Psychological Functioning Among Latino Victims of Teen Dating Violence: The Role of Relational and Collective Resources

Abstract

This study examined whether the relationship between teen dating violence (DV) and psychological functioning (depression, anxiety, and hostility) varied as a function of relational and collective resources (social support, familism, and school connectedness) among Latino teen victims of DV. Data came from a subset of youth who experienced DV (n = 95) from the Dating Violence Among Latinos Adolescents Study, a national survey of Latino teens aged 12-18 years old. Multivariate regression models showed that school connectedness was associated with lower depression and anxiety for DV victims. Additionally, five interactions were significant across depression, anxiety, and hostility: Three following a pattern of protective-enhancing (DV × School connectedness for depression and anxiety; DV × Social support for anxiety) and two following a pattern of vulnerability-reactive (DV × Familism for anxiety and hostility). School connectedness is an important protective factor for Latino teen victims of DV and one that can inform intervention efforts. Latino victims of DV benefit from high levels of school connectedness and social support, especially when DV is high. At high levels of DV familism is associated with a worsening of mental health. It is important to understand the nuances of how these resources work at varying levels of DV for intervention and prevention purposes.

Keywords

relational and collective resources dating violence Latinos social support depression anxiety school connectedness familism

Introduction

Dating violence (DV) is a critical problem among Latino teens in the U.S. DV involves a variety of coercive behaviors directed to a romantic partner that may include physical and/or psychological abuse, sexual assault, and stalking (Centers for Disease Control, 2014). A meta-analysis found a prevalence rate of 20% for physical DV and 9% for sexual DV, with gender differences apparent in sexual DV victimization (Wincentak et al., 2017). Latino youth are equally at risk for physical and sexual DV compared to White youth according to recent Youth Risk Behavior Surveillance data (Kann et al., 2018). DV affects academic achievement, mental health, and physical well-being, including an increased likelihood of depression, anxiety, anger, tobacco and alcohol use, and suicidal ideation (Bonomi et al., 2013; Exner-Cortens et al., 2013; Foshee et al., 2013; Roberts et al., 2003; Rutter et al., 2012). Data from the Dating Violence among Latinos Adolescents (DAVILA) study shows that the number of youths above the clinical levels of depression, anxiety, and hostility is 2-3 times greater if the youth experienced DV in the last year (Sabina et al., 2013).

However, not all youths who experience DV suffer these negative consequences, instead demonstrating resilience. Resilience refers to the process of overcoming adversity that emerges through the interplay between risk and protective factors where, despite exposure to developmental threats, typical or even outstanding developmental outcomes are observed (Boxer & Sloan-Power, 2013; Masten, 2001). Protective factors increase the likelihood of a positive outcome by reducing the risk impact, disrupting/changing negative mechanisms, developing competence, and creating opportunities (Masten, 2001; Rutter, 1988). Culture provides a belief system that brings meaning, purpose, rituals to deal with adversity as well as shape priorities and values (Masten, 2014; Ungar, 2015). Thus, cultural expectations determine whether a youth adequately met the developmental expectations and whether a factor is considered protective (Masten, 2014; Ungar, 2015).

From a socioecological framework, resilience depends on the capacity of interconnected and multidimensional systems (youth’s physical and social ecology), including the individual, family, community, society, and culture, to promote positive outcomes (Masten, 2014; Ungar, 2015). Within this framework, resilience depends on individuals’ (including collectively) ability to navigate resources and take advantage of opportunities when facing adversity, as well as on their ability to negotiate for these resources and opportunities to be provided in culturally meaningful ways (Ungar, 2015). Resilience, thus, also depends on the capacity of the physical and social ecologies to provide youth with those resources and opportunities, including opportunities to exercise influence (Ungar, 2013, 2015). Resilience should not be narrowly focused on the individual but be understood as a “process that is co-facilitated by individuals and their physical and social ecologies” (Ungar & Theron, 2020). Resilience will manifest through an active engagement of resources in processes that enhance well-being in the presence of risk (Ungar, 2013, 2015). Here we are focusing on the experience of DV as the experience of risk or adversity, and thus are only focusing on victims of DV to determine the characteristics of those who do not develop poor outcomes, despite this exposure.

Ungar (2015) conducted a mixed methods study in 11 countries to assess resilience in challenging contexts. Youth participating in the study had experienced barriers to psychological, physical, and social development and yet, were doing well in ways that were relevant to their communities and cultures. Qualitative findings identified seven factors, including the following: (1) Relationships with primary caregiver as well as with prosocial peers, teachers, mentors, and extended family; (2) A powerful identity with a sense of self-competency and external recognition for one’s skills; (3) Personal control and efficacy: Youth’s ability to exercise influence in their environment; (4) Social justice and a sense of being treated fairly in their communities; (5) Access to material resources, from basic needs, education, and opportunities for future employment; (6) A sense of belonging within a community (e.g., school, religious community); (7) Cultural adherence: Youth’s identification with a set of beliefs and norms from a group or culture. From this study, Ungar (2015), grouped resources with three levels: individual (e.g., cognitive skills, personality trait, etc.), relational (e.g., positive relationships with caregiver, mentors, peers, extended family); and collective (e.g., a sense of belonging, cultural adherence, personal safety, and opportunities to contribute to the welfare of others). Collective resources are the responsibility of the immediate and broader community, as it makes these resources available and provides youth opportunities to access them in meaningful ways. Recently Ungar and Theron (2020) underscored the importance of external promotive and protective factors and processes and called for a systemic understanding of resilience, as opposed to focusing exclusively on the individual.

Although research on resources and victimization has increased, gaps persist in the understanding of external protective factors across all groups and risk and protective factors across racial/ethnic groups, with research generally focusing on White Americans (Tummala-Narra, 2007). This study examines the protective effects of three resources on the psychological functioning of Latino adolescents who experienced DV. We included all the resources available in the dataset—two relational, including social support (e.g., emotional support from friends, family, and a special person) and familism (e.g., close relationships with immediate and extended family), and one collective consisting of school connectedness (e.g., respect and fairness, opportunities to provide ideas, express concerns, participate in decision making, and contribute to the welfare of others). We empirically examined the moderating (interaction) effects of these resources on the relationship between DV and psychological functioning. Understanding the resources associated with better psychological functioning among victims is important in order to identify resilience pathways for Latinos and to develop intervention and prevention programs.

Social Support

Extensive research has identified caring relationships and social support as resilience-enhancing relational resources (Benard, 2004; Masten, 2001). For the purpose of this study, social support refers to an individual’s perception that they are a valued, loved, and esteemed member of a group or network, in other words, emotional support (Richards & Branch, 2012). Caring relationships are characterized by a sense of compassion, positive regard, kindness, and availability (Benard, 2004). It is hypothesized that social support aids in the coping process by being related to stress appraisal, impeding physiological stress reactions, and/or promoting healthy coping (Cohen & Wills, 1985). A meta-analysis of protective factors among children exposed to violence identified family support, school support, and peer support as important protective factors, which have additive and/or buffering effects across different types of violence experienced (Yule et al., 2019). Among youth exposed to domestic violence, caring relationships with mentors significantly moderated the relationship between exposure to domestic violence and school engagement showing less negative effects among youth who had more caring adults in their lives (Mariscal, 2013). A review of studies on resilience among those who experienced child maltreatment found social support was the most commonly studied protective factor and was consistently linked to well-being and adaptive functioning (Meng et al., 2018). Specific to DV, high maternal support was associated with lower levels of anxiety and depression, especially at low levels of physical and emotional DV (Holt & Espelage, 2005). Additionally, Banyard and Cross (2008) found social support from the community to moderate the relationships between sexual abuse and physical abuse and suicidal thoughts, depressed mood, and substance use.

