Polydrug Use and Dating Violence Among Emerging Adults

Abstract

We examined sociodemographic and psychosocial risk factors that moderate the (poly) substance use and dating violence victimization and perpetration relationship among emerging adults. Using an ethnically diverse sample (N = 698), we used latent class analyses to identify mutually exclusive groups based on monthly and past-year substance use. We then examined these groups as they relate to dating violence victimization and perpetration and the moderating effect of various risk factors. Five classes were identified based on substance use patterns: (a) Regular Alcohol use, (b) Polysubstance use, (c) Heavy Alcohol and Marijuana use, (d) Mild Alcohol use, and (e) Occasional Alcohol and Marijuana use classes. Participants in the Polysubstance use class were the most likely to perpetrate dating violence followed by Heavy Alcohol and Marijuana use, Occasional Alcohol and Marijuana use, Regular Alcohol, and Mild Alcohol use classes. Similarly, participants in the Polysubstance use class were the most likely to be victims of dating violence followed by Occasional Alcohol and Marijuana, Heavy Alcohol and Marijuana, Regular Alcohol, and Mild Alcohol use classes. Depending on substance use class, gender, ethnicity, socioeconomic status, history of dating violence, and trauma symptoms differentially influenced dating violence perpetration and victimization at 1-year follow-up. Our findings support the need to comprehensively address dating violence among emerging adults. Intimate partner violence prevention and intervention programs may benefit from targeting emerging adults who misuse substances and incorporating substance use interventions into dating violence prevention efforts.

Keywords

emerging adults substance use intimate partner violence trauma symptoms

Dating violence is a prevalent public health problem among adolescents and emerging adults. Nationally representative studies cite physical and sexual dating violence at comparable rates, affecting approximately 1 in 10 adolescents in Grades 9–12 more than a 12-month period (Centers for Disease Control and Prevention, 2013). Psychological dating violence is posited to occur with greater frequency affecting 30% of the adolescents aged 12–21 (Halpern et al., 2001). This prevalence is alarming considering the detrimental effects dating violence has on victims, particularly at such critically important developmental ages. Victims of dating violence have a heightened risk of mental health disorders, increased substance use, greater rates of academic drop out, and lower psychosocial functioning, relative to nonvictims (Banyard & Cross, 2008 ; Brown et al., 2009; Ellis et al., 2009). Negative consequences impel the field to examine the potential risk factors that increase the likelihood of perpetration and victimization, as well as related areas for intervention.

Substance Use and Dating Violence

Substance use is frequently cited as both a consequence of and precursor to dating violence victimization (Nowotny & Graves, 2013; Reingle et al., 2014; Smith et al., 2012) and perpetration (Leonard, 2005; Reyes et al., 2014; Smith et al., 2012; Testa & Brown, 2015). Substance misuse, including misuse of alcohol, marijuana, illicit substances (e.g., cocaine, heroin, amphetamines, hallucinogens), and prescription medication is prevalent among emerging adults (Substance Abuse and Mental Health Services Administration, 2015). While prior research supports the relationship between singular substances and dating violence perpetration and victimization among emerging adults (Haynie et al., 2013; Shorey et al., 2011), there is limited research examining emerging adults’ dating violence as it relates to the use of multiple substances and the particular patterns of substance use.

Emerging adults’ use of multiple substances, or polysubstance use (i.e., the use of two or more substances either simultaneously or concurrently during a given time period), is particularly concerning given its prevalence and its association with problematic behaviors such as aggression (Connor et al., 2014; Gilreath et al., 2014; Kokkevi et al., 2014 McCabe et al., 2006; Wanner et al., 2009). Examination of patterns of polysubstance use among adolescents showed that the number of latent classes tends to range from three to six (Tomczyk et al., 2016). Common latent classes included a no or low substance use class, an alcohol only class with varying severity of alcohol use, and polysubstance use classes, which frequently included alcohol, tobacco, and/or marijuana, with varying severity of use (Tomczyk et al., 2016). Polysubstance use, particularly marijuana and alcohol use and alcohol and prescription drug misuse, is linked to dating violence perpetration and victimization among emerging adults (Espelage et al., 2018; Low et al., 2017; Parker & Bradshaw, 2015). However, research has yet to examine polysubstance use that includes substances such as cocaine, amphetamines, inhalants, hallucinogens, and ecstasy as it relates to dating violence perpetration and victimization.

Substance use may relate to emerging adult’s dating violence victimization and perpetration due to the singular or multiple substance’s pharmacological effects. That is, the disinhibition, impaired cognitive function, increased arousal, and irritability associated with using substances may increase the likelihood for violence perpetration and victimization (Pihl & Hoaken, 1997; Pihl & Peterson, 1995; Virkkunen & Linnoila, 1993; White, 1997). It is also possible that substance use and dating violence cooccur through the tendency that problematic behaviors have a propensity to cluster (Dryfoos, 1990; Jessor, 1991; Pharo et al., 2011). Indeed, polysubstance use is linked to risky sexual behaviors (Connell et al., 2009), frequency of substance use (Conway et al., 2013; Dierker et al., 2007), and dating violence (Parker & Bradshaw, 2015). While substance use is a robust predictor of dating violence, many individuals who use substances do not engage in dating violence, suggesting the need to examine individual risk factors that moderate the (poly) substance use and dating violence relationship (Foran & O’Leary, 2008; Leonard, 2005; Moore et al., 2008; Reyes et al., 2014; Shorey et al., 2011).

