, substance use, and overall dysfunctional family patterns (Briggs-Gowan et al., 2010; Ehrensaft & Cohen, 2012). To disrupt this pattern, legislative efforts and financial support should be allocated to providing community mental health services aimed at preventing family violence in socioeconomically disadvantaged communities that are cost-efficient and gender-specific (Chan et al., 2009). Communities that have utilized evidence-based models, such as the Duluth and Domestic Abuse Intervention Project (DDAIP) have reported reduced family violence and the occurrence of domestic violence among families (Feinberg et al., 2016).

Practically, because the onset of family violence could lead to a strained parent-child relationship, which could subsequently associate with child-parent aggression, including verbal (e.g., yelling at parents) and physical aggression (e.g., slapping, kicking, punching), family clinicians could disrupt these patterns by using evidence-based family interventions including Solution Focused Family Therapy. Such therapeutic interventions consist of incorporating core values into desired outcomes to form realistic solutions to problems (Beyebach, 2009). Prior evidence has shown that families who participated in Solution Focused Family Therapy reported fewer child-parent aggression, and families felt empowered to resolve conflict using healthy communication strategies (Powell & Ladd, 2010).

Situations in which individual therapy is an appropriate approach compared to family therapy may encourage clinicians to use evidence-based interventions such as Cognitive Behavioral Therapy (CBT), to identify distorted thinking patterns that often lead to emotional dysregulation, which could lead to adolescent antisocial behavior (Persampiere et al., 2014). Additionally, CBT interventions provide guidance and psychoeducation on the overlap between thoughts and behaviors to help develop healthy coping strategies, such as reappraisal of emotions, problem-solving techniques, and seeking support from others (Simpson et al., 2012). The utility of CBT in schools is also evident. For instance, clinicians report reductions in bullying preparation and overall hostility among students who participated in CBT brief interventions, which utilize challenging distorted thinking patterns that lead to aggressive behavior (Powell et al., 2011). Lastly, because exposure to family violence is strongly correlated with the development of traumatic symptoms and dysfunctional sibling relationships (i.e., sibling aggression; Lawrence, Hong, Espelage, & Voisin, 2023; Saleem et al., 2020; Vickerman & Margolin, 2007), clinicians can use Cognitive Processing Therapy (CPT) to address trauma symptoms by specifically encouraging clients to write about their worst traumatic event while utilizing affective coping strategies to attenuate intense emotions (Iverson et al., 2011; Vogel & Rosner, 2020). Studies have shown the efficacy of using CPT among adolescents who endorse traumatic symptoms (Ahrens & Rexford, 2002; LoSavio et al., 2021).

Conclusion

Overall, the current findings contribute to the current literature by demonstrating that bullying perpetration and depressive symptoms increased in early adolescence after adolescents transitioned to middle school and peaked in Grade 7. Bullying perpetration subsequently declined, and depressive symptoms stabilized thereafter. Findings indicated that bullying perpetration and depressive symptoms are likely to co-occur during this developmental period, and that this co-occurrence is associated with higher levels of exposure to family violence. Exposure to family violence appeared to be a common risk factor which was significantly associated with both bullying perpetration and depressive symptoms, and youth who experienced higher family violence exposure in Grade 6 were more likely to exhibit persistently elevated rates of bullying perpetration and depressive symptoms across the middle school years (i.e., Grades 6 to 8). These findings highlight the importance of preventive interventions in thwarting family violence while providing evidence-based interventions that are effective in addressing the onset and co-occurrence of bullying perpetration and depressive symptoms.

Footnotes

ORCID iD

Timothy I. Lawrence

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

IRB Approval

The data are publicly available. The study utilized publicly available data with no identifiers and was exempted from the Institutional Review Board oversights.

Author Biographies

Timothy I. Lawrence received his PhD in clinical adolescent psychology from Prairie View A&M University. He is currently an assistant professor at Texas A&M University with research interests including antisocial behavior (aggressive behavior, rule-breaking conduct, substance use), developmental psychopathology, emotions, family sciences, offender rehabilitation, and psych-law decision-making. Lawrence conceptualized the initial draft and initial analysis and prepared the first and final draft of the manuscript.

Idean Ettekal received his PhD in family and human development from Arizona State University. He is currently an associate professor at Texas A&M University with research interests including children’s and adolescents’ social and emotional development and the impact of their interpersonal relationships and experiences in different social contexts. His work has been primarily interested in examining the development of antisocial and externalizing behaviors such as aggression, bullying, rule-breaking, and youth violence. Idean significantly contributed to data reanalysis and editing of the final version of the manuscript.