Familism

Familism refers to family centeredness, that is, a person’s attachment to their nuclear and extended family as well as the lifelong strong emotional and instrumental interdependence between family members (Welland & Ribner, 2008). Familism suggests that attitudes, behavior, and the structure of the family are guide posts used to shape the lives of each member in the family unit (Coohey, 2001) and may supersede the needs of any individual within the family unit. A meta-analysis found a small effect size for the relationship between familism and internalizing behaviors and depression, pointing to its protective effects (Valdivieso-Mora et al., 2016). Another study found familism was related to lower levels of externalizing symptoms among low income Latino youth (DeCarlo Santiago & Wadsworth, 2011). Specific to behaviors, a study of Latino youth found that familism was related to lower levels of conduct problems, rule-breaking, and aggressive behavior (Marsiglia et al., 2009). Furthermore, a study that modeled growth curves over 10 years found the referent and supportive dimensions of familism to be significantly related to lower levels of depression among Mexican-origin families (Zeiders et al., 2013). However, research specific to familism and family violence is very limited and most evidence comes from related constructs such parental monitoring, family connectedness, family cohesion, and communication with parents (East & Hokoda, 2015; Kast et al., 2016; McNaughton Reyes et al., 2016; Yan et al., 2010). Nonetheless, some studies (Alcalde, 2010; Welland & Ribner, 2008) have reported that familism may contribute to domestic violence among Latinos as the family unity is kept at the expense of the victim’s own “safety or well-being” (Welland & Ribner, 2008, p. 59). Research has not yet explored familism as a moderator between teen DV victimization and mental health.

School Connectedness

Considering the amount of time that youth spend in schools, the relevance of schools regarding DV is evident. DV may occur on school grounds and/or either the victim, aggressor, or their families may ask school personnel for help. Although many DV prevention programs are implemented in schools (Malhotra et al., 2015), scarce research exists on the protective role of school connectedness against DV. “School connectedness refers to an academic environment in which students believe that adults in the school care about their learning and about them as individuals” (Blum, 2005, p. 16). School connectedness is strongest when there are supportive and affirming teachers, staff, and principals or administrators invested in student goals and safety (Blum, 2005). Mentoring and afterschool programs, educator and counselor training, and an investment in student safety and security promote school connectedness and positive student outcomes, especially among youth from marginalized communities (Azmitia et al., 2006; Kuperminc et al., 2009; Stanton-Salazar & Spina, 2005). Greater levels of connectedness and school commitment have been found to have a protective effect for students at risk of engaging in violent behaviors (Catalano et al., 2004), including involvement in either DV victimization or perpetration (Chiodo et al., 2012; Jain et al., 2018; Parker et al., 2016). Similarly, a supportive school climate was related to help-seeking regarding bullying and other threats of violence (Eliot et al., 2010). While school connectedness has been found to be related to lower levels of externalizing symptoms of child sexual abuse survivors (Pérez-González et al., 2017), the role of school connectedness in the psychological functioning of victims of DV is not clear.

The Current Study

The three resources selected reflect relational and collective resources and include the main contexts of the life of youth (i.e., family, friends, and school). The current study explores the moderation effects of social support, school connectedness, and familism on the relationship between DV and depression, anxiety and hostility among Latino teen victims of DV. We add to the current literature by examining these associations for an understudied group, Latinos. We hypothesize that school connectedness, social support, and familism will moderate the relationship between DV and psychological functioning (i.e., depression, anxiety, and hostility).

Method

These analyses used data from the DAVILA study (N = 1,525), a national study conducted from September 2011 to February 2012. The methodology of the study has been published previously (Cuevas et al., 2014; Sabina et al., 2013) and a synopsis is provided here. The following two sampling methodologies were employed: (1) random digit dialing from densely populated Latino neighborhoods (n = 111) and (2) random selection of participants from a list of households with Latino surnames (n = 1,424). The response rate was 36% while the cooperation rate was 55%. Weights have been applied to the data to correct for the probability of being selected and adjust for age and gender to match national numbers for Latino children.

About 58% (n = 893) of the sample either had a boyfriend/girlfriend or went out on a date with someone in the last year and 33% (n = 299) of the daters indicated some form of DV in the last year. The types of DV experienced included psychological (76.8%), physical (33.5%), sexual (28.3%), and stalking (5.2%). Detailed analyses on victimization (Cuevas et al., 2014) and perpetration (Cuevas et al., 2021) have been previously published. The subsample used for the current study consists of 95 youth who reported experiencing DV, excluding psychological DV because school connectedness and familism measures were not collected from them. Measures of school connectedness and familism were collected only from respondents who reported physical, sexual, or stalking DV experiences. See Table 1 for descriptive demographic statistics of the full sample, the victimized subsample, and demographic differences between DV victimized versus non-DV victimized participants.

Procedure

Interviews were conducted over the phone by trained bilingual interviewers using computer-assisted telephone interviewing (CATI). If a household included one child between the ages of 12 and 18, that child was asked to participate. If multiple children met the criteria, the child with the most recent/next birthday was selected. Children below the age of 18 gave assent and required parental consent before participating in the study. Parents provided information about attitudes toward youth safety, their child’s school performance, and demographic information. In total, 80% of the caretakers and 30% of the children responded in Spanish.

The following protections were in place for participants: they were informed about the nature of the questions, they were given the phone number for the National Child Abuse Hotline, they were told about the privacy certificate, and they were asked if they were distressed at the end of the survey. Of those distressed, 6 participants or .4% of the participants requested a follow-up call for referrals. Parents were given $5 and adolescents were given $10 for participation. The Institutional Review Boards of both primary institutions approved the protocol.

Table 1.

Descriptives for Full Sample and Dating Violence Victimized Youth.