Sociodemographic Risk Factors

Sociodemographic factors, including age, gender, parental education, and ethnicity may moderate the association between patterns of substance use and dating violence perpetration and victimization. With respect to age, it may be that substance use has a more substantial impact on neurological functioning earlier in their development (De Bellis et al., 2000), resulting in a stronger relationship between substance use and dating violence. However, it could also be that as age increases, adolescents are more likely to engage in several risky behaviors, resulting in a strong link between substance use and dating violence. These points likely contribute to whether a relationship exists between age and dating violence (Karakurt & Silver, 2013; Reuterman & Burcky, 1989; Tomczyk et al., 2016; Wolitzky-Taylor et al., 2008). Of these mixed results, few studies have examined this relationship, or the moderating impact of age, in samples of adolescents. Considering these relationships will extend our understanding of the patterns and risk factors of adolescent dating violence.

Gender may also be significantly related to the association between substance use and dating violence. In a national representative sample, Haynie et al. (2013) revealed that females involved in dating violence were more likely to smoke cigarettes and use marijuana compared with males who were involving in dating violence. In another study using a national sample, females who used synthetic cannabinoids were more likely to be victims of physical dating violence compared with males who used synthetic cannabinoids (Ihongbe & Masho, 2018). Research also suggests that longitudinal predictors of dating violence among adolescents may have differing impacts across gender (Foshee et al., 2001). For instance, one study indicated that belonging to a peer group with experience in dating violence and alcohol use predicted dating violence perpetration among women whereas only attitudes endorsing the acceptability of dating violence predicted dating violence perpetration for men (Foshee et al., 2001). It has been posited that women may respond to alcohol differently, including being less likely to respond aggressively when drinking while simultaneously putting them at increased risk for victimization (Nolen-Hoeksema, 2004). An examination on how gender affects the relationship between substance use and dating violence will inform prevention and intervention programs.

Ethnicity and socioeconomic status (SES) are also important to consider when examining the link between substance use and dating violence. For instance, research has called for increased accessibility to services for Hispanic individuals as they may be less likely to seek assistance for dating violence relative to others (Lipsky et al., 2006). In addition, dating violence among African American adolescents has shown to occur at higher rates than other ethnic groups (Carver et al., 2003; Halpern et al., 2001; Haynie et al., 2013). Schnurr and Lohman (2008) found that Hispanic males (but not Hispanic females or African American females and males) who used substances in early adolescence were significantly more likely to perpetrate dating violence at 6-year follow up (Schnurr, and Lohman, 2008). Little is known with respect to ethnicity as a moderating variable for the link between substance use and dating violence. This lack of knowledge is surprising given the plethora of studies examining substance use and race/ethnicity (Chen & Jacobson, 2012; Shih et al., 2010) and dating violence and race/ethnicity (Foshee et al., 2010; Rojas-Gaona, et al., 2016), respectively. Similarly, there is a paucity of research examining the moderating role of SES, despite findings that youth from low-income households may have an increased likelihood of experiencing dating violence (Foshee et al., 2009). We addressed these shortcomings in the current study.

Prior experience with dating violence is among the strongest predictors of future dating violence. Longitudinal research showed that for both men and women, those who experience dating violence as an adolescent are more likely to experience adult dating violence victimization (Exner-Cortens et al., 2013). Moreover, studies have shown that dating violence victimization in adolescence predicts dating violence victimization and perpetration in young adulthood (Gómez, 2011), and that prior perpetration predicts future dating violence perpetration (Gidycz et al., 2007). Thus, research is needed to examine how prior dating violence experiences moderate the relationship between patterns of (poly) substance use and dating violence.

Psychosocial Risk Factors

Mental health problems are frequently comorbid with substance use and dating violence victimization and perpetration among adolescents and adults (Kilpatrick et al., 2003; Rhodes et al., 2009; Schneider et al., 2009). Adults with symptoms of depression and post-traumatic stress disorder (PTSD) are more likely to perpetrate and be victims of dating violence than those without these symptoms (Reingle et al., 2014). Hostility has also been associated with dating violence (Birkley & Eckhardt, 2015; Leen et al., 2013) and has been shown to moderate the relationship between alcohol use and marital aggression among men (Leonard & Blane, 1992). While these are well-known risk factors of dating violence, research has yet to examine their role as moderators of (poly) substance use and dating violence.

Current Study Purpose and Hypotheses

We investigate how different patterns of (poly) substance use (i.e., alcohol, tobacco, marijuana, prescription drugs, and other substances) influence dating violence, as well as examine whether sociodemographic (i.e., age, gender, ethnicity, SES) and psychosocial variables (i.e., hostility, PTSD symptoms, depression symptoms) influence dating violence based on patterns of substance use. Given previous research (Tomczyk et al., 2016), we hypothesized that (a) poly (or dual) substance users will report higher dating violence victimization and perpetration relative to subgroups who used no substances or a single substance and (b) sociodemographic and psychosocial risk factors will differentially relate to dating violence victimization and perpetration depending on patterns of substance use.

Method

Participants and Procedure

A total of 698 emerging adults (62.6% female; mean age = 18.06, standard deviation [SD] = 0.79) participated in Wave 5 (spring 2014) and Wave 6 (spring 2015) of an ongoing longitudinal study (N = 1,042). Participants were recruited in 2010 from public high schools throughout southeast Texas (response rate: 62% at Wave 1; see Temple et al., [2013] for more details). Written parental consent and student assent were obtained following institutional review board (IRB) approval from the last author’s institution. Participants reconsented when they reached the age of 18. Beginning with Wave 5, all participants completed web-based surveys and were reimbursed via a gift card to an online retailer in the amounts of US$20 (Wave 5) and US$30 (Wave 6). At Wave 5, participants’ ethnic/racial identities were Hispanic (33.1%), White (27.4%), African American (27.2%), and other (12.3%). Occupational and educational status included attending college/trade school (even if working; 70.2%), working (not in school; 20.2%), not attending school or working (8.9%), and attending high school (0.7%). Most participants lived with their family (56.3%), followed by friends/roommates (28.4%), romantic partners (9.8%), and alone (5.5%). With respect to parent education, 15.9% had less than high school graduation, 16.6% had a high school diploma, 27.8% had some college or training after high school, and 38.7% completed college.

