Dating Aggression among Court-Involved Adolescents: Prevalence,Offense Type,and Gender

Abstract

Adolescents involved in the juvenile justice system face a variety of risk factors that are associated with more frequent and severe experiences of aggression within romantic relationships as compared to community samples. The current study examines the nature and characteristics of adolescent dating violence (ADV) among first-time offense court-involved non-incarcerated (CINI) adolescents. A sample of 199 male and female CINI adolescents (58% male; M age = 15.05) who had a first-time, open status (e.g., truancy, curfew violation) and/or delinquent petition (e.g., assault, breaking, and entering). Overall, CINI adolescents reported prevalence rates of ADV consistent with community samples of adolescents. Females reported higher perpetration than did males in the sample of physical abuse and social networking abuse, as well as higher victimization of social networking abuse. Only one difference was found by offense type. CINI females report an increased risk for dating violence, though the cause of these gender differences is unclear. Findings also highlight that risk for ADV does not differ by offense type, suggesting that prevention efforts targeting ADV at the earliest possible intervention point, regardless of first-time offense type or severity, may be especially impactful.

Keywords

adolescent dating violence dating aggression relationship violence juvenile justice court-involved non-incarcerated youth.

Approximately 80% of arrested adolescents are diverted from detention to community supervision (Hockenberry & Puzzanchera, 2014). Yet, the vast majority of work examining risk for adolescent dating violence (ADV) among court-involved youth has focused on detained or incarcerated adolescents (DeHart & Moran, 2015; Hilton et al., 2010; Krohn & Lane, 2015). This limitation is especially noteworthy as court-involved non-incarcerated (CINI) adolescents may be at more imminent risk for ADV as they remain in their communities with continued interaction with partners. CINI adolescents are at the intersection of risk factors for ADV. Specifically, consistent with risk for ADV, court-involved youth experience higher rates of prior trauma, lower-quality parental relationships, peer deviance, lower-quality romantic experiences, more violent neighborhoods, lower socioeconomic status, gang membership, delinquent behaviors, and higher psychopathology than that documented among general adolescent samples (Ford et al., 2008; Krohn & Lane, 2015; Yu et al., 2018). Thus, CINI adolescents are an understudied yet vulnerable population. The current study aims to address this gap in the literature by examining patterns of ADV among CINI adolescents.

In addition to limited work examining ADV among court-involved youth more generally, existing literature has only examined males and females in separate studies, making understanding potential gender effects impossible (Brendgen et al., 2002; Hilton et al., 2010; Kelly et al., 2009). Finally, no research with court-involved youth has examined differences in ADV by offense type (status vs. delinquent). Status offenses include conduct that would not be a crime if the individual were an adult (e.g., truancy, curfew violation). Delinquent offenses include conduct that is a crime regardless of the individual’s age (e.g., assault, larceny). Understanding if risk for ADV differs by offense type is necessary to inform whether differential intervention may be appropriate by offense type. Specifically, there is some preliminary evidence that the severity of offense is related to the likelihood of treatment referral (Zeola et al., 2017). For ADV, if there are no differences by offense type, then earlier dating violence intervention may be indicated.

Current Study

The current study is the first to our knowledge to examine the nature and characteristics of ADV among first-time offense CINI adolescents. We first present a series of descriptive characteristics of ADV behavior among CINI adolescent males and females. Next, we examine a series of regressions examining gender and offense type as predictors of ADV perpetration and victimization (threatening behaviors, physical abuse, and social network abuse).

Methods

Participants and Procedure

Participants were drawn from a longitudinal study investigating CINI youth who had a first-time, open status and/or delinquent petition through a large Family Court in the Northeastern region of the United States. All caregivers of CINI youth were sent a study flyer along with the standard court appointment date notification letter and then approached in the court setting for study participation within the first 30 days of initial court contact. Interested youth and families were screened for eligibility (e.g., first-time court contact, age between 12 and 18 years) in a private court space. Four hundred and twenty-three youth between ages 12 and 18 were recruited. Further study procedures are detailed in blinded citation. The current study includes baseline demographic data (Table 1). ADV was assessed at the 4-month follow-up (T2). Self-report data collection among justice-involved youth has been found to be valid (Jolliffe et al., 2003). Only participants who reported on a romantic relationship at the assessment of ADV (T2) were included. Finally, transgender participants (n = 1) were excluded from analyses. The final sample for the current study is 199 youth (58% male; M age = 15.05, SD = 1.54). The study was approved by the Blinded Institutional Review Board.