Stephany Pinales received her MA in school psychology from the University of Texas RGV. She is currently a doctoral student at Texas A&M University school psychology program. Her research interests include developmental psychopathology, culturally and linguistically diverse populations, and school-based interventions. Stephany contributed to the final preparation of the manuscript.

Stephen Watson received his BA from Taylor University. He is a counseling psychology doctoral student at Texas A&M University. He is currently in his second year with research interests including first responder and military populations, rehabilitation psychology, and offender rehabilitation. Watson assisted in the editing of this manuscript.

Melissa Tolentino received her BA in psychology and Spanish from Kalamazoo College. She is currently a counseling psychology doctoral student at Texas A&M University with research interests including Latinx youth, immigration adaptation processes, imposter phenomenon, ethno-racial trauma, and cultural humility. Tolentino edited the in-text citations and reference section.

Alejandro Flores MA is a doctoral student within the counseling psychology program at Texas A&M. Originally from Laredo, Texas, Alejandro completed his undergraduate degree in psychology and his master’s in counseling psychology at Texas A&M International University (TAMIU). His research interests include a focus on multiculturalism, culturally informed therapies, and personal growth initiative. Alejandro edited the discussion section.

References

Achenbach

T. M.

Edelbrock

Howell

C. T.

(1987). Empirically based assessment of the behavioral/emotional problems of 2-and 3-year-old children. Journal of Abnormal Child Psychology, 15(4), 629–650. https://doi.org/10.1007/BF00917246

Achenbach

T. M.

Ivanova

M. Y.

Rescorla

L. A.

Turner

L. V.

Althoff

R. R.

(2016). Internalizing/externalizing problems: Review and recommendations for clinical and research applications. Journal of the American Academy of Child & Adolescent Psychiatry, 55(8), 647–656. https://doi.org/10.1016/j.jaac.2016.05.012

Ahrens

Rexford

(2002). Cognitive processing therapy for incarcerated adolescents with PTSD. Journal of Aggression, Maltreatment & Trauma, 6(1), 201–216. https://doi.org/10.1300/J146v06n01_10

Alegre

Benson

M. J.

Pérez-Escoda

(2014). Maternal warmth and early adolescents’ internalizing symptoms and externalizing behavior: Mediation via emotional insecurity. The Journal of Early Adolescence, 34(6), 712–735. https://doi.org/10.1177/0272431613501408

Allegrini

A. G.

Cheesman

Rimfeld

Selzam

Pingault

J. B.

Eley

T. C.

Plomin

(2020). The p factor: Genetic analyses support a general dimension of psychopathology in childhood and adolescence. The Journal of Child Psychology and Psychiatry and Allied Disciplines, 61(1), 30–39. https://doi.org/10.1111/jcpp.13113

Arango

Opperman

K. J.

Gipson

P. Y.

King

C. A.

(2016). Suicidal ideation and suicide attempts among youth who report bully victimization, bully perpetration and/or low social connectedness. Journal of Adolescence, 51, 19–29. https://doi.org/10.1016/j.adolescence.2016.05.003

Auerbach

R. P.

Bigda-Peyton

J. S.

Eberhart

N. K.

Webb

C. A.

M. H.

(2011). Conceptualizing the prospective relationship between social support, stress, and depressive symptoms among adolescents. Journal of Abnormal Child Psychology, 39(4), 475–487. https://doi.org/10.1007/s10802-010-9479-x

Baek

Roberts

A. M.

Seepersad

Swartz

(2019). Examining negative emotions as mediators between exposures to family violence and bullying: A gendered perspective. Journal of School Violence, 18(3), 440–454. https://doi.org/10.1080/15388220.2018.1519441

Beyebach

(2009). Integrative brief solution-focused family therapy: A provisional roadmap. Journal of Systemic Therapies, 28(3), 18–35. https://doi.org/10.1521/jsyt.2009.28.3.18

10.

Black

D. S.

Sussman

Unger

J. B.

(2010). A further look at the intergenerational transmission of violence: Witnessing interparental violence in emerging adulthood. Journal of Interpersonal Violence, 25(6), 1022–1042. https://doi.org/10.1177/0886260509340539

11.

Bonanno

R. A.

Hymel

(2013). Cyber bullying and internalizing difficulties: Above and beyond the impact of traditional forms of bullying. Journal of Youth and Adolescence, 42(5), 685–697. https://doi.org/10.1007/s10964-013-9937-1

12.