Measures

Yearly substance use (Wave 5; indicators in latent class analysis)

Using a yes/no format, participants reported their past-year use of alcohol, tobacco, marijuana, prescription medication, cocaine, amphetamines, inhalants, hallucinogens, and ecstasy. Due to relatively low prevalence of the latter five substances, we created a dichotomous other “hard” drug use based on positive endorsement of any of these substances.

Monthly substance use (Wave 5; indicators in latent class analysis)

Participants reported the number of days in the past month they used alcohol, marijuana, episodic heavy drinking, prescription medication (i.e., “During the past 30 days, on how many days did you have at least one drink of alcohol/use marijuana/participated in binge drinking/use prescription medication that were not prescribed?”). Episodic heavy drinking was defined as “five or more drinks for boys and four or more drinks for girls.” Exemplars of prescribed medication were provided (“Xanax, Vicodin, Oxycontin, Percocet, Ritalin, Adderall, Ludes, Vitamin R, Handlebars”). To account for constraints in our analyses, we created binary items (yes/no) on past-month substance use. Specifically, when a participant reported alcohol use (or any substance) at least one day in the past month, we coded their response as past-month alcohol (or any specific substance) use.

Dating violence victimization and perpetration (Wave 5 and 1-year follow-up)

Forty-two items from The Conflict in Adolescent Dating Relationships Inventory (Wolfe et al., 2001) measured past-year physical (4 items) and psychological (17 items) victimization (total 21 items) and perpetration (total 21 items) with a yes-or-no format. Example items for victimization include “He/She threatened to end the relationship” and “He/She insulted me with put downs.” Perpetration items included “I spread rumors about him or her,” and “I kicked, hit, or punched him or her.” Because physical and psychological dating violence cooccur (Choi et al., 2017; Choi & Temple, 2016), we summed all 21 items for victimization (range: 0–21) and perpetration (range: 0–21). The internal consistency of the measure was .90 for victimization at both waves, and .87 and .89 for perpetration at Waves 5 and 6, respectively.

Depressive symptoms (Wave 5)

The Center for Epidemiologic Studies Short Depression Scale (Andersen et al., 1994) assessed depressive symptoms using a 4-point scale (1 = “less than 1 day” and 4 = “5–7 days”). Although the survey originally included 10 items, 2 items had poor correlations with other items (e.g., r = −.02) and were thus dropped in favor of higher internal consistency (Nowakowski et al., 2016). We created a composite variable by taking the average score of the eight items, with higher scores suggesting greater depressive symptoms (e.g., “I had trouble keeping my mind on what I was doing”; “I felt depressed”; Cronbach’s α =.82).

Hostility (Wave 5)

Hostility was measured with the six items subscale of the Symptom CheckList-90 (SCL-90; Derogatis, 1983). The six items were averaged, with higher scores suggesting greater hostility. Using a 4-point scale (1 = “never” and 4 = “most of the time”), example items include: In general, how often do you “have temper outbursts you can’t control?,” “have urges to beat, injure, or harm someone?,” and “shout or throw things?” (Cronbach’s α = .79).

Trauma symptoms (Wave 5)

Four items (yes/no format) from the primary care PTSD screen (PC-PTSD; Prins & Ouimette, 2004) were summed to create a composite variable of trauma symptoms. Thus, higher scores indicated greater trauma-related symptoms. Using the premise “Have you had any experience in your lifetime that was so frightening, horrible, or upsetting that, in the PAST MONTH you,” example items included: “Had nightmares about it or thought about it when you did not want to?” and “Tried hard not to think about it or went out of your way to avoid situations that reminded you of it?” The PC-PTSD has been shown to be a valid measure with an acceptable level of internal consistency (Kimerling et al., 2006; Cronbach’s α = .81).

Demographics

Participants reported gender identity, racial/ethnic identity, age, and parents’ highest education (as a proxy for SES). Gender was dummy coded (female = 1 vs. male = 0). For racial/ethnic identity, three dummy-coded variables were created. White participants served as the reference group (e.g., Hispanic = 1 vs. White = 0; African American = 1 vs. White = 0; other races/ethnicities = 1 vs. White = 0).

Analytical Plan

We used latent class analysis (LCA) to identify homogeneous subgroups based on patterns of participant substance use at Wave 5. LCA is a statistical tool used to identify similar patterns of behaviors within subgroups while also differentiating these patterns between subgroups by accounting for measurement error (Collins & Lanza, 2010). That is, LCA can estimate the prevalence of latent classes and the parameters of item–response probabilities (Collins & Lanza, 2010). Because estimates in LCA can be influenced by additional variables such as predictors for membership in classes or distal outcomes (Asparouhov & Muthén, 2014a; Vermunt, 2010), we used a three-step LCA in Mplus 7.4 (Muthén & Muthén, 1998–2015) to answer our research questions. This approach enables the analysis of predictor estimates or outcome estimates without influencing parameters (e.g., item–response probabilities; see Asparouhov & Muthén, 2014a for additional details). To identify latent substance use classes, we included the following nine indicators: past-year use of alcohol, tobacco, marijuana, prescription medication, and hard drugs, as well as past-month use of alcohol, marijuana, episodic heavy drinking, and prescription medication.