Measures

Demographics. Age, self-identified gender, race, and ethnicity were assessed at baseline.

Dating violence. Conflict in Adolescent Dating Relationships Inventory (CADRI; Wolfe et al., 2001) is a 35-item self-report assessment of abusive and violent behavior within adolescent romantic relationships. Items are rated on a 4-point Likert-type scale, from “Never” to “Often,” in the past year. An example question is, “I slapped him/her or pulled his/her hair.” The current study used the Physical Abuse and Threatening Behavior subscales for victimization and perpetration (physical abuse; perpetration α = 0.84; victimization α = 0.80; threatening behavior; perpetration α = 0.86; victimization α = 0.92).

Social Networking Abuse Scale (Rizzo et al., 2018) is a 9-item self-report assessment of adolescent victimization and perpetration of digital abuse. Item responses are yes/no and were summed to create scores of victimization (3 items, α = 0.74; range = 0–3) and perpetration (6 items, α = 0.87; range = 0–6). An example item is, “In the past 12 months, have you checked through a boyfriend/girlfriend’s text messages to see if they have spoken to someone you don’t trust (like a person they may be hooking up with behind your back).”

Analytic Strategy

Due to zero-inflation, physical dating violence and threatening behaviors were dichotomized between any behaviors in the past year versus none in the past year. Social networking abuse was able to be treated as continuous. Analyses were conducted using full information maximum likelihood to estimate parameters when data are missing (Graham, 2009). As we aimed to describe patterns of ADV within this sample by gender, first offense type, and race/ethnicity, we included these three characteristics as independent variables. Given the dichotomous nature of physical ADV and threatening behaviors, we conducted a series of stepwise logistic regression analyses (see Table 2). Multiple linear regression models were tested to examine social networking abuse since it is a continuous variable (see Table 2). For each analysis, we included age and race/ethnicity in the first block; gender and offense type were entered simultaneously into the second block. A separate model was run for each subtype of ADV.

Results

Among CINI youth, 13.7% and 12.7% reported threatening behavior perpetration and victimization, respectively. Regarding physical dating abuse, 19.3% and 15.7% reported perpetration and victimization, respectively. Finally, 27.3% and 44.1% reported social networking abuse perpetration and victimization, respectively. Rates of ADV overall spanned 13–40% among females as compared to 13–25% among males. Additional descriptive statistics of ADV by gender and offense type are displayed in Table 1.

Table 1.
Summary of Dating Violence Behaviors among CINI Youth.

Full Sample Physical ADV Perpetration Physical ADV Victimization Threatening ADV Perpetration Threatening ADV Victimization Social Networking ADV Perpetration Social Networking ADV Victimization

(N = 199) (n = 38) (n = 31) (n = 27) (n = 25) (n = 87) (n = 53)

Gender

Male 58% 41% 65% 56% 72% 41% 47%

Female 42% 58% 36% 44% 28% 58% 53%

Offense type

Status 45% 40% 39% 26% 36% 41% 42%

Delinquent 55% 61% 61% 74% 64% 59% 59%

Race/ethnicity

White, non-Latinx 38% 26% 32% 30% 36% 40% 40%

Black, non-Latinx 5% 13% 7% 15% 8% 6% 6%

Mixed Race, non-Latinx 7% 13% 10% 11% 12% 7% 4%

Latinx 43% 42% 42% 41% 8% 38% 47%

Other, non-Latinx 7% 3% 10% 4% 36% 8% 4%

Notes: Percentages within each form of ADV reflect the proportion of the ADV type by each predictor. Percentages were rounded to the nearest whole value.