	Full Sample %	Dating Violence Victimized Subsample %	DV Victimized vs. All Others p
	M (SD)Range (n)	M (SD)Range (n)	DV Victimized vs. All Others p
Age	14.85 (1.88)12-18 (1,525)	15.68 (1.72)12-18 (95)	<.001
SES	.00 (1.00)-1-10 (1,525)	.14 (1.11)-1-2 (95)	.09
Non-DV victimizations	.76 (1.18)0-8 (1,525)	1.87 (1.74)0-7 (95)	<.001
DV victimizations	.29 (.83)0-8 (1,525)	2.68 (1.57)1-8 (95)	–
School connectedness	–	2.82 (.52)1-3.8 (85)	–
Social support	6.04 (1.04)1.17-7 (1,506)	5.65 (1.15)1.5-7 (90)	<.001
Familism	–	4.28 (.64)2-5 (87)	–
Depression	44.63 (9.19)36-80 (1,515)	49.40 (10.34)36-72 (91)	<.001
Anxiety	43.51 (9.37)35-80 (1,513)	48.11 (9.10)35-70 (91)	<.001
Hostility	44.27 (8.84)45-79 (1,512)	49.48 (10.27)34-79 (91)	<.001
	Full Sample %	Subsample %
Gender			<.001
Male	49.3	68.4
Female	50.7	31.6
Age			<.001
12	13.5	3.2
13	14.8	12.6
14	17.5	9.5
15	15.0	16.8
16	14.4	16.8
17	16.2	27.4
18	8.7	13.7
Employment status			.39a
Not employed	91.8	89.5
Employed full-time	0.9	3.2
Employed part-time	6.5	7.4
Seasonal	0.7	0.0
Sexual orientation			.56b
Heterosexual	93.1	91.6
Gay or Lesbian	0.7	0.0
Bisexual	1.7	4.2
Not sure/transitioning	4.5	4.2
Place of birth			.45
USA	74.7	81.3
Mexico	17.9	13.2
Other	6.1	5.4
Parental education level (%)			.54c
Less that high school	35.2	32.6
High school grad/GED	34.3	31.6
Some college/trade school	12.3	13.7
Two-year college graduate	5.8	5.3
Four-year college graduate	7.2	8.4
Some graduate school	1.1	3.2
Graduate degree	4.0	5.3
Parental relationship status			.44d
Single (never married)	12.1	12.2
Married	69.2	65.6
Cohabitating/committed relationship	8.4	8.9
Divorced	4.2	8.9
Separated	4.2	3.3
Widowed	1.7	1.1
Other	0.3	0.0
Household income 2010			.62c
Under $9,999	12.3	16.3
$10,000-$19,999	26.8	22.1
$20,000-$29,999	21.8	19.8
$30,000-$39,999	13.7	10.5
$40,000-$49,999	8.7	8.1
$50,000-$59,999	5.6	10.5
$60,000-$69,999	2.6	0.0
$70,000-$79,999	2.0	2.3
$80,000 or more	6.5	10.5

Note. Familism and school connectedness only collected for dating violence victims.

aChi-square comparison made on not employed vs. all other categories. bChi-square comparison made on heterosexual vs. all other categories. cCompared using K-S nonparametric test. dChi-square comparison made on married/committed vs. all other categories.

Measures

Demographic information. Caretakers provided information on their ethnic background; country of origin for themselves, their children, and their parents; level of education; household income; employment status; gender and age of child; and relationship status. Adolescents reported their employment status, age, grade level, and sexual orientation. Socioeconomic status was a combination of the standardized scores for household income and parent education.

Victimization. In total, 17 screeners from the Juvenile Victimization Questionnaire (JVQ) (Finkelhor et al., 2005) were used to capture victimization experiences within five areas of victimization in the past year (conventional crime, child maltreatment, peer and sibling victimization, sexual assault, and witnessing and indirect victimizations). Example items are “In the last year, did anyone hit or attack you without using an object or weapon?” and “Now think about kids your age, like from school, a boyfriend or girlfriend, or even a brother or sister. In the last year, did another child or teen make you do sexual things?” Follow-up questions probed the frequency of victimization, injuries sustained, and perpetrator(s). Any violent actions perpetrated by a boyfriend/girlfriend, ex-boyfriend/girlfriend, or date indicated DV and acts were summed (and added to the CTS score described below) to represent the extent of DV. Additionally, the number of non-DV acts (any violent action perpetrated by a nonintimate) endorsed were summed to indicate the extent of other forms of victimization. Validity of the JVQ has been demonstrated with expected associations with trauma symptoms (Finkelhor et al., 2005). However, the authors contend that alpha is not an appropriate for this measure as it is not reflecting an underlying latent construct, but actual life events (Finkelhor et al., 2005).

Dating violence. Six items were used from the modified Conflict Tactics Scales 2-Short Form to assess physical, sexual, and psychological victimization experienced by adolescents who indicated having a dating partner in the past year. Each item asked about behaviors of both the individual and their dating partner within the past year. Response choices are 1 = in the past year, 2 = before, but not in the past year, and 3 = no, this has never happened. Example items are “___ punched, kicked, or beat me up” and “___ used force (like hitting, holding down, or using a weapon) to make me have sex.” Any response indicating past year physical or sexual victimization was used to detect DV in this study (0/1). The scale correlated with the full version of the CTS2 victimization subscales, ranging from .64 to .94 for victimization and perpetration behaviors, and also was correlated with known risk factors for violence in a similar pattern as the full version of the CTS2 (Straus & Douglas, 2004). A count of DV victimization was computed by dichotomizing responses to each item and summing across the items from the JVQ (13 possible that could relate to dating partners) and the CTS (6 possible), resulting in an observed variable ranging from 1 to 8. This summary index is intended to be an indicator variable (Diamantopoulos & Winklhofer, 2001), measuring the number of DV behaviors reported by DV victims.

Familism. The Familism Support Subscale of the Mexican American Cultural Values Scale for Adolescents and Adults measured familism within the context of Mexican culture (Knight et al., 2010). However, other studies have successfully used this measure with a more diverse Latino population (Haack et al., 2014; Kapke et al., 2017). Respondents who experienced physical, sexual, or stalking DV in the past year were asked to indicate what they think or believe about six statements rated on a scale on 1, not at all to 5, completely. An example item is “It is always important to be united as a family.” Ethnic pride, socialization, and social support were correlated with familism scores to determine construct validity (Knight et al., 2010). Cronbach’s alpha for the familism support subscale for the current subsample was .75.

School connectedness. The School Connection Scale assesses students’ sense of belongingness, commitment, belief, and power in relation to school (Brown et al., 2000). Only respondents who indicated experiencing physical, sexual, or stalking DV were asked to respond to 10 items on a 4-point Likert-type scale ranging from 1, strongly disagree, to 4, strongly agree. An example item is “I am comfortable talking to teachers at this school about problems.” Validity for the scale comes from expected relationships with substance use, school participation, and school grades (Brown et al., 2000). For our subsample, Cronbach’s alpha was .82.

Social support. The Multidimensional Scale of Perceived Social Support (MSPSS) was used to assess social support adolescents received from friends, family, and significant others (Zimet et al., 1988). In total, 12 items were provided and responses were recorded on a scale ranging from 1, very strongly disagree, to 7, very strongly agree. An example item is “I have friends with whom I can share my joys and sorrows.” Construct validity was determined by expected correlations between the MSPSS subscales and depression and anxiety subscales (Zimet et al., 1988, 1990). Cronbach’s alpha for our subsample was .90.

Psychological functioning. Depression, anxiety, and hostility were measured using the Brief Symptom Inventory (BSI) scale (Derogatis, 1993). Participants reported on a scale ranging from 0, not at all, to 4, extremely, how each of 17 problems has bothered or distressed them in the past seven days (e.g., feeling blue, feeling so restless you couldn’t sit still, feeling easily annoyed or irritated). Convergent validity was shown with significant correlations with other similar measures such as the Minnesota Multiphasic Personality Inventory (Derogatis, 1993). In our subsample, the overall reliabilities were .82, .61, and .80 for the depression (6 items), anxiety (6 items), and hostility scales (5 items) respectively. Of the subsample, 6.3% met the clinical cutoffs for depression, 5.3% for anxiety, and 5.3% for hostility.