First, we used LCA to identify latent substance use classes with these nine indicators. To identify the optimal number of latent classes, we used the (adjusted) Bayesian information criterion (BIC), the adjusted Lo-Mendell–Rubin likelihood ratio test (Lo et al., 2001), and theoretically meaningful interpretations based on substance LCA literature (Tomczyk et al., 2016). Once the optimal number of classes was identified, we compared the means in a distal outcome such that dating violence victimization and perpetration at 1-year follow-up significantly differed across these latent classes using a Wald test with BCT command (Asparouhov & Muthén, 2014b). Finally, we used a manual three-step estimation procedure (Asparouhov & Muthén, 2014a) to examine how gender (reference group: male), race/ethnicity (reference group: White), age, parental education, depressive symptoms, hostility, trauma symptoms, and dating violence perpetration and victimization at Wave 5 influenced dating violence perpetration and victimization at the 1-year follow-up depending on the latent substance classes. This analysis allowed us to explore a moderator role of latent substance use class in a linear regression to predict dating violence victimization and perpetration. We used full information maximum likelihood to handle missing responses (Enders & Bandalos, 2001).

Results

Table 1 includes frequency, means, and standard deviations across all variables of interest. The most frequently used substance in the past year was alcohol (67.2%) followed by marijuana (37.9%), tobacco (24.5%), “hard” drugs (10.8%), and nonmedical use of prescription medication (9.4%). Participants reported monthly alcohol use (52.2%), monthly episodic heavy drinking (33.4%), monthly marijuana use (26.0%), and monthly nonmedical use of prescription medication (6.1%). Perpetration of dating violence slightly increased whereas victimization slightly decreased at the 1-year follow-up.

Table 1.

Descriptive Statistics.

Latent Class Indicators	Response	Frequency (%), M (SD)
Wave 5
Yearly alcohol use	No	227 (32.8%)
Yearly alcohol use	Yes	466 (67.2%)
Yearly tobacco use	No	523 (75.5%)
Yearly tobacco use	Yes	170 (24.5%)
Yearly marijuana use	No	430 (62.1%)
Yearly marijuana use	Yes	262 (37.9%)
Yearly prescribed medicine use	No	627 (90.6%)
Yearly prescribed medicine use	Yes	65 (9.4%)
Yearly other hard drug use	No	617 (89.2%)
Yearly other hard drug use	Yes	75 (10.8%)
Monthly alcohol use	No	331(47.8%)
Monthly alcohol use	Yes	362 (52.2%)
Monthly episodic heavy drinking	No	461 (66.6%)
Monthly episodic heavy drinking	Yes	231 (33.4%)
Monthly marijuana use	No	512 (74.0%)
Monthly marijuana use	Yes	180 (26.0%)
Monthly prescribed medication use	No	620 (93.9%)
Monthly prescribed medication use	Yes	42 (6.1%)
Depressive symptoms	1–4	1.76 (0.62)
Hostility	1–4	1.71 (0.52)
Trauma symptoms	0–4	1.09 (1.42)
Dating violence perpetration	0–21	3.51 (3.86)
Dating violence victimization	0–21	3.78 (4.35)
One-year follow-up
Dating violence perpetration	0–21	3.70 (4.12)
Dating violence victimization	0–21	3.74 (4.31)

Latent Substance Use Class and Different Dating Violence Among These Classes

Latent substance use class

Based on the fit indices (Table 2), five latent substance classes were selected. To test possible gender difference in LCA, we compared men and women using the same nine indicators. The same optimal number of classes emerged for men and women. We also compared one model with varying item–response probabilities among men and women (BIC = 5,873.974) to a model constraining item–response probabilities (BIC = 5,622.758), which indicated that females and males had an equivalent number of classes. Thus, rather than separately investigating LCA among men and women, we used gender as a covariate in the three-step LCA. As shown in Figure 1, the following five substance classes emerged: (a) Regular Alcohol use (n = 178, 25.7%), (b) Polysubstance use (n = 46, 6.6%), (c) Heavy Alcohol and Marijuana use (n = 145, 20.9%), (d) Mild Alcohol use (n = 264, 38.1%), and (e) Occasional Alcohol and Marijuana use (n = 60, 8.7%). The Regular Alcohol use class had high item–response probabilities regarding yearly (1.00) and monthly alcohol use (.96) and a moderate degree of item–response probabilities on episodic heavy drinking (.53). The Polysubstance use class had higher item probabilities across all substances including yearly alcohol (1.00), tobacco (.66), marijuana (.93), nonmedical use of prescription drugs (1.00), and “hard” drugs (.65), as well as monthly alcohol (.97), marijuana (.82), episodic heavy drinking (.82) and nonmedical use of prescription drugs (.86). The Heavy Alcohol and Marijuana use class had a higher item probability on yearly (1.00) and monthly (.99) alcohol use, monthly episodic heavy drinking (.67) and yearly (1.00) and monthly marijuana use (.79). The Mild Alcohol use class had a relatively low item probability of alcohol use (.22) and the lowest item probabilities on the remaining substances. Finally, Occasional Alcohol and Marijuana use class had higher item–response probabilities on yearly marijuana use (.97) and alcohol use (.62) and moderate degree of item–response probability on monthly marijuana use (.48), relative to the other substance indicators.

Table 2.

LCA Fit Index.