Boulton

M. J.

(2013). The effects of victim of bullying reputation on adolescents’ choice of friends: Mediation by fear of becoming a victim of bullying, moderation by victim status, and implications for befriending interventions. Journal of Experimental Child Psychology, 114(1), 146–160. https://doi.org/10.1016/j.jecp.2012.05.001

13.

Briggs‐Gowan

M. J.

Carter

A. S.

Clark

Augustyn

McCarthy

K. J.

Ford

J. D.

(2010). Exposure to potentially traumatic events in early childhood: Differential links to emergent psychopathology. Journal of Child Psychology and Psychiatry, 51(10), 1132–1140. https://doi.org/10.1111/j.1469-7610.2010.02256.x

14.

Carr

J. L.

VanDeusen

K. M.

(2002). The relationship between family of origin violence and dating violence in college men. Journal of Interpersonal Violence, 17(6), 630–646. https://doi.org/10.1177/0886260502017006003

15.

Chan

Y. C.

Lam

G. L.

Cheng

H. C.

(2009). Community capacity building as a strategy of family violence prevention in a problem-stricken community: A theoretical formulation. Journal of Family Violence, 24(8), 559–568. https://doi.org/10.1007/s10896-009-9254-3

16.

Chapple

C. L.

(2003). Examining intergenerational violence: Violent role modeling or weak parental controls? Violence & Victims, 18(2), 143–162. https://doi.org/10.1891/vivi.2003.18.2.143

17.

Chen

Song

Huang

(2023). The interactive effects of family violence and peer support on adolescent depressive symptoms: The mediating role of cognitive vulnerabilities. Journal of Affective Disorders, 323, 524–533. https://doi.org/10.1016/j.jad.2022.11.080

18.

Chen

W. Y.

Corvo

Lee

Hahm

H. C.

(2017). Longitudinal trajectory of adolescent exposure to community violence and depressive symptoms among adolescents and young adults: Understanding the effect of mental health service usage. Community Mental Health Journal, 53(1), 39–52. https://doi.org/10.1007/s10597-016-0031-5

19.

Chester

Joscelyne

(2021). “I Thought It Was Normal”: Adolescents’ attempts to make sense of their experiences of domestic violence in their families. Journal of Interpersonal Violence, 36(11-12), 5250–5276. https://doi.org/10.1177/0886260518802847

20.

Cicchetti

Rogosch

F. A.

(1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8(4), 597–600. https://doi.org/10.1017/S0954579400007318

21.

Cummings

E. M.

Cheung

R. Y.

Davies

P. T.

(2013). Prospective relations between parental depression, negative expressiveness, emotional insecurity, and children’s internalizing symptoms. Child Psychiatry and Human Development, 44(6), 698–708. https://doi.org/10.1007/s10578-013-0362-1

22.

Cummings

E. M.

Davies

(1996). Emotional security as a regulatory process in normal development and the development of psychopathology. Development and Psychopathology, 8(1), 123–139. https://doi.org/10.1017/S0954579400007008

23.

Cummings

E. M.

Miller-Graff

L. E.

(2015). Emotional security theory: An emerging theoretical model for youths’ psychological and physiological responses across multiple developmental contexts. Current Directions in Psychological Science, 24(3), 208–213. https://doi.org/10.1177/0963721414561510

24.

Davies

P. T.

Hentges

R. F.

Coe

J. L.

Martin

M. J.

Sturge-Apple

M. L.

Cummings

E. M.

(2016a). The multiple faces of interparental conflict: Implications for cascades of children’s insecurity and externalizing problems. Journal of abnormal psychology, 125(5), 664.

25.

Davies

P. T.

Martin

M. J.

Sturge-Apple

M. L.

Ripple

M. T.

Cicchetti

(2016b). The distinctive sequelae of children’s coping with interparental conflict: Testing the reformulated emotional security theory. Developmental Psychology, 52(10), 1646–1665. https://doi.org/10.1037/dev0000170

26.

Dunlap

Golub

Johnson

B. D.

Wesley

(2002). Intergenerational transmission of conduct norms for drugs, sexual exploitation and violence: A case study. British Journal of Criminology, 42(1), 1–20. https://doi.org/10.1093/bjc/42.1.1

27.

Ehrensaft

M. K.

Cohen

(2012). Contribution of family violence to the intergenerational transmission of externalizing behavior. Prevention Science: The Official Journal of the Society for Prevention Research, 13(4), 370–383. https://doi.org/10.1007/s11121-011-0223-8

28.

Ellis

R. E.

Seal

M. L.

Simmons

J. G.

Whittle

Schwartz

O. S.

Byrne

M. L.

Allen

N. B.

(2017). Longitudinal trajectories of depression symptoms in adolescence: Psychosocial risk factors and outcomes. Child Psychiatry and Human Development, 48(4), 554–571. https://doi.org/10.1007/s10578-016-0682-z

29.