Analyses

In this study, DV includes physical violence, sexual violence, or stalking by a dating partner in the past year (measured by the JVQ and CTS2). In total, 95 DV victims were identified. Data were inspected for collinearity and multivariate outliers. Nine significant multivariate outliers were identified upon inspecting Mahalanobis distance scores—these were excluded from analyses. Three models were run for each dependent variable (i.e., depression, anxiety, and hostility)—Model 1 examined specific resources (i.e., school connectedness, social support, and familism) and Model 2 examined the moderation effects of each resource on the relationship between the number of DV events and psychological functioning (depression, anxiety, and hostility; e.g., two-way interactions). Control variables were gender, age, socioeconomic status, and non-DV victimizations. Continuous focal variables were centered. Interactions were formed by multiplying the centered variables of interest. Significant interactions were probed by calculating predicted scores at one standard deviation above and below the mean for the focal variables and holding the other variables constant. Additionally, a simple slope analysis was conducted on significant interactions.

Results

Bivariate correlations among study variables are available in Table 2, showing that resources were correlated with each other (rs from .42 to .50, p < .001). The three psychological functioning variables were correlated with each other (rs from .56 to .64, p < .001). The three resources were all significantly negatively correlated with the three psychological functioning variables (rs from –.26 to –.51, p < .05) except for hostility and familism.

Resources and Psychological Functioning

Linear regression models (Table 3) examined the relationship between resources (school connectedness, social support, and familism) and depression, anxiety, and hostility among Latino teen DV victims. The three resources were differentially associated with psychological functioning and the second step in each model was significant, therefore Model 2 results are presented here.

Table 2.

Correlations for Victimization, Relational Resources, and Psychological Distress at Wave 1.

Variable	1	2	3	4	5	6	7	8	9	10	11
Female	–
Age	–.01	–
SES	–.11	–.20	–
Non-DV victimizations	–.13	–.30**	.16	–
DV victimization count	–.04	.35***	.03	.10	–
Familism	–.093	.023	–.093	–.063	.043	–
Social support	.062	.26*2	.022	–.50***2	.012	.42***4	–
School connectedness	.125	.175	–.26*5	–.42***5	–.075	.43***5	.50***6	–
Depression	.061	–.26*1	.28**1	.51***1	.011	–.34**3	–.42***2	–.51***5	–
Anxiety	–.041	–.211	.28**1	.44***1	.131	–.26*3	–.26*2	–.47***5	.64***1	–
Hostility	.002	–.23**2	.28**2	.52***2	.092	–.114	–.23*2	–.38***6	.56***2	.63***2	–

Note. n = 95 except were indicated.

*p < .05; **p < .01; ***p < .001.

1n = 91; 2n = 90; 3n = 87; 4n = 86; 5n = 85; 6n = 84.

Table 3.

Linear Regression Models Predicting Psychological Distress (n = 75).

Variable	Model 1			Model 2
Variable	B	SE B	β	B	SE B	β
Depression
Female	3.10	2.13	0.14	3.28	2.07	0.15
Age	–0.39	0.68	–0.07	–0.21	0.66	–0.04
SES	0.87	0.99	0.09	1.03	0.97	0.11
Non-DV victimization count	2.09	0.71	0.36**	2.22	0.69	0.38**
DV count	–0.34	0.91	–0.04	–0.80	0.90	–0.10
Familism	–3.48	1.92	–0.19	–2.81	1.99	–0.15
Social support	–1.07	1.25	–0.11	–0.98	1.26	–0.10
School connectedness	–5.71	2.76	–0.23*	–9.76	3.10	–0.40**
DV × Familism				1.75	1.97	0.11
DV × Social support				0.86	1.14	0.09
DV × School connectedness				–7.27	2.54	–0.35**
R2	.43			.50
F/ΔF	6.19***			5.67***
Anxiety
Female	–1.31	1.93	–0.07	–0.83	1.75	–0.04
Age	–0.93	0.61	–0.17	–1.08	0.56	–0.20
SES	0.60	0.90	0.07	1.02	0.82	0.12
Non-DV victimization count	1.62	0.65	0.31*	1.63	0.58	0.32**
DV count	0.02	0.82	0.00	0.05	0.76	0.01
Familism	–3.10	1.73	–0.19	–0.88	1.69	–0.05
Social support	–0.28	1.13	–0.03	–1.23	1.07	–0.14
School connectedness	–5.63	2.50	–0.26*	–8.88	2.63	–0.41**
DV × Familism				5.26	1.67	0.36**
DV × Social support				–2.10	0.97	–0.26*
DV × School connectedness				–5.54	2.15	–0.30*
R2	.41			.54
F/ΔF	5.72***			6.78***
Hostility
Female	1.84	2.38	0.08	1.49	2.34	0.07
Age	–1.06	0.75	–0.18	–1.23	0.75	–0.20
SES	1.16	1.11	0.12	1.58	1.10	0.17
Non-DV victimization count	2.02	0.80	0.35*	2.02	0.78	0.35*
DV count	1.17	1.01	0.14	1.25	1.02	0.15
Familism	–1.46	2.14	–0.08	0.45	2.26	0.02
Social support	0.44	1.39	0.04	–0.34	1.43	–0.03
School connectedness	–3.27	3.08	–0.13	–1.44	3.51	–0.06
DV × Familism				5.26	2.23	0.32*
DV × Social support				–1.22	1.29	–0.13
DV × School connectedness				1.19	2.87	0.06
R2	.29			.36
F/ΔF	3.43***			3.21**

Note. School connectedness, social support, familism, and DV victimization count were centered at their mean.

*p < .05; **p < .01; ***p < .001.

Depression. Non-DV victimization count (β = .38, p = .002) was positively associated with depression and school connectedness (β = –.40, p = .003) was negatively associated with depression. As Figure 1 shows, there was a significant interaction between school connectedness and DV on depression scores (β = –.35, p = .006). When school connectedness was high, there was a significant negative relationship between DV and depression (b = –4.06, p = .01). When school connectedness was low, there was a nonsignificant relationship between DV and depression (b = 1.88, p = .12).

Anxiety. Non-DV victimization count (β = .32, p = .007) was positively associated with anxiety and school connectedness (β = –.41, p = .001) was negatively associated with anxiety. All three resources moderated the relationship between DV and anxiety, as shown in Figure 2. The first significant interaction was between familism and DV (β = .36, p = .002). When familism was high, there was a significant positive relationship between DV and anxiety (b = 3.55, p = .01). When familism was low, there was a significant negative relationship between DV and anxiety (b = –2.80, p = .02). There was a second significant interaction between social support and DV (β = –.26, p = .03). When social support was high there was a marginally significant negative relationship between DV and anxiety (b = –2.05, p = .07). When social support was low there was a nonsignificant relationship between DV and anxiety (b = 2.15, p = .12). The third significant interaction was between school connectedness and DV (β = –.30, p = .01). When school connectedness was high, there was a marginally significant negative relationship between DV and anxiety (b = –2.44, p = .07). When school connectedness was low, there was a significant positive relationship between DV and anxiety (b = 2.09, p = .04).

Figure 1.

Graph of interaction between school connectedness and DV as a predictor of depression.

Hostility. Non-DV victimization count (β = .35, p = .01) was positively associated with hostility. A significant two-way interaction showed that familism significantly moderated the relationship between DV and hostility (β = .32, p = .02). As shown in Figure 3, when familism was high there was a significant positive relationship between DV and hostility (b = 4.56, p = .01). When familism was low, there was a nonsignificant relationship between DV and hostility (b = –1.22, p = .40).