Female CINI adolescents were more likely to endorse perpetration of physical abuse as compared to males (see Table 2). Females also reported more social networking abuse perpetration and victimization. No other gender differences emerged. No differences by race/ethnicity were found. Only one significant effect of offense type emerged. Specifically, youth with delinquent charges were more likely to report perpetration of threatening behaviors.

Table 2.
Regressions Examining Gender and Offense Type as Predictors of ADV.

Logistic Regressions of Victimization and Perpetration of Physical Violence and Threatening Behaviors

Physical ADV Victimization Physical ADV Perpetration

R = 0.08

Predictors B SE OR [95% CI] B SE OR [95% CI]

Age 0.03 0.14 1.03 [0.82, 1.30] 0.12 0.12 1.13 [0.93, 1.38]

Offense Type 0.08 0.45 1.33 [0.52, 2.25] 0.36 0.38 1.43 [0.76, 2.68]

Gender –0.45 0.45 0.716 [0.31, 1.33] 0.86* 0.37 2.36 [1.29, 4.33]

Race/Ethnicity 0.07 0.12 1.034 [0.89, 1.31] 0.06 0.10 1.06 [0.89, 1.25]

Threatening Behaviors ADV Victimization Threatening Behaviors ADV Perpetration

R = 0.11

Age –0.06 0.16 0.95 [0.72, 1.23] 0.07 0.16 1.07 [0.83, 1.38]

Offense Type 0.40 0.52 1.50 [0.64, 3.51] 1.22* 0.55 3.39 [1.38, 8.34]

Gender –0.70 0.53 0.50 [0.21, 1.18] –0.01 0.48 0.99 [0.45, 2.15]

Race/Ethnicity –0.07 0.13 0.94 [0.75, 1.16] –0.02 0.13 0.99 [0.80, 1.22]

Multiple Linear Regression Associations with Victimization and Perpetration of Social Networking Abuse

Predictors B SE B SE Age –0.03 0.05

0.049 0.11 Offense Type

0.15 0.17 0.30 0.35 Gender 0.26* 0.12 1.27***

Notes: OR = odds ratio; CI = confidence interval. ^p < .05; ^p < .01; ^p < .001.

Discussion

The current study demonstrates that CINI youth, age 12–18, report ADV rates consistent with general adolescent samples (Haynie et al., 2013; Leen et al., 2013; Taylor & Mumford, 2016). In contrast, prior work has demonstrated an increased risk for dating violence among justice-involved youth, with physical ADV rates among broad samples of justice-involved adolescents ranging from 40–50% in the past year (Buttar et al., 2013; Mueller et al., 2013) as compared to the approximately 10% in the past year among community samples of adolescents (Kann et al. 2016) . Thus, the lower CINI youth prevalence rates are surprising. Some insight into the patterns among CINI youth may be found in the gender differences.

Females reported greater perpetration of physical abuse, as well as social networking perpetration and victimization, as compared to males. Among female CINI adolescents, rates of ADV overall spanned 13–40% as compared to 13–25% among males. These lower prevalence rates among males may reflect true gender differences. Among general adolescent samples, some research suggests that females are more likely to report ADV (Brooks-Russell et al., 2015; Burk & Seiggne-Krenke, 2015; Foshee et al., 2007). The current findings suggest these gender differences may be more pronounced among CINI adolescents. An alternative possible account for the pattern is that the lower prevalence rates may also reflect a potential underreporting of ADV among male CINI adolescents. It is possible CINI males may be less likely to endorse ADV for fear of consequence of greater systems involvement as females are more likely to perceive ADV as acceptable (Bowen et al., 2013). Future work should aim to further understand dating violence among CINI males using naturalistic and ecologically valid assessments that account for potential desirability biases.