Espelage

D. L.

Holt

M. K.

(2001). Bullying and victimization during early adolescence: Peer influences and psychosocial correlates. Journal of Emotional Abuse, 2(2-3), 123–142. https://doi.org/10.1300/J135v02n02_08

30.

Espelage

D. L.

Holt

M. K.

Henkel

R. R.

(2003). Examination of peer–group contextual effects on aggression during early adolescence. Child Development, 74(1), 205–220. https://doi.org/10.1111/1467-8624.00531

31.

Espelage

D. L.

Low

Rao

M. A.

Hong

J. S.

Little

T. D.

(2014). Family violence, bullying, fighting, and substance use among adolescents: A longitudinal mediational model. Journal of Research on Adolescence, 24(2), 337–349. https://doi.org/10.1111/jora.12060

32.

Espelage

D. L.

Van Ryzin

M. J.

Holt

M. K.

(2018). Trajectories of bully perpetration across early adolescence: Static risk factors, dynamic covariates, and longitudinal outcomes. Psychology of Violence, 8(2), 141–150. https://doi.org/10.1037/vio0000095

33.

Evans

L. D.

Kouros

Frankel

S. A.

McCauley

Diamond

G. S.

Schloredt

K. A.

Garber

(2015). Longitudinal relations between stress and depressive symptoms in youth: Coping as a mediator. Journal of Abnormal Child Psychology, 43(2), 355–368. https://doi.org/10.1007/s10802-014-9906-5

34.

Farrell

A. D.

Sullivan

T. N.

Goncy

E. A.

A. T. H.

(2016). Assessment of adolescents’ victimization, aggression, and problem behaviors: Evaluation of the problem behavior frequency scale. Psychological Assessment, 28(6), 702–714. https://doi.org/10.1037/pas0000225

35.

Feinberg

M. E.

Jones

D. E.

Hostetler

M. L.

Roettger

M. E.

Paul

I. M.

Ehrenthal

D. B.

(2016). Couple-focused prevention at the transition to parenthood, a randomized trial: Effects on coparenting, parenting, family violence, and parent and child adjustment. Prevention Science: The Official Journal of the Society for Prevention Research, 17(6), 751–764. https://doi.org/10.1007/s11121-016-0674-z

36.

Fleming

J. E.

Offord

D. R.

(1990). Epidemiology of childhood depressive disorders: A critical review. Journal of the American Academy of Child & Adolescent Psychiatry, 29(4), 571–580. https://doi.org/10.1097/00004583-199007000-00010

37.

Gao

Nickodem

Espelage

D. L.

(2025). Family violence exposure and physical adolescent dating violence perpetration: A longitudinal study. Journal of Family Violence, 1–17. https://doi.org/10.1007/s10896-025-00822-3

38.

Grant

N. J.

Merrin

G. J.

King

M. T.

Espelage

D. L.

(2019). Examining within-person and between-person associations of family violence and peer deviance on bullying perpetration among middle school students. Psychology of Violence, 9(1), 18–27. https://doi.org/10.1037/vio0000210

39.

Hamilton

J. L.

Hamlat

E. J.

Stange

J. P.

Abramson

L. Y.

Alloy

L. B.

(2014). Pubertal timing and vulnerabilities to depression in early adolescence: Differential pathways to depressive symptoms by sex. Journal of Adolescence, 37(2), 165–174. https://doi.org/10.1016/j.adolescence.2013.11.010

40.

Hawkins

R. D.

McDonald

S. E.

O’Connor

Matijczak

Ascione

F. R.

Williams

J. H.

(2019). Exposure to intimate partner violence and internalizing symptoms: The moderating effects of positive relationships with pets and animal cruelty exposure. Child Abuse & Neglect, 98, 104166. https://doi.org/10.1016/j.chiabu.2019.104166

41.

Heyman

R. E.

Slep

A. M. S.

(2002). Do child abuse and interparental violence lead to adulthood family violence? Journal of Marriage and Family, 64(4), 864–870. https://doi.org/10.1111/j.1741-3737.2002.00864.x

42.

Holfeld

Mishna

(2019). Internalizing symptoms and externalizing problems: Risk factors for or consequences of cyber victimization? Journal of Youth and Adolescence, 48(3), 567–580. https://doi.org/10.1007/s10964-018-0974-7

43.

L. T.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

44.