Figure 2.

Graphs of interactions between relational resources and DV as a predictors of anxiety.

Figure 3.

Graph of interaction between familism and DV as a predictor of hostility.

Discussion

This study addressed two gaps in the resilience literature by focusing on relational and collective resources and examining the functioning of Latino teen victims of DV. Our main aim was to understand how relational and collective resources could mitigate the deleterious effects of DV among Latino teen victims of DV. To examine this question, we limited our sample to victims of DV, who had varying levels of exposure to DV as indicated in the count variable, and examined psychological functioning and the relationships between familism, social support, and school connectedness using interaction effects.

Overall, the findings indicate that the relational and collective resources were related to psychological functioning among Latino teens who experienced DV. Specifically, the collective resource of school connectedness played a protective role for Latino victims of DV by being related to lower levels of depression and anxiety. Additionally, five significant interaction effects were identified pointing to how resources work differently at varying levels of risk exposure (Ungar & Theron, 2020). Indeed, Ungar and Theron (2020) argue for focusing on this question, “Which promotive or protective factors of processes are best for which people in which contexts at what levels of risk exposure and for which outcomes?” Here, we see varying answers to that question, depending on the outcome, level of DV, and resource under examination.

Five interactions surfaced indicating that the relationship between the resources and psychological functioning differed at varying levels of DV (i.e., at different levels of adversity): (1) School connectedness moderated the relationships between DV and depression and (2) anxiety; (3) Social support moderated the relationship between DV and anxiety; (4) Familism moderated the relationships between DV and anxiety and (5) hostility. Three of the interactions followed a protective-enhancing pattern (e.g., school connectedness and social support) and two followed a vulnerability-reactive pattern (e.g., familism), indicating complex interactions that respond to varying levels of stressors instead of one unitary relationship at play. These varying patterns of protection and risk reflect the complexity of resilience, as explained by the sociological approach. Different resources work differently under varying levels of risk exposure. Resilience emerged as the dynamic interplay between evident risk and the active engagement of relational and collective resources in processes that enhance youth well-being (Ungar, 2013, 2015).

The collective resource of school connectedness serves several functions that could help mitigate the negative effects of DV. Ungar et al. (2019) detail the ways in which schools support resilience: accessing material resources, providing supportive relationships, fostering a desirable personal identity, experiencing power, adhering to cultural norms, experiencing social justice, and partaking in social cohesion. In this case, supportive adults and a relatively safe environment, could be related to lower levels of depression and anxiety. This finding is related to similar work examining how school connectedness and commitment were found to be protective among students at risk for engaging in DV victimization or perpetration (Chiodo et al., 2012; Jain et al., 2018; Parker et al., 2016). While researchers have used the term differently, in a recent synthesis Bowles and Scull (2019) focused on four aspects of school connectedness—attending, belonging, engaging, and flowing. They posit these sequential aspects fill the nurturance and attachment needs of the students allowing them to feel safe and valued, thus able to engage positively with the school and its values, think about the future, and be responsive to positive role models (Bowles & Scull, 2019). Students who have experienced DV may find this supportive and challenging environment aids in their well-being. It could also be true that schools that foster higher levels of school connectedness are better able to identify and respond to student victimization.

The two interactions that surfaced for school connectedness show that at low levels of school connectedness there was a positive slope between DV and anxiety—no protection was evident. However, when school connectedness was high there was a negative slope between DV and depression and anxiety. That is, when school connectedness was high more DV was associated with less depression and anxiety. This is called a protective-enhancing effect (Luthar et al., 2000; Masten, 2001). In other words, school connectedness protects students who experience more adversity such that their well-being is enhanced. When school connectedness is high, students can respond to high levels of DV by engaging collective resources in ways that decrease their levels of depression and anxiety. From a socioecological framework of resilience, it is important to note that school connectedness is a collective resource, which requires for the school community (e.g., teachers, administrators, staff, other students, etc.) to make resources available and provide youth with opportunities to access and engage these resources in purposeful ways, such as through opportunities to provide ideas, express concerns, and contribute to the welfare of others, among others (Ungar, 2015). Feeling listened to, having opportunities to express their ideas and make an impact in their environment, having fair and equitable standards and rules, and being able to talk with adults and ask for help (all items in the school connectedness measure), likely provide opportunities for students to mobilize resources, connect with others, and cope with violence in ways that are enhancing to their well-being. These resources appear to have more of an influence when adversity (i.e., DV) is higher. Under these conditions, the benefits of school connectedness are enhanced, which is unexpected, but possibly could reflect a cascading effect (Ungar & Theron, 2020) such that the resource of school connectedness would mobilize other resources (e.g., parents, peers). In sum, school connectedness is protective for those with the highest levels of adversity.

Another interaction that followed the protective-enhancing pattern was between social support (e.g., emotional support) and DV for anxiety. High levels of social support were related to a marginally significant negative slope between DV and anxiety. Here again, we suggest social support is a relational resource that activates when the risk factor is present at high levels (high DV) and engages processes, which may involve positive ways of coping that decrease youth’s anxiety and enhance their well-being. This finding corroborates existing literature regarding children exposed to violence which identified school and peer support as important protective factors with buffering effects across different types of violence experienced (Yule et al., 2019). Another possible explanation for these findings is help-seeking. More DV events, unlike fewer events, may prompt youth to seek informal and formal help, which may result in services, increased sense of social support, and decreased anxiety.

The interactions involving familism followed a different pattern corresponding to risk enhancing effects. Familism enhanced the negative effects of DV on anxiety and hostility. For low levels of familism there was a nonsignificant slope (hostility) and a negative slope (anxiety) as DV increased. However, at high levels of familism, there was a positive slope between DV and anxiety and hostility. From a socioecological resilience framework, familism is an asset, that is, promotive of good outcomes when the risk is low or nonexistent (Ungar, 2013). However, as risk increases, such as high levels of DV, their effects are no longer protective. Further, this is not a protective effect, but rather a vulnerable reactive effect (Luthar et al., 2000; Masten, 2001), which enhances the negative effects of risk in relation to anxiety and hostility at high levels of familism. This means the factor is related to a worsening of well-being at high levels of stress (Luthar et al., 2000; Masten, 2001). This finding contradicts previous theories and research (Tummala-Narra, 2007; Valdivieso-Mora et al., 2016). It is also important to note that the current study is examining psychological functioning among teen DV victims, but other studies using DAVILA data reported that familial support, which is related to familism, was related to lower odds of all forms of victimization measured in DAVILA (Sabina et al., 2021). Considering that familism refers to the centeredness of family, including one’s attachment and interdependence between family members (Welland & Ribner, 2008), why would more familism be related to more anxiety and hostility for Latino victims of DV? Having a strong sense of connection to family could make transgressions of these bonds (as what occurs when there is violence in dating relationships) even more damaging as there is a stark difference between what one values and one what experiences. A strong sense of familism could also create a situation in which Latino teens may have a difficult time deciding if they should tell their families or, if families know, could create additional stress due to the relationship with the perpetrator and divided loyalties due to the power and control dynamics characteristic of DV, including attempts to limit family contact (e.g., isolation) which can create conflict both in the relationship and with family, resulting in increased anxiety and hostility. In addition, familism’s vulnerability enhancing effects on hostility and anxiety may be explained by other variables not included in the model, such as family cohesion or parental monitoring. For instance, familism was associated with increased parental monitoring among Latino youth which was associated with less risky behaviors (e.g., yelling, hitting, anger, cigarettes, alcohol/drugs) (Peña et al., 2011; Romero & Ruiz, 2007). Peña et al. (2011) reported indirect effects of familism on suicide attempts among Latina youth that varied depending on the family environment (e.g., cohesion and conflict), in which familism increased the odds of being in the high cohesion-low conflict family, which was less likely to have teens attempt suicide compared to other family environments. Increased family cohesion and parental monitoring are likely to create family conflict when a youth is in the mid of a violent dating relationship. Together, familism has been shown to be protective in relation to victimization, but can create vulnerabilities in abusive relationships. Further research is necessary around the role of familism and other cultural values on DV among Latino teens and protective mechanisms.