Also contrary to hypotheses, only one difference in risk for ADV was found by offense type. Given the number of analyses conducted, this finding should be interpreted with caution and may reflect a spurious association. The dearth of differences by offense type is surprising given that risk for ADV is related to factors known to be more pronounced among delinquent offenders (Chase et al., 2002; Kelly et al., 2009). One potential explanation for this pattern may be the increased monitoring, restrictions, or structure following delinquent (versus status) offenses that may limit interaction with romantic partners. Further, status offenses (e.g., truancy) may be linked to greater opportunities for unsupervised times with romantic partners when dating violence may occur. Adolescents in violent relationships may also be encouraged to skip school to spend time with their partner. The overall lack of differences by offense type indicates that dating violence prevention efforts among CINI adolescents should begin as early as possible, (i.e., at first-time court contact) given the consistency of risk across justice involvement.

Limitations and Conclusion

There are several limitations in the current study. First, the low frequency of dating violence made it necessary to examine threatening behavior and physical abuse dichotomously. This is a noteworthy limitation as, based on past literature, we would expect rates of dating violence among CINI adolescents to be higher than general adolescent samples. Future work should aim to use multiple measures of dating violence to better capture the variation in violence. Relatedly, future work should also examine patterns across additional subtypes of ADV (e.g., sexual ADV). A second limitation is we were unable to examine differences by first offense type. Future work should compare violent and non-violent offenses as we may theoretically expect differences. Notably, the current sample reflects the disproportionate minority contact found within the juvenile justice system; yet no differences in ADV by race/ethnicity were found. However, the sample was too limited to examine potential interactions by race/ethnicity, which would be important for future work understanding these processes across youth. Finally, the current study was cross-sectional. Understanding how the prevalence of dating violence changes developmentally among CINI adolescents is necessary.

Despite these limitations, the current study provides necessary insight into the prevalence and patterns of dating violence among CINI adolescents. It demonstrates that CINI females report an increased risk for dating violence and underscores a potential need for innovative assessments of ADV behaviors among CINI males. Further, it highlights that risk for ADV is largely uniform regardless of first offense type that results in an adolescent’s first-time court contact, suggesting that prevention efforts targeting ADV at the earliest possible intervention point, regardless of the reason for court referral, may be especially impactful.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Preparation of this manuscript was supported by Grant R01DA034538 from the National Institute on Drug Abuse (M. Tolou-Shams, P.I.), Grant K24DA046569 from the National Institute on Drug Abuse (M. Tolou-Shams, P.I.), Grant K01HD097218 from National Institute of Child Health and Human Development (C. Collibee, P.I.), Grant T32MH018261 from the National Institute of Mental Health, and Grant K23MH111606 from the National Institute of Mental Health (K. Kemp, P.I.).

ORCID iD

Charlene Collibee

Author Biographies

Charlene Collibee, PhD, is an assistant professor (Research) in the Department of Psychiatry and Human Behavior at the Alpert Medical School of Brown University. Her research interests include interpersonal and developmental risk and resilience.

Kara Fox is a clinical research assistant at Alpert Medical School of Brown University and Rhode Island Hospital. She is interested in the effects of adolescent interpersonal processes, particularly within the context of technology, on development and health.

Johanna Folk, PhD, is a clinical psychologist and postdoctoral fellow at the University of California, San Francisco. Dr. Folk’s research centers on improving behavioral health outcomes for justice-involved populations, focusing on the role of interpersonal relationships, and leveraging family support to improve outcomes.

Christie Rizzo, PhD, is an associate professor in the Department of Applied Psychology at Northeastern University, Bouvé College of Health Sciences. Her research interests include dating violence and sexual risk prevention for high-risk youth.

Kathleen Kemp, PhD, is an assistant professor (Research) in the Department of Psychiatry and Human Behavior at the Alpert Medical School of Brown University. Her areas of expertise focus on forensic psychology, adolescent development, and treatment development and implementation in juvenile justice settings.

Marina Tolou-Shams is a UCSF professor in the Department of Psychiatry, Division Director of Infant, Child and Adolescent Psychiatry, Deputy Vice Chair for Research, and Chief Psychologist at the Zuckerberg San Francisco General Hospital. Her research interests have primarily focused on the development and testing of empirically supported integrated mental health, substance use and sexual health interventions for justice-involved youth.