Hughes

E. K.

Gullone

(2010). Reciprocal relationships between parent and adolescent internalizing symptoms. Journal of Family Psychology: JFP: Journal of the Division of Family Psychology of the American Psychological Association (Division 43), 24(2), 115–124. https://doi.org/10.1037/a0018788

45.

Ingram

K. M.

Espelage

D. L.

Davis

J. P.

Merrin

G. J.

(2020). Family violence, sibling, and peer aggression during adolescence: Associations with behavioral health outcomes. Frontiers in Psychiatry, 11, 26. https://doi.org/10.3389/fpsyt.2020.00026

46.

Iverson

K. M.

Resick

P. A.

Suvak

M. K.

Walling

Taft

C. T.

(2011). Intimate partner violence exposure predicts PTSD treatment engagement and outcome in cognitive processing therapy. Behavior Therapy, 42(2), 236–248. https://doi.org/10.1016/j.beth.2010.06.003

47.

Iyer

P. A.

Dougall

A. L.

Jensen-Campbell

L. A.

(2013). Are some adolescents differentially susceptible to the influence of bullying on depression? Journal of Research in Personality, 47(4), 272–281. https://doi.org/10.1016/j.jrp.2013.02.004

48.

Jung

Herrenkohl

T. I.

Lee

J. O.

Hemphill

S. A.

Heerde

J. A.

Skinner

M. L.

(2017). Gendered pathways from child abuse to adult crime through internalizing and externalizing behaviors in childhood and adolescence. Journal of Interpersonal Violence, 32(18), 2724–2750. https://doi.org/10.1177/0886260515596146

49.

Katz

S. J.

Conway

C. C.

Hammen

C. L.

Brennan

P. A.

Najman

J. M.

(2011). Childhood social withdrawal, interpersonal impairment, and young adult depression: A mediational model. Journal of Abnormal Child Psychology, 39(8), 1227–1238. https://doi.org/10.1007/s10802-011-9537-z

50.

Kim

Rodríguez-Guzmán

V. M.

Hamm

Nugent

N. R.

(2021). Childhood trauma and the role of ethnic pride and interpersonal relationships among Latina mothers. Psychological Trauma: Theory, Research, Practice, and Policy, 13(1), 26–34. https://doi.org/10.1037/tra0000954

51.

Knous-Westfall

H. M.

Ehrensaft

M. K.

Watson MacDonell

Cohen

(2012). Parental intimate partner violence, parenting practices, and adolescent peer bullying: A prospective study. Journal of Child and Family Studies, 21(5), 754–766. https://doi.org/10.1007/s10826-011-9528-2

52.

Kretschmer

Veenstra

Deković

Oldehinkel

A. J.

(2017). Bullying development across adolescence, its antecedents, outcomes, and gender-specific patterns. Development and Psychopathology, 29(3), 941–955. https://doi.org/10.1017/S0954579416000596

53.

Lahtinen

Järvinen

Kumlander

Salmivalli

(2020). Does self-compassion protect adolescents who are victimized or suffer from academic difficulties from depression? European Journal of Developmental Psychology, 17(3), 432–446. https://doi.org/10.1080/17405629.2019.1662290

54.

LaMontagne

L. G.

Diehl

D. C.

Doty

J. L.

Smith

(2023). The mediation of family context and youth depressive symptoms by adolescent emotion regulation. Youth & Society, 55(3), 552–580. https://doi.org/10.1177/0044118X211067266

55.

Lawrence

(2022). Family violence, depressive symptoms, school bonding, and bullying perpetration: An intergenerational transmission of violence perspective. Journal of School Violence, 21(4), 517–529. https://doi.org/10.1080/15388220.2022.2114490

56.

Lawrence

T. I.

(2022). Parental support, marital conflict, and stress as predictors of depressive symptoms among African American adolescents. Clinical Child Psychology and Psychiatry, 27(3), 630–643. https://doi.org/10.1177/13591045211070163

57.

Lawrence

T. I.

Chen

Kwok

O. M.

(2025). The distinct patterns and developmental comorbidity of adolescent depressive symptoms, impulsivity, and substance use. Journal of Psychopathology and Behavioral Assessment, 47(2), 39. https://doi.org/10.1007/s10862-025-10216-w

58.

Lawrence

T. I.

Hong

J. S.

Espelage

D. L.

Voisin

D. R.

(2023). Antecedents of sibling aggression and bullying victimization: The parallel and serial contributions of depressive symptoms and substance use. Journal of Affective Disorders, 333, 193–201. https://doi.org/10.1016/j.jad.2023.04.023

59.