While this study makes several contributions, there are several limitations to note. Because of our interest in exploring moderation effects of resources on the relationship between DV and psychological functioning, the sample in the study was limited to Latino who had certain DV experiences, as resources measures were collected only from them. Unfortunately, the substantial number of teens who had missing data or experienced only psychological DV were excluded because we did not collect data on school connectedness and familism, lowering the size of the sample and not capturing the DV experience of many teens. Our rate of self-disclosed DV is lower than rates in the Youth Risk Behavior Survey (Basile et al., 2020) which focused on those in high school, similar to the rate reported using Add Health data on 12-18 year olds (Halpern et al., 2001), and higher than DV rates of samples that covered all of youth from birth to 17 years (Finkelhor et al., 2013). The lower DV rates found in the current study may be due to underreporting, considering that some adolescents may have not disclosed DV during the phone survey. Those excluded from analyses (n = 20) due to being outliers or having incomplete data, were not significantly different from those included in analyses on any of the study variables except familism (excluded M = .37, included M = –.06, t = 2.20, p = .03), but this may have introduced statistical bias. Another measurement-related limitation is the combination of two measures to assess DV which could lead to reporting of one incident in response to two measures, thus potentially leading to an elevated number of victimizations, although this does not seem to be the case, given the low overall rate. Subsequent DV victimization is not included in the analyses here nor are changing levels of resources, thus the results a capture only a glimpse into the relationships at play and no causation should be assumed, which also restricts our ability to examine DV dynamics. The current study did not examine the factors that influenced participants in seeking help or support from various sources. For example, social status and/or peer influence could influence both behaviors and psychosocial outcomes when experiencing DV, but were not measured here. Additionally, perpetration data is not examined here which could have unique relationships to relational and collective resources. Participants’ reports may be impacted by their recall of incidents at the point in time when the interview was conducted as well as response biases due to the nature of the topic. The sampling technique excluded Latinos who may have had non-Latino surnames.

The study findings do call attention to the importance of resources particularly around school settings and underscore how factors function differently under varying circumstances. Considering that Latinos represent 32% of the population younger than 18 in the United States (Patten, 2016), this study has important implications for family members, school staff, teachers, or those in other helping positions who play a role in helping youth foster resilience. Latino youth who confront with high levels of adversity could avoid poor mental health functioning if they partake in a strong collective environment. Emphasis must be placed on training helping professionals with culturally sensitive approaches targeting the Latino population, including immigrants. Care should be taken to maintain positive relationships with schools and make sure that school environments, including cyberspaces, feel safe for individual victims after experiencing DV. Connection both to schools and to those who offer social support can enhance the well-being of victims. The school community is responsible to provide Latino youth with opportunities to access resources and exercise influence in meaningful ways. These findings point to hopefulness, but also underscore the important role these relational and collective resources play in the lives of teen victims.

Footnotes

Acknowledgments

We acknowledge the contributions of Elizabeth Zadnik, Esprene Liddell- Quintyn, Erin Conklin, and Shana Savage to earlier versions of this manuscript.

Author’s Note

Chiara Sabina is now affiliated with Rutgers University, New Brunswick, NJ, USA

Declaration of Conflicting Interests

Funding

The author(s) disclosed the following financial support for the research, authorship, and/or publication of this article: This project was supported by Grant No. 2009-W9-BX-0001 awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. Findings and conclusions of the research reported here are those of the authors and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

ORCID iDs

Chiara Sabina

E. Susana Mariscal

Carlos A. Cuevas

Author Biographies

Chiara Sabina, PhD, is an associate professor in the School of Social Work at Rutgers University. Her research employs a contextual, strengths-based perspective with respect to interpersonal violence focusing on understudied groups, the influence of cultural variables, help-seeking responses, and examination of the service-delivery system.

E. Susana Mariscal, PhD, is a community-engaged scholar with a research agenda centered on the prevention of child maltreatment and promotion of resilience among children and families, particularly those who identify as Latinx. She led several federally-funded projects that evaluate evidence-based services in community settings, and her work continues to explore the intersections between child maltreatment, domestic violence, and substance use.

Carlos A. Cuevas, PhD, is currently co-director of the Violence and Justice Research Laboratory. His research interests are in the area of victimization and trauma, sexual violence and sexual offending, and psychological assessment. Specifically, his work focuses examining victimization among Latinos and how it relates to psychological distress and service utilization, as well as the role cultural factors play on victimization.

References

Alcalde

M. C.

(2010). Violence across borders: Familism, hegemonic masculinity, and self-sacrificing femininity in the lives of Mexican and Peruvian migrants. Latino Studies , 8(1), 48-68. https://doi.org/10.1057/lst.2009.44

Azmitia

Ittel

Brenk

(2006). Latino-heritage adolescents’ friendships. In Schneider

B. H.

French

D. C.

Chen

(Eds.), Peer relationships in cultural context (pp. 426-451). Cambridge University Press. https://doi.org/10.1017/CBO9780511499739.019

Banyard

Cross

(2008). Consequences of teen dating violence: Understanding intervening variables in ecological context. Violence Against Women , 14(9), 998-1013.

Basile

K. C.

Clayton

DeGue

Gilford

J. W.

Vagi

K. J.

Suarez

N. A.

Zwald

M. L.

Lowry

(2020). Interpersonal violence victimization among high school students—Youth Risk Behavior Survey, United States, 2019. MMWR , 69(1), 28-37.

Benard

(2004). Resiliency: What we have learned . WestEd.

Blum

R. W.

(2005). A case for school connectedness. Educational Leadership , 62(7), 16-19.

Bonomi

A. E.

Anderson

M. L.

Nemeth

Rivara

F. P.

Buettner

(2013). History of dating violence and the association with late adolescent health. BMC Public Health , 13(1), 1-12.

Bowles

Scull

(2019). The centrality of connectedness: A conceptual synthesis of attending, belonging, engaging and flowing. Journal of Psychologists and Counsellors in Schools , 29(1), 3-21. https://doi.org/10.1017/jgc.2018.13

Boxer

Sloan-Power

(2013). Coping with violence: A comprehensive framework and implications for understanding resilience. Trauma, Violence & Abuse , 14(3), 209-221.

10.

Brown

R. A.

Leigh

G. K.

Barton

(2000). The School Connection Scale: A factor analysis. Psychological Reports , 87(3), 851-858.