Full Sample	AIC	BIC	Adjust BIC	LMRT	Entropy
Model
1-Class solution	6,441.646	6,482.515	6,453.939	N/A	N/A
2-Class solution	5,156.718	5,242.998	5,182.669	1,285.278***	0.95
3-Class solution	4,748.525	4,880.215	4,788.135	421.745***	0.93
4-Class solution	4,584.305	4,761.406	4,637.574	181.446***	0.94
5-Class solution	4,478.718	4,701.228	4,545.645	123.697***	0.95
6-Class solution	4,457.216	4,725.136	4,537.801	40.877*	0.96
Female
1-Class solution	3,883.152	3,919.830	3,891.268	N/A	N/A
2-Class solution	3,057.365	3,134.796	3,074.500	832.091***	0.95
3-Class solution	2,822.540	2,940.725	2,848.695	250.698***	0.93
4-Class solution	2,738.694	2,897.633	2,773.868	102.164	0.95
5-Class solution	2,699.654	2,899.345	2,743.846	58.085***	0.95
6-Class solution	2,688.823	2,929.269	2,742.034	30.331	0.97
Male
1-Class solution	2,551.668	2,583.645	2,555.112	N/A	N/A
2-Class solution	2,083.956	2,151.462	2,091.226	479.085***	0.96
3-Class solution	1,922.108	2,025.144	1,933.204	178.631***	0.94
4-Class solution	1,872.542	2,011.107	1,887.464	68.335	0.94
5-Class solution	1,829.207	2,003.302	1,847.955	62.215	0.94
6-Class solution	1,821.015	2,030.639	1,843.590	27.693*	0.97

Note. LCA = latent class analysis; AIC = Akaike information criterion; BIC = Bayesian information criterion; LMRT = Lo-Mendell–Rubin likelihood ratio test.

p <. 05. ***p <.001.

Figure 1.

Latent substance classes.

Dating violence comparisons among latent substance use classes

At the 1-year follow-up, the Polysubstance use class (M = 4.72, SD = 0.74) had the highest dating violence perpetration scores followed by Heavy Alcohol and Marijuana use (M = 4.23, SD = 0.44), Occasional Alcohol and Marijuana use (M = 4.08, SD = 0.60), Regular Alcohol use (M = 3.66, SD = 0.34), and Mild Alcohol use (M = 3.06, SD = 0.27) classes (see Figure 2). Wald-test showed that the Polysubstance use, χ²(1) = 4.46, p = .04, and Heavy Alcohol and Marijuana use, χ²(1) = 5.22, p = .02, classes had significantly greater dating violence perpetration compared with the Mild Alcohol use class. With respect to victimization, the Polysubstance use class (M = 5.03, SD = 0.79) had the highest dating violence victimization scores followed by the Occasional Alcohol and Marijuana use (M = 4.61, SD = 0.72), Heavy Alcohol and Marijuana use (M = 3.88, SD = 0.39), Regular Alcohol use (M = 3.68, SD = 0.36), and Mild Alcohol use (M = 3.21, SD = 0.29) classes. The Wald-test showed that the Polysubstance use class had significantly greater dating violence victimization scores compared with the Mild Alcohol use class, χ²(1) = 4.72, p = .03. The Occasional Alcohol and Marijuana use class had marginally greater victimization scores relative to the Mild Alcohol use class, χ²(1) = 3.25, p =.07.

Figure 2.

Perpetration and victimization scores each substance use class.

Sociodemographic and psychosocial variables and dating violence among latent substance use classes

Gender, race/ethnicity, parental education, age, depressive symptoms, trauma symptoms, hostility, and dating violence victimization and perpetration at Wave 5 were included as covariates in the LCA to examine how these variables influence dating violence perpetration (BIC = 23,262.62) and victimization (BIC = 23,338.07) at the 1-year follow-up (see Table 3 for perpetration and Table 4 for victimization). In the Regular Alcohol use class, participants with higher dating violence perpetration scores at Wave 5 (B = 0.66, standard error [SE] = 0.17, p <.001) were more likely to perpetrate dating violence at 1-year follow up, whereas participants reporting greater dating violence victimization at Wave 5 (B = 0.45, SE = 0.21, p <.05) were more likely to experience dating violence victimization at the 1-year follow-up.

Table 3.

Moderator Effects of Each Class on Dating Violence Perpetration.

Sociodemographic and Psycho-social variables	Perpetration
	Regular alcohol use		Polysubstance use		Heavy alcohol and marijuana use		Mild alcohol use		Occasional alcohol and marijuana use
	B	SE	B	SE	B	SE	B	SE	B	SE
Female	0.36	0.54	0.78	2.03	0.13	1.09	−0.08	0.69	2.24*	1.00
Hispanic	1.39	0.81	2.04	1.85	1.23	1.54	−1.08	0.62	−2.90	1.78
African American	0.31	0.73	−1.05	1.74	2.19*	1.00	−0.52	0.72	−0.01	1.84
Other ethnics	−0.63	0.72	−3.68**	1.33	2.29	1.40	−0.26	0.93	−5.57*	2.37
Parental education	0.26	0.36	−0.58	0.63	0.39	0.54	−0.23	0.21	−1.54**	0.50
Age	−0.16	0.32	−1.07	1.09	−0.00	0.44	0.20	0.26	−0.80	0.51
Dating violence perpetration at Wave 5	0.66***	0.17	0.32	0.27	0.64*	0.27	0.60***	0.10	0.32	0.56
Dating violence victimization at Wave 5	−0.06	0.17	−0.33	0.20	−0.14	0.21	−0.14	0.12	0.21	0.53
Depressive symptoms	−0.71	0.53	−1.64	1.34	−0.02	0.77	0.82	1.03	0.61	0.79
Trauma symptoms	−0.05	0.26	1.61**	0.49	−0.08	0.36	−0.12	0.25	−0.02	0.41
Hostility	1.60	0.88	1.15	2.50	0.94	1.09	1.17	0.73	0.31	0.83

Note. White participants were the reference group for racial/ethnic comparisons. Men were the reference group for gender comparison.