Lawrence

T. I.

Hong

J. S.

Sopchak

K. S.

Voisin

D. R.

(2023). The association between exposure to community violence and somatic symptoms through bullying victimization among African American adolescents in Chicago: A developmental trauma approach. Journal of Clinical Psychology, 79(5), 1280–1292. https://doi.org/10.1002/jclp.23475

60.

Lawrence

T. I.

Merrin

G. J.

Mcfield

A. A.

(2022). Family violence and adolescent aggressive behavior: The direct and indirect effects of depression and substance use. International Journal of Mental Health and Addiction, 23, 1–14. https://doi.org/10.1007/s11469-022-00971-2

61.

Lee

Russell

K. N.

O’Donnell

K. A.

Miller

E. K.

Bender

A. E.

Scaggs

A. L.

Berg

K. A.

Holmes

M. R.

(2022). The effect of childhood intimate partner violence (IPV) exposure on bullying: A systematic review. Journal of Family Violence, 37(8), 1283–1300. https://doi.org/10.1007/s10896-021-00299-w

62.

LoSavio

S. T.

Murphy

R. A.

Resick

P. A.

(2021). Treatment outcomes for adolescents versus adults receiving cognitive processing therapy for posttraumatic stress disorder during community training. Journal of Traumatic Stress, 34(4), 757–763. https://doi.org/10.1002/jts.22668

63.

March

J. S.

Parker

J. D.

Sullivan

Stallings

Conners

C. K.

(1997). The Multidimensional Anxiety Scale for Children (MASC): Factor structure, reliability, and validity. Journal of the American Academy of Child & Adolescent Psychiatry, 36(4), 554–565. https://doi.org/10.1097/00004583-199704000-00019

64.

McCrory

E. J.

De Brito

S. A.

Sebastian

C. L.

Mechelli

Bird

Kelly

P. A.

Viding

(2011). Heightened neural reactivity to threat in child victims of family violence. Current Biology: CB, 21(23), R947–R948. https://doi.org/10.1016/j.cub.2011.10.015

65.

McField

A. A.

Lawrence

T. I.

Okoli

I. C.

(2023). Examining the relationships between cyberbullying, relational victimization, and family support on depressive symptoms and substance use among adolescents. Clinical Child Psychology and Psychiatry, 28(1), 224–236. https://doi.org/10.1177/13591045221110126

66.

Mihalic

S. W.

Elliott

(1997). A social learning theory model of marital violence. Journal of Family Violence, 12(1), 21–47. https://doi.org/10.1023/A:1021941816102

67.

Milletich

R. J.

Kelley

M. L.

Doane

A. N.

Pearson

M. R.

(2010). Exposure to interparental violence and childhood physical and emotional abuse as related to physical aggression in undergraduate dating relationships. Journal of Family Violence, 25(7), 627–637. https://doi.org/10.1007/s10896-010-9319-3

68.

Moffitt

T. E.

(1993). Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review, 100(4), 674–701. https://doi.org/10.1037/0033-295X.100.4.674

69.

Moses

J. O.

Villodas

M. T.

(2017). The potential protective role of peer relationships on school engagement in at-risk adolescents. Journal of Youth and Adolescence, 46(11), 2255–2272. https://doi.org/10.1007/s10964-017-0644-1

70.

Nocentini

Fiorentini

Di Paola

Menesini

(2019). Parents, family characteristics and bullying behavior: A systematic review. Aggression and Violent Behavior, 45, 41–50. https://doi.org/10.1016/j.avb.2018.07.010

71.

Olweus

Limber

S. P.

Breivik

(2019). Addressing specific forms of bullying: A large-scale evaluation of the Olweus bullying prevention program. International Journal of Bullying Prevention, 1(1), 70–84. https://doi.org/10.1007/s42380-019-00009-7

72.

Orpinas

P. K.

(1993). Skills training and social influences for violence prevention in middle schools: A curriculum evaluation. Unpublished doctoral dissertation. University of Texas Health Science Center.

73.

Pellegrini

A. D.

Long

J. D.

(2002). A longitudinal study of bullying, dominance, and victimization during the transition from primary school through secondary school. British Journal of Developmental Psychology, 20(2), 259–280. https://doi.org/10.1348/026151002166442

74.

Persampiere

Poole

Murphy

C. M.

(2014). Neuropsychological correlates of anger, hostility, and relationship-relevant distortions in thinking among partner violent men. Journal of Family Violence, 29(6), 625–641. https://doi.org/10.1007/s10896-014-9614-5

75.