References

Bowen

Holdsworth

Leen

Sorbring

Helsing

Jaans

Awouters

(2013). Northern European adolescent attitudes toward dating violence. Violence and Victims , 28(4), 619–634.

Brendgen

Vitaro

Tremblay

R. E.

Wanner

(2002). Parent and peer effects on delinquency-related violence and dating violence: a test of two mediational models. Social Development , 11(2), 225–244.

Brooks-Russell

Foshee

V. A.

Reyes

H. L. M.

(2015). Dating Violence. In Gullotta

T. P.

Plant

R. W.

Evans

(Eds.), Handbook of adolescent behavioral problems (pp. 559–576). Springer.

Burk

W. J.

Seiffge-Krenke

(2015). One-sided and mutually aggressive couples: Differences in attachment, conflict prevalence, and coping. Child Abuse & Neglect , 50, 254–266.

Buttar

Clements-Nolle

Haas

Reese

(2013). Dating violence, psychological distress, and attempted suicide among female adolescents in the juvenile justice system. Journal of Correctional Health Care , 19(2), 101–112.

Chase

K. A.

Treboux

O’leary

K. D.

(2002). Characteristics of high-risk adolescents’ dating violence. Journal of Interpersonal Violence , 17(1), 33–49.

DeHart

D. D.

Moran

(2015). Poly-victimization among girls in the justice system: Trajectories of risk and associations to juvenile offending. Violence Against Women , 21(3), 291–312.

Ford

J. D.

Hartman

J. K.

Hawke

Chapman

J. F.

(2008). Traumatic victimization, posttraumatic stress disorder, suicidal ideation, and substance abuse risk among juvenile justice-involved youth. Journal of Child & Adolescent Trauma , 1(1), 75–92.

Foshee

V. A.

Bauman

K. E.

Linder

Rice

Wilcher

(2007). Typologies of adolescent dating violence: Identifying typologies of adolescent dating violence perpetration. Journal of Interpersonal Violence , 22(5), 498–519.

10.

Graham

J. W.

(2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology , 60, 549–576.

11.

Haynie

D. L.

Farhat

Brooks-Russell

Wang

Barbieri

Iannotti

R. J.

(2013). Dating violence perpetration and victimization among U.S. adolescents: Prevalence, patterns, and associations with health complaints and substance use. Journal of Adolescent Health , 53(2), 194–201.

12.

Hilton

N. Z.

Harris

G. T.

Popham

Lang

(2010). Risk assessment among incarcerated male domestic violence offenders. Criminal Justice and Behavior , 37(8), 815–832.

13.

Hockenberry

Puzzanchera

(2014). Juvenile court statistics, 2011 . Office of Juvenile Justice and Delinquency Prevention, Office of Justice Programs, U.S. Department of Justice.

14.

Jolliffe

Farrington

D. P.

Hawkins

J. D.

Catalano

R. F.

Hill

K. G.

Kosterman

(2003). Predictive, concurrent, prospective and retrospective validity of self-reported delinquency. Criminal Behaviour and Mental Health , 13(3), 179–197.

15.

Kann

McManus

Harris

W. A.

Shanklin

S. L.

Flint

K. H.

Hawkins

Queen

Lowry

Olsen

E. O. M.

Chyen

Whittle

(2016). Youth risk behavior surveillance—United States, 2015. Morbidity and Mortality Weekly Report: Surveillance Summaries , 65(6), 1–174.

16.

Kelly

P. J.

Cheng

A. L.

Peralez-Dieckmann

Martinez

(2009). Dating violence and girls in the juvenile justice system. Journal of Interpersonal Violence , 24(9), 1536–1551.

17.

Krohn

M. D.

Lane

(2015). The handbook of juvenile delinquency and juvenile justice . John Wiley and Sons.

18.

Leen

Sorbring

Mawer

Holdsworth

Helsing

Bowen

(2013). Prevalence, dynamic risk factors and the efficacy of primary interventions for adolescent dating violence: An international review. Aggression and Violent Behavior , 18(1), 159–174.

19.