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

Table 4.

Moderator Effects of Each Class on Dating Violence Victimization.

Sociodemographic and Psycho-social variables	Victimization
	Regular alcohol use		Polysubstance use		Heavy alcohol and marijuana use		Mild alcohol use		Occasional alcohol and marijuana use
	B	SE	B	SE	B	SE	B	SE	B	SE
Female	−0.19	0.57	0.15	1.43	−0.59	0.94	−0.47	0.74	1.61	1.22
Hispanic	0.29	0.74	−0.26	1.35	1.49	1.07	−1.11	0.71	−2.33	1.66
African American	−0.21	0.73	−1.69	2.06	1.92	1.35	−0.08	0.84	0.68	1.87
Other ethnics	−0.69	0.80	−4.61**	1.46	1.95	1.40	−0.66	0.93	6.52	6.03
Parental education	0.00	0.30	−0.99	0.59	0.47	0.69	−0.29	0.24	−1.00*	0.50
Age	−0.62	0.33	−0.92	0.90	0.12	0.78	−0.17	0.27	−0.55	0.77
Dating violence perpetration at Wave 5	0.27	0.20	0.38	0.25	0.29	0.16	0.04	0.15	0.52	0.69
Dating violence victimization at Wave 5	0.45*	0.21	−0.26	0.18	−0.03	0.15	0.44*	0.19	0.06	0.69
Depressive symptoms	−0.66	0.58	−0.88	1.13	0.44	1.32	1.03	0.99	0.59	1.01
Trauma symptoms	0.10	0.25	1.78***	0.43	−0.07	0.28	−0.15	0.28	0.20	0.51
Hostility	1.26	0.75	−0.80	1.52	0.65	0.63	0.40	0.65	2.52	1.44

Note. White participants were the reference group for racial/ethnic comparisons. Men were the reference group for gender comparison.

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

In the Polysubstance use class, White participants were more likely to experience dating violence—both perpetration (B = −3.68, SE = 1.33, p <.01; reference group: White) and victimization (B = −4.61, SE = 1.46, p <.01)—compared with “others” in the same class. Furthermore, in the Polysubstance use class, participants with greater trauma symptoms scores were more likely to be victims (B = 1.78, SE = 0.43, p <.001) and perpetrators (B = 1.61, SE = 0.49, p <.01) of dating violence compared with youth with lower trauma symptoms scores. In the Heavy Alcohol and Marijuana use class, African American (B = 2.19, SE = 1.00, p <.05) participants and participants with greater dating violence perpetration experiences at Wave 5 (B = 0.64, SE = 0.21, p <.05) reported greater dating violence perpetration scores at 1-year follow-up, relative to White participants and participants with less perpetration of dating violence in the same class.

In the Mild Alcohol use class, participants with higher dating violence perpetration scores at Wave 5 (B = 0.60, SE = 0.10, p <.001) were more likely to perpetrate dating violence, whereas youth reporting greater dating violence victimization at Wave 5 (B = 0.44, SE = 0.19, p <.05) were more likely to experience dating violence at 1-year follow-up relative to counterparts in the same class. Finally, in the Occasional Alcohol and Marijuana use class, women participants (B = 2.24, SE = 1.00, p<.05), White participants (B = −5.57, SE = 2.37, p <.01), and participants with lower parental education (B = −1.54, SE = 0.50, p <.01) were more likely to perpetrate dating violence compared with their counterparts in the same class. Also, participants whose parents had lower levels of education (B = −1.00, SE = .05, p <.05) were more likely to be victims of dating violence 1 year later, compared with their same-class counterparts.

Discussion

This study extends prior research by examining how different patterns of substance use influenced dating violence and how sociodemographic variables (e.g., gender identity, race/ethnicity) and psychosocial variables (e.g., hostility, trauma-related, and depressive symptoms) could exacerbate these influences. This study also expands prior studies by examining latent classes of polysubstance use as it relates to dating violence by including drugs not previously included (i.e., cocaine, amphetamines, inhalants, hallucinogens, and ecstasy). The latent classes that emerged included Polysubstance use, Regular Alcohol use, Mild Alcohol use, Heavy Alcohol and Marijuana use, and Occasional Alcohol and Marijuana use classes. These classes are largely consistent with those found in the prior research (Tomczyk et al., 2016). A systematic review of 23 studies (Tomczyk et al., 2016) concluded that two latent classes are commonly found: a low substance use class and a polysubstance use class. This review demonstrated that the alcohol onlyuse class is commonly illustrated in most studies. We extended this finding by revealing Heavy and Occasional Alcohol and Marijuana use classes. This finding might reflect the current adolescent substance use trend. Although traditionally alcohol has been the most common and accessible substance compared with other drugs, marijuana has been increasingly accessible. Furthermore, the perceived harmfulness of marijuana use has decreased as its widespread legalization in the United States (Keyes et al., 2016; Cerdá et al., 2017). This might account for the emergence of the alcohol and marijuana classes.