Powell

M. D.

Ladd

L. D.

(2010). Bullying: A review of the literature and implications for family therapists. American Journal of Family Therapy, 38(3), 189–206. https://doi.org/10.1080/01926180902961662

76.

Powell

N. P.

Boxmeyer

C. L.

Baden

Stromeyer

Minney

J. A.

Mushtaq

Lochman

J. E.

(2011). Assessing and treating aggression and conduct problems in schools: Implications from the coping power program. Psychology in the Schools, 48(3), 233–242. https://doi.org/10.1002/pits.20549

77.

Prince

Joinson

Kwong

A. S.

Fraser

Heron

(2023). The relationship between timing of onset of menarche and depressive symptoms from adolescence to adulthood. Epidemiology and Psychiatric Sciences, 32, Article e60. https://doi.org/10.1017/S2045796023000707

78.

Prinstein

M. J.

Aikins

J. W.

(2004). Cognitive moderators of the longitudinal association between peer rejection and adolescent depressive symptoms. Journal of Abnormal Child Psychology, 32(2), 147–158. https://doi.org/10.1023/b:jacp.0000019767.55592.63

79.

Raine

Dodge

Loeber

Gatzke‐Kopp

Lynam

Reynolds

Liu

(2006). The reactive–proactive aggression questionnaire: Differential correlates of reactive and proactive aggression in adolescent boys. Aggressive Behavior, 32(2), 159–171. https://doi.org/10.1002/ab.20115

80.

Reinke

W. M.

Eddy

J. M.

Dishion

T. J.

Reid

J. B.

(2012). Joint trajectories of symptoms of disruptive behavior problems and depressive symptoms during early adolescence and adjustment problems during emerging adulthood. Journal of Abnormal Child Psychology, 40(7), 1123–1136. https://doi.org/10.1007/s10802-012-9630-y

81.

Reyes

H. L. M.

Foshee

V. A.

Fortson

B. L.

Valle

L. A.

Breiding

M. J.

Merrick

M. T.

(2015). Longitudinal mediators of relations between family violence and adolescent dating aggression perpetration. Journal of Marriage and Family, 77(4), 1016–1030. https://doi.org/10.1111/jomf.12200

82.

Rivers

A. S.

Russon

Winston-Lindeboom

Ruan-Iu

Diamond

(2022). Family and peer relationships in a residential youth sample: Exploring unique, non-linear, and interactive associations with depressive symptoms and suicide risk. Journal of Youth and Adolescence, 51(6), 1062–1073. https://doi.org/10.1007/s10964-021-01524-x

83.

Saleem

F. T.

Anderson

R. E.

Williams

(2020). Addressing the “myth” of racial trauma: Developmental and ecological considerations for youth of color. Clinical Child and Family Psychology Review, 23, 1–14. https://doi.org/10.1007/s10567-019-00304-1

84.

Salmivalli

Voeten

Poskiparta

(2011). Bystanders matter: Associations between reinforcing, defending, and the frequency of bullying behavior in classrooms. Journal of Clinical Child and Adolescent Psychology: The Official Journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53, 40(5), 668–676. https://doi.org/10.1080/15374416.2011.597090

85.

Satorra

(2000). Scaled and adjusted restricted tests in multi-sample analysis of moment structures. In Heijmans

R. D. H.

Pollock

D. S. G.

Satorra

(Eds.), Innovations in multivariate statistical analysis: A Festschrift for Heinz Neudecker (pp. 233–247). Kluwer Academic Publishers.

86.

Shell

M. D.

Gazelle

Faldowski

R. A.

(2014). Anxious solitude and the middle school transition: A diathesis× stress model of peer exclusion and victimization trajectories. Developmental Psychology, 50(5), 1569–1583. https://doi.org/10.1037/a0035528

87.

Shi

Ettekal

Deutz

M. H.

Woltering

(2020). Trajectories of pure and co-occurring internalizing and externalizing problems from early childhood to adolescence: Associations with early childhood individual and contextual antecedents. Developmental psychology, 56(10), 1906.

88.

Siennick

S. E.

Turanovic

J. J.

(2024). The longitudinal associations between bullying perpetration, bullying victimization, and internalizing symptoms: Bidirectionality and mediation by friend support. Development and Psychopathology, 36(2), 866–877. https://doi.org/10.1017/S0954579423000147

89.

Simpson

Suarez

Connolly

(2012). Treatment and outcomes for anxiety disorders among children and adolescents: A review of coping strategies and parental behaviors. Current Psychiatry Reports, 14(2), 87–95. https://doi.org/10.1007/s11920-012-0254-2

90.