11.

Catalano

R. F.

Haggerty

K. P.

Oesterle

Fleming

C. B.

Hawkins

J. D.

(2004). The importance of bonding to school for healthy development: Findings from the social development research group. Journal of School Health , 74(7), 252-261.

12.

Centers for Disease Control. (2014). Understanding teen dating violence . https://www.accesskent.com/Health/HealthPromo/pdfs/2017/Understanding_Teen_Violence.pdf

13.

Chiodo

Crooks

C. V.

Wolfe

D. A.

McIsaac

Hughes

Jaffe

P. G.

(2012). Longitudinal prediction and concurrent functioning of adolescent girls demonstrating various profiles of dating violence and victimization. Prevention Science , 13(4), 350-359. https://doi.org/10.1007/s11121-011-0236-3

14.

Cohen

Wills

T. A.

(1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin , 98(2), 310-357.

15.

Coohey

(2001). The relationship between familism and child maltreatment in Latino and Anglo families. Child Maltreatment , 6(2), 130-142. https://doi.org/10.1177/1077559501006002005

16.

Cuevas

C. A.

Sabina

Bell

K. A.

(2014). Dating violence and interpersonal victimization among a national sample of Latino youth. Journal of Adolescent Health , 55(4), 564-570. https://doi.org/10.1016/j.jadohealth.2014.04.007

17.

Cuevas

C. A.

Sabina

Fahlberg

Espinola

(2021). The role of cultural factors on dating aggression and delinquency among Latino youth. Journal of Interpersonal Violence , 36(3-4), NP2137-2163NP. https://doi.org/10.1177/0886260518755486

18.

Santiago

DeCarlo C.

Wadsworth

M. E.

(2011). Family and cultural influences on low-income latino children’s adjustment. Journal of Clinical Child and Adolescent Psychology , 40(2), 332-337. https://doi.org/10.1080/15374416.2011.546038

19.

Derogatis

L. R.

(1993). BSI: Brief symptom inventory . NCS Pearson, Inc.

20.

Diamantopoulos

Winklhofer

H. M.

(2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research , 38(2), 269-277. https://doi.org/10.1509/jmkr.38.2.269.18845

21.

East

P. L.

Hokoda

(2015). Risk and protective factors for sexual and dating violence victimization: A longitudinal, prospective study of Latino and African American adolescents. Journal of Youth and Adolescence , 44(6), 1288-1300. https://doi.org/10.1007/s10964-015-0273-5

22.

Eliot

Cornell

Gregory

Fan

(2010). Supportive school climate and student willingness to seek help for bullying and threats of violence. Journal of School Psychology , 48(6), 533-553. https://doi.org/10.1016/j.jsp.2010.07.001

23.

Exner-Cortens

Eckenrode

Rothman

(2013). Longitudinal associations between teen dating violence victimization and adverse health outcomes. Pediatrics , 131(1), 71-78. https://doi.org/10.1542/peds.2012-1029

24.

Finkelhor

Hamby

S. L.

Ormrod

R. K.

Turner

H. A.

(2005). The Juvenile Victimization Questionnaire: Reliability, validity, and national norms. Child Abuse & Neglect , 29(4), 383-412. https://doi.org/10.1016/j.chiabu.2004.11.001

25.

Finkelhor

Turner

H. A.

Shattuck

Hamby

S. L.

(2013). Violence, crime, and abuse exposure in a national sample of children and youth: An update. JAMA Pediatrics , 167(7), 614-621. https://doi.org/10.1001/jamapediatrics.2013.42

26.

Foshee

V. A.

Reyes

H. L. M.

Gottfredson

N. C.

Chang

L.-Y.

Ennett

S. T.

(2013). A longitudinal examination of psychological, behavioral, academic, and relationship consequences of dating abuse victimization among a primarily rural sample of adolescents. Journal of Adolescent Health , 53(6), 723-729. https://doi.org/10.1016/j.jadohealth.2013.06.016

27.

Haack

L. M.

Gerdes

A. C.

Lawton

K. E.

(2014). Conducting research with latino families: Examination of strategies to improve recruitment, retention, and satisfaction with an at-risk and underserved population. Journal of Child and Family Studies , 23(2), 410-421. https://doi.org/10.1007/s10826-012-9689-7

28.

Halpern

C. T.

Oslak

S. G.

Young

M. L.

Martin

S. L.

Kupper

L. L.

(2001). Partner violence among adolescents in opposite-sex romantic relationships: Findings from the National Longitudinal Study of Adolescent Health. American Journal of Public Health , 91(10), 1679-1685. https://doi.org/10.2105/ajph.91.10.1679

29.

Holt

M. K.

Espelage

D. L.

(2005). Social support as a moderator between dating violence victimization and depression/anxiety among African American and Caucasian adolescents. School Psychology Review , 34(3), 309-328.

30.

Jain

Cohen

A. K.

Paglisotti

Subramanyam

M. A.

Chopel

Miller

(2018). School climate and physical adolescent relationship abuse: Differences by sex, socioeconomic status, and bullying. Journal of Adolescence , 66, 71-82. https://doi.org/10.1016/j.adolescence.2018.05.001

31.

Kann

McManus

Harris

W. A.

Shanklin

S. L.

Flint

K. H.

Queen

Lowry

Chyen

Whittle

Thornton

Lim

Bradford

Yamakawa

Leon

Brener

Ethier

K. A.

(2018). Youth risk behavior surveillance—United States, 2017. Morbidity and Mortality Weekly Report , 67(8), 1-114.

32.

Kapke

T. L.

Gerdes

A. C.

Lawton

K. E.

(2017). Global self-worth in Latino youth: The role of acculturation and acculturation risk factors. Child & Youth Care Forum , 46(3), 307-333. https://doi.org/10.1007/s10566-016-9374-x

33.

Kast

N. R.

Eisenberg

M. E.

Sieving

R. E.

(2016). The role of parent communication and connectedness in dating violence victimization among Latino adolescents. Journal of Interpersonal Violence , 31(10), 1932-1955. https://doi.org/10.1177/0886260515570750

34.

Knight

G. P.

Gonzales

N. A.

Saenz

D. S.

Bonds

D. D.

Germán

Deardorff

Roosav

M. W.

Updegraff

K. A.

(2010). The Mexican American Cultural Values Scale for adolescents and adults. The Journal of Early Adolescence , 30(3), 444-481. https://doi.org/10.1177/0272431609338178

35.

Kuperminc

G. P.

Wilkins

N. J.

Roche

Alvarez-Jimenez

(2009). Risk, resilience, and positive development among Latino youth. In Villarruel

F. A.

Carlo

Grau

J. M.

Azmitia

Cabrera

N. J.

Chahin

T. J.

(Eds.), Handbook of U.S. Latino psychology: Developmental and community-based Perspectives (pp. 213-233). SAGE Publications, Inc.

36.

Luthar

S. S.

Cicchetti

Becker

(2000). The construct of resilience: A critical evaluation and guidelines for future work. Child Development , 71(3), 543-562.

37.

Malhotra

Gonzalez-Guarda

R. M.

Mitchell

E. M.

(2015). A review of teen dating violence prevention research: What about Hispanic youth? Trauma, Violence & Abuse , 16(4), 444-465. https://doi.org/10.1177/1524838014537903

38.