Polysubstance users, as well as alcohol and marijuana users, were at greatest risk for dating violence victimization and perpetration compared with the Mild Alcohol use class. This is consistent with the previous research. For example, in a diverse sample of high-school adolescents in Maryland, prior dating violence victimization resulted in an increased likelihood of polysubstance use (i.e., alcohol, marijuana, nicotine, and prescription drugs; Parker & Bradshaw, 2015). Adolescents who were polysubstance users of both alcohol and prescription drugs, compared with those who only used alcohol, were more likely to perpetrate and be victims of dating violence (Espelage et al., 2018). While little research has examined mechanisms underlying the link between polysubstance use and dating violence victimization and perpetration, theories related to the role of substance use, in general, in dating victimization and perpetration are common. As the use of substances alters judgment, it is possible that emerging adults who use substances have an impaired ability to recognize and defend themselves from dating situations (Malik et al., 1997) or to misinterpret behaviors as threatening and react in kind (Rothman et al., 2012). This notion is consistent with empirical work demonstrating a temporal relationship between substance misuse and dating violence victimization and perpetration (Moore et al., 2011; Shorey et al., 2016; Stuart et al., 2013; Temple et al., 2013). Given deficits in multiple areas of executive functioning found among polysubstance users (Verdejo-Garcıa et al., 2005), it is possible that polysubstance using emerging adults are at a greater susceptibility to dating violence perpetration and victimization due to increased impairments.

Dating violence victimization and perpetration differed among ethnic groups, indicating that some individuals may be at increased or decreased risk depending on patterns of substance use. Our finding showed that being White increased dating violence victimization and perpetration in the Polysubstance use class and the Occasional Alcohol and Marijuana use class. Prior research highlights the inconsistency of findings related to race/ethnicity and dating violence victimization and perpetration, with some studies finding a particular racial/ethnic group as being a risk factor for and other studies categorize the same group as a protective factor against dating violence (Dardis et al., 2015; Vezina & Hebert, 2007). While it is unclear what accounts for race/ethnicity differences in dating violence victimization and perpetration among emerging adults, the larger research literature suggests that differences in help-seeking behavior may help explain these differences (Lipsky & Caetano, 2007; Lipsky et al., 2006). For example, among victims of dating violence, African American women, relative to other groups, were more likely to utilize help-seeking behaviors (e.g., police assistance) and non-Hispanic White women and African American women were more likely to utilize emergency services relative to their Hispanic counterparts (Lipsky et al., 2006). White youth have been shown to be more likely to receive treatment for substance use disorders compared with their African American and Hispanic counterparts (Alegria et al., 2011; Cummings et al., 2011). These differences in treatment and help seeking coupled with the strong link between substance use and dating violence (Leonard, 2005; Reyes et al., 2014; Smith et al., 2012; Testa & Brown, 2015) could account for our finding that African Americans in the Heavy Alcohol and Marijuana use classes (compared with their White counterparts) reported more perpetration of dating violence at the 1-year follow-up.

Parental education was inversely associated with dating perpetration and victimization in the Occasional Alcohol and Marijuana use class, which suggests that emerging adults from lower SES households were at an increased likelihood of perpetrating and being victims of dating violence even though they used less substances. This suggests that variables other than substance misuse are important in predicting dating violence. Among those in the Occasional Alcohol and Marijuana use class, women were at a greater risk for dating violence perpetration, suggesting that alcohol may be a more salient risk factor for women compared with men (Foshee et al., 2001).

Emerging adults with a history of perpetrating dating violence in the Regular Alcohol use, Heavy Alcohol and Marijuana use, and Mild Alcohol use classes were more likely to perpetrate violence over time, even though their perpetration scores were lower at baseline than emerging adults in the Polysubstance use class. In addition, emerging adults with a history of dating violence victimization in the Regular Alcohol use and Mild Alcohol use classes were more vulnerable to future dating violence victimization. This is consistent with the past findings that prior experiences with dating violence predict future experiences with dating violence (Exner-Cortens et al., 2013; Gidycz et al., 2007). These findings lend support to the pharmacological effects of alcohol hindering cognitive and attentional resources (Steele & Josephs, 1990) leading to dating violence perpetration or that victims of dating violence may be more likely to use alcohol to cope with erstwhile violence (Kaysen et al., 2007).

Among individuals engaging in polysubstance use, those with more trauma-related symptoms were more likely to be victims and perpetrators of dating violence at follow-up. Prior research has found that trauma mediates the link between dating violence as an adolescent and dating violence as an emerging adult (Jouriles et al., 2017). Furthermore, trauma symptoms secondary to childhood maltreatment predict future dating violence perpetration and victimization (Wolfe et al., 2004). Trauma has also been linked to substance use (Sullivan et al., 2016), which is related to increased risk for victimization and perpetration (Espelage et al., 2018; Testa et al., 2003).

Age did not moderate the relationship between substance use classes and experiences with dating violence. It is possible that age did not emerge as an important factor given our exclusive focus on emerging adults, who are similar neurodevelopmentally and in their risk for engaging in risky behaviors (Spear, 2000). Symptoms of depression also did not emerge as a moderator of the link between substance use and dating violence. It may be that these symptoms are less salient risk factors as trauma-related symptoms. Previous research has demonstrated that symptoms of depression only affect certain forms of dating violence (e.g., psychological abuse), while trauma-related symptoms were linked to all forms of dating violence (Eshelman & Levendosky, 2012). The fact that we only assessed depression symptoms in the past week may also have affected results. Finally, hostility did not emerge as a moderator, suggesting that other factors (e.g., substance use; Stith et al., 2004) may be more important in predicting physical dating violence.

Limitations

Findings should be considered several limitations. First, we limited analyses to presence/absence of physical and psychological dating violence. Including other forms (e.g., sexual assault, stalking) and the intensity/frequency of dating violence would have strengthened our understanding of how patterns of substance use influence victimization and perpetration given different individual characteristics (e.g., gender, PTSD). Second, despite the cooccurrence of physical and psychological dating violence (Choi et al., 2017; Choi & Temple, 2016), it is possible that patterns of substance use differently influence people who experience either of these forms of violence alone. Third, while several covariates were included (e.g., ethnicity, SES, mental health), other possible variables may also be important in explaining the current finding. For instance, dysregulated emotion has been associated with post-traumatic stress, depressive symptoms, and substance use (Bekh et al., 2011). Finally, the actual quantity of substances used (e.g., frequency or amount) was not considered. It is likely that emerging adults with greater frequency and amount of alcohol use have a greater likelihood of victimization relative to those who used multiple substances once.