Smith

P. K.

Cowie

Olafsson

R. F.

Liefooghe

A. P. D.

Almeida

Araki

del Barrio

Costabile

Dekleva

Houndoumadi

Kim

Ortega

Pain

Pateraki

Schafer

Singer

Smorti

Toda

Wenxin

(2002). Definitions of bullying: A comparison of terms used, and age and gender differences, in a fourteen-country international comparison. Child Development, 73(4), 1119–1133. https://doi.org/10.1111/1467-8624.00461

91.

Sternberg

K. J.

Lamb

M. E.

Guterman

Abbott

C. B.

(2006). Effects of early and later family violence on children’s behavior problems and depression: A longitudinal, multi-informant perspective. Child Abuse & Neglect, 30(3), 283–306. https://doi.org/10.1016/j.chiabu.2005.10.008

92.

Thornberry

T. P.

Lizotte

A. J.

Krohn

M. D.

Smith

C. A.

Porter

P. K.

(2003). Causes and consequences of deliquency: Findings from the Rochester youth development study. In Thornberry

T. P.

Krohn

M. D.

(Eds.), Taking stock of delinquency (pp. 11–46). Springer. https://doi.org/10.1007/0-306-47945-1_2

93.

Tracy

Salo

Appleton

A. A.

(2018). The mitigating effects of maternal social support and paternal involvement on the intergenerational transmission of violence. Child Abuse & Neglect, 78, 46–59. https://doi.org/10.1016/j.chiabu.2017.09.023

94.

Vickerman

K. A.

Margolin

(2007). Posttraumatic stress in children and adolescents exposed to family violence: II. Treatment. Professional Psychology: Research and Practice, 38(6), 620–628. https://doi.org/10.1037/0735-7028.38.6.620

95.

Vogel

Rosner

(2020). Lost in transition? Evidence-based treatments for adolescents and young adults with posttraumatic stress disorder and results of an uncontrolled feasibility trial evaluating cognitive processing therapy. Clinical Child and Family Psychology Review, 23(1), 122–152. https://doi.org/10.1007/s10567-019-00305-0

96.

Wang

Zhu

Tan

Shi

(2025). Childhood violence exposure and anxiety and depression of children and adolescents. Journal of Affective Disorders, 369, 608–614. https://doi.org/10.1016/j.jad.2024.10.044

97.

Wei

H. S.

Chen

J. K.

(2012). The moderating effect of Machiavellianism on the relationships between bullying, peer acceptance, and school adjustment in adolescents. School Psychology International, 33(3), 345–363. https://doi.org/10.1177/0143034311420640

98.

Whitten

Tzoumakis

Green

M. J.

Dean

(2024). Global prevalence of childhood exposure to physical violence within domestic and family relationships in the general population: A systematic review and proportional meta-analysis. Trauma, Violence, & Abuse, 25(2), 1411–1430. https://doi.org/10.1177/15248380231179133

99.

Widom

C. S.

(1989). The cycle of violence. Science, 244(4901), 160–166. https://doi.org/10.1126/science.2704995

100.

Widom

C. S.

Wilson

H. W.

(2015). Intergenerational transmission of violence. In Lindert

Levav

(Eds.), Violence and mental health: Its manifold faces (pp. 27–45). Springer Science + Business Media. https://doi.org/10.1007/978-94-017-8999-8_2

101.

Willner

C. J.

Gatzke-Kopp

L. M.

Bray

B. C.

(2016). The dynamics of internalizing and externalizing comorbidity across the early school years. Development and Psychopathology, 28(4pt1), 1033–1052. https://doi.org/10.1017/S0954579416000687

Longitudinal Estimation of Exposure to Family Violence Predicting Internalizing and Externalizing Symptoms and the Co-Occurrence During Early Adolescence

Abstract

Keywords

Theoretical Framework

Empirical Evidence on the Associations of Family Violence With Adolescents’ Bullying Perpetration and Depressive Symptoms

Study Aims and Hypotheses

Method

Participants

Procedure

Measures

Exposure to Family Violence

Depressive Symptoms

Bullying Perpetration

Data Analysis Plan

Results

Preliminary Analyses (Missing Data, Descriptive Statistics, and Bivariate Correlations)

Unconditional Latent Growth Model Results

Parallel Process Latent Growth Model Results

Discussion

Development and Co-Occurrence of Bullying Perpetration and Depressive Symptoms

Exposure to Family Violence and Effects on Bullying Perpetration and Depressive Symptoms

Study Strengths and Limitations

Practice Implications

Conclusion

Footnotes

ORCID iD

Funding

Declaration of Conflicting Interests

IRB Approval

Author Biographies

References