Marsiglia

F. F.

Parsai

Kulis

(2009). Effects of familism and family cohesion on problem behaviors among adolescents in Mexican immigrant families in the Southwest United States. Journal of Ethnic & Cultural Diversity in Social Work , 18(3), 203-220.

39.

Masten

A. S.

(2001). Ordinary magic: Resilience processes in development. American Psychologist , 56(3), 227-238. https://doi.org/10.1037//0003-066x.56.3.227

40.

Masten

A. S.

(2014). Ordinary magic: Resilience in development . The Guilford Press.

41.

Reyes

McNaughton H. L.

Foshee

V. A.

Klevens

Tharp

A. T.

Chapman

M. V.

Chen

M. S.

Ennett

S. T.

(2016). Familial influences on dating violence victimization among Latino youth. Journal of Aggression, Maltreatment & Trauma , 25(8), 773-792. https://doi.org/10.1080/10926771.2016.1210270

42.

Meng

Fleury

M.-J.

Xiang

Y.-T.

D’Arcy

(2018). Resilience and protective factors among people with a history of child maltreatment: A systematic review. Social Psychiatry and Psychiatric Epidemiology , 53(5), 453-475.

43.

Parker

E. M.

Debnam

Pas

E. T.

Bradshaw

C. P.

(2016). Exploring the link between alcohol and marijuana use and teen dating violence victimization among high school students: The influence of school context. Health Education & Behavior , 43(5), 528-536. https://doi.org/10.1177/1090198115605308

44.

Peña

J. B.

Kuhlberg

J. A.

Zayas

L. H.

Baumann

A. A.

Gulbas

Hausmann-Stabile

Nolle

A. P.

(2011). Familism, family environment, and suicide attempts among Latina youth familism and family environment. Suicide and Life-Threatening Behavior , 41(3), 330-341. https://doi.org/10.1111/j.1943-278X.2011.00032.x

45.

Pérez-González

Guilera

Pereda

Jarne

(2017). Protective factors promoting resilience in the relation between child sexual victimization and internalizing and externalizing symptoms. Child Abuse & Neglect , 72, 393-403. https://doi.org/10.1016/j.chiabu.2017.09.006

46.

Patten

(2016). The nation’s Latino population is defined by its youth . Pew Research Center. https://www.pewresearch.org/hispanic/2016/04/20/the-nations-latino-population-is-defined-by-its-youth/

47.

Richards

T. N.

Branch

K. A.

(2012). The relationship between social support and adolescent dating violence. Journal of Interpersonal Violence , 27(8), 1540-1561. https://doi.org/10.1177/0886260511425796

48.

Roberts

T. A.

Klein

J. D.

Fisher

(2003). Longitudinal effect of intimate partner abuse on high-risk behavior among adolescents. Archives of Pediatrics & Adolescent Medicine , 157(9), 875-881.

49.

Romero

Ruiz

(2007). Does familism lead to increased parental monitoring? Protective factors for coping with risky behaviors. Journal of Child and Family Studies , 16(2), 143-154.

50.

Rutter

L. A.

Weatherill

R. P.

Taft

C. T.

Orazem

R. J.

(2012). Examining gender differences in the relationship between dating violence victimization and anger in college students. Violence and Victims , 27(1), 70-77.

51.

Rutter

(1988). Studies of psychosocial risk: The power of longitudinal data . Cambridge University Press.

52.

Sabina

Cuevas

C. A.

Bell

(2013). Final report: Dating Violence among Latino Adolescents (DAVILA) Study (Document No. 242775) .

53.

Sabina

Cuevas

C. A.

L. Y.

(2021). Multiple dimensions of acculturation and the victimization of Latino adolescents. Cultural Diversity and Ethnic Minority Psychology , 27(2), 280-295. https://doi.org/10.1037/cdp0000338

54.

Stanton-Salazar

R. D.

Spina

S. U.

(2005). Adolescent peer networks as a context for social and emotional support. Youth & Society , 36(4), 379-417.

55.

Straus

M. A.

Douglas

E. M.

(2004). A short form of the Revised Conflict Tactics Scales, and typologies for severity and mutuality. Violence and Victims , 19(5), 507-520. https://doi.org/10.1891/088667004780927800

56.

Tummala-Narra

(2007). Conceptualizing trauma and resilience across diverse contexts: A multicultural perspective. Journal of Aggression, Maltreatment & Trauma , 14(1-2), 33-53. https://doi.org/10.1300/J146v14n01_03

57.

Ungar

(2013). Resilience, trauma, context, and culture. Trauma, Violence & Abuse , 14(3), 255-266. https://doi.org/10.1177/1524838013487805

58.

Ungar

(2015). Resilience and culture: The diversity of protective processes and positive adaptation. In Youth resilience and culture: Commonalities and complexities (pp. 37-48). Springer. https://doi.org/10.1007/978-94-017-9415-2_3

59.

Ungar

Connelly

Liebenberg

Theron

(2019). How schools enhance the development of young people’s resilience. Social Indicators Research , 145(2), 615-627. https://doi.org/10.1007/s11205-017-1728-8

60.

Ungar

Theron

(2020). Resilience and mental health: How multisystemic processes contribute to positive outcomes. The Lancet Psychiatry , 7(5), 441-448. https://doi.org/10.1016/S2215-0366(19)30434-1

61.

Valdivieso-Mora

Peet

C. L.

Garnier-Villarreal

Salazar-Villanea

Johnson

D. K.

(2016). A systematic review of the relationship between familism and mental health outcomes in Latino population. Frontiers in Psychology , 7, Article 1632.

62.

Welland

Ribner

(2008). Healing from violence: Latino men’s journey to a new masculinity . Springer.

63.

Wincentak

Connolly

Card

(2017). Teen dating violence: A meta-analytic review of prevalence rates. Psychology of Violence , 7(2), 224-241.

64.

Yan

F. A.

Howard

D. E.

Beck

K. H.

Shattuck

Hallmark-Kerr

(2010). Psychosocial correlates of physical dating violence victimization among Latino eraly adolescents. Journal of Interpersonal Violence , 25(5), 808-831. https://doi.org/10.1177/0886260509336958

65.

Yule

Houston

Grych

(2019). Resilience in children exposed to violence: A meta-analysis of protective factors across ecological contexts. Clinical Child and Family Psychology Review , 22(3), 406-431.

66.

Zeiders

K. H.

Updegraff

K. A.

Umaña-Taylor

A. J.

Wheeler

L. A.

Perez-Brena

N. J.

Rodríguez

S. A.

(2013). Mexican-origin youths’ trajectories of depressive symptoms: The role of familism values. The Journal of Adolescent Health , 53(5), 648-654. https://doi.org/10.1016/j.jadohealth.2013.06.008

67.

Zimet

G. D.

Dahlem

Zimet

Farley

(1988). The Multidimensional Scale of Perceived Social Support. Journal of Personality Assessment , 52(1), 30-41.

68.

Zimet

G. D.

Powell

S. S.

Farley

G. K.

Werkman

Berkoff

(1990). Psychometric characteristics of the Multidimensional Scale of Perceived Social Support. Journal of Personality Assessment , 55(3-4), 610-617. https://doi.org/10.1080/00223891.1990.9674095