Mueller

Jouriles

E. N.

McDonald

Rosenfield

(2013). Adolescent beliefs about the acceptability of dating violence: Does violent behavior change them? Journal of Interpersonal Violence , 28(2), 436–450.

20.

Rizzo

C. J.

Joppa

Barker

Collibee

Zlotnick

Brown

L. K.

(2018). Project Date SMART: A dating violence (DV) and sexual risk prevention program for adolescent girls with prior DV exposure. Prevention Science , 19(4), 416–426.

21.

Taylor

B. G.

Mumford

E. A.

(2016). A national descriptive portrait of adolescent relationship abuse: Results from the National Survey on Teen Relationships and Intimate Violence. Journal of interpersonal violence , 31(6), 963–988.

22.

Wolfe

D. A.

Scott

Reitzel-Jaffe

Wekerle

Grasley

Straatman

A. L.

(2001). Development and validation of the conflict in adolescent dating relationships inventory. Psychological Assessment , 13(2), 277.

23.

Pepler

D. J.

van de Bongardt

Josephson

W. L.

Connolly

(2018). Internalizing symptoms and dating violence perpetration in adolescence. Journal of Adolescence , 69, 88–91.

24.

Zeola

M. P.

Guina

Nahhas

R. W.

(2017). Mental health referrals reduce recidivism in first-time juvenile offenders, but how do we determine who is referred?. Psychiatric Quarterly, 88(1), 167–183.

	Full Sample	Physical ADV Perpetration	Physical ADV Victimization	Threatening ADV Perpetration	Threatening ADV Victimization	Social Networking ADV Perpetration	Social Networking ADV Victimization
	(N = 199)	(n = 38)	(n = 31)	(n = 27)	(n = 25)	(n = 87)	(n = 53)
Gender
Male	58%	41%	65%	56%	72%	41%	47%
Female	42%	58%	36%	44%	28%	58%	53%
Offense type
Status	45%	40%	39%	26%	36%	41%	42%
Delinquent	55%	61%	61%	74%	64%	59%	59%
Race/ethnicity
White, non-Latinx	38%	26%	32%	30%	36%	40%	40%
Black, non-Latinx	5%	13%	7%	15%	8%	6%	6%
Mixed Race, non-Latinx	7%	13%	10%	11%	12%	7%	4%
Latinx	43%	42%	42%	41%	8%	38%	47%
Other, non-Latinx	7%	3%	10%	4%	36%	8%	4%

Logistic Regressions of Victimization and Perpetration of Physical Violence and Threatening Behaviors
Physical ADV Victimization				Physical ADV Perpetration
	R = 0.08
Predictors	B	SE	OR [95% CI]	B	SE	OR [95% CI]
Age	0.03	0.14	1.03 [0.82, 1.30]	0.12	0.12	1.13 [0.93, 1.38]
Offense Type	0.08	0.45	1.33 [0.52, 2.25]	0.36	0.38	1.43 [0.76, 2.68]
Gender	–0.45	0.45	0.716 [0.31, 1.33]	0.86*	0.37	2.36 [1.29, 4.33]
Race/Ethnicity	0.07	0.12	1.034 [0.89, 1.31]	0.06	0.10	1.06 [0.89, 1.25]
Threatening Behaviors ADV Victimization				Threatening Behaviors ADV Perpetration
	R = 0.11
Age	–0.06	0.16	0.95 [0.72, 1.23]	0.07	0.16	1.07 [0.83, 1.38]
Offense Type	0.40	0.52	1.50 [0.64, 3.51]	1.22*	0.55	3.39 [1.38, 8.34]
Gender	–0.70	0.53	0.50 [0.21, 1.18]	–0.01	0.48	0.99 [0.45, 2.15]
Race/Ethnicity	–0.07	0.13	0.94 [0.75, 1.16]	–0.02	0.13	0.99 [0.80, 1.22]
Multiple Linear Regression Associations with Victimization and Perpetration of Social Networking Abuse
Predictors		B	SE	B		SE	Age		–0.03	0.05
0.049		0.11	Offense Type
0.15	0.17	0.30		0.35	Gender		0.26*	0.12	1.27***