Implications and Conclusion

Our findings support the need to comprehensively research dating violence among emerging adults, and that interventions may need to be individually tailored. For example, individuals with a history of polysubstance use may be less likely to perpetrate dating violence if interventions target their symptoms of trauma. Conversely, to effectively reduce dating violence among individuals who regularly use alcohol and/or marijuana we may need to target their substance use. For individuals with limited alcohol use, prior dating violence emerged as the most important predictor of future dating violence; interventions targeting substance use for these individuals may do little to prevent future dating violence.

Taken together, our findings provide additional evidence linking substance misuse and violence (Duke et al., 2018; Stith et al., 2004) and add to the growing call that prevention programs should be multifaceted and targeted the shared risk and protective factors of multiple problem behaviors (e.g., Fourth R; Wolfe et al., 2009). Furthermore, our findings suggest that interventions may be strengthened by tailoring programming to subgroups based on ethnicity and/or gender. Results also emphasize the potential need to treat the comorbid problems of substance misuse and mental health (e.g., PTSD), especially among poly substance users.

Footnotes

Declaration of Conflicting Interests

The author(s) 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: This research was supported by Award Number K23HD059916 (PI: Temple) from the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD) and 2012-WG-BX-0005 (PI: Temple) from the National Institute of Justice (NIJ). The content is solely the responsibility of the authors and does not necessarily represent the official views of NICHD or NIJ. This work would not have been possible without the permission and assistance of the schools and school districts.

ORCID iDs

Hye Jeong Choi

Jeff. R. Temple

Author Biographies

Hye Jeong Choi, PhD, is an assistant professor at University of Missouri-Columbia. Her research focuses on adolescent dating violence and emphasizes evaluating and designing community-based prevention programs. She is particularly interested in adolescent informal and/or social media communication within prevention programs.

Hannah Grigorian, MA, is a clinical psychology doctoral student at the University of Tennessee. Her research interests include factors that increase the risk for intimate partner violence perpetration and victimization such as sleep quality, emotion dysregulation, and substance use.

Alisa Garner, MA, is a clinical psychology doctoral student at the University of Tennessee. She received her BS from Weber State University and MA from Midwestern State University. Her research interests include risk factors for engagement in sexual risk-taking and perpetration of sexual aggression and coercion.

Gregory L. Stuart, PhD, is a professor of psychology at the University of Tennessee-Knoxville. He is also an adjunct professor in the Department of Psychiatry and Human Behavior at the Warren Alpert Medical School of Brown University. His research focuses primarily on the comorbidity of intimate partner violence and substance misuse. He is particularly interested in interventions that address both substance use and relationship aggression.

Jeff. R. Temple, PhD, is a professor and director of Behavioral Health and Research in the Department of Obstetrics and Gynecology at the University of Texas Medical Branch in Galveston. His research focuses on interpersonal relationships, with a particular emphasis on understanding factors related to the onset, course, consequences, prevention, and intervention of partner violence.

References

Alegria

Carson

N. J.

Goncalves

Keefe

(2011). Disparities in treatment for substance use disorders and co-occurring disorders for ethnic/racial minority youth. Journal of the American Academy of Child & Adolescent Psychiatry, 50(1), 22–31.

Andersen

Vestergaard

Lauritzen

(1994). Effective treatment of poststroke depression with the selective serotonin reuptake inhibitor citalopram. Stroke, 25(6), 1099–1104.

Asparouhov

Muthén

(2014a). Auxiliary variables in mixture modeling: Three-step approaches using M plus. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 329–341.

Asparouhov

Muthén

(2014b). Auxiliary variables in mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary secondary model. Mplus Web Notes, 21(2), 1–22.

Banyard

V. L.

Cross

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

Bekh

B. D.

DeFife

J. A.

Guarnaccia

Phifer

M. J.

Fani

M. N.

Ressler

K. J.

Westen

(2011). Emotion dysregulation and negative affect: Association with psychiatric symptoms. Journal of Clinical Psychiatry, 72(5), 685–691.

Birkley

E. L.

Eckhardt

C. I.

(2015). Anger, hostility, internalizing negative emotions, and intimate partner violence perpetration: A meta-analytic review. Clinical Psychology Review, 37, 40–56.

Brown

Cosgrave

Killackey

Purcell

Buckby

Yung

A. R.

(2009). The longitudinal association of adolescent dating violence with psychiatric disorders and functioning. Journal of Interpersonal Violence, 24(12), 1964–1979.

Carver

Joyner

Udry

J. R.

(2003). National estimates of adolescent romantic relationships. In Florsheim

(Ed.), Adolescent romantic relations and sexual behavior: Theory, research, and practical implications (pp. 23–56). Lawrence Erlbaum.

10.

Centers for Disease Control and Prevention. (2013). Surveillance summaries: Youth risk behavior surveillance: United States, 2013. Morbidity and Mortality Weekly Report63(4). https://www.cdc.gov/mmwr/pdf/ss/ss6304.pdf

11.

Cerdá

Wall

Feng

Keyes

K. M.

Sarvet

Schulenberg

… Hasin

D. S.

(2017). Association of state recreational marijuana laws with adolescent marijuana use. JAMA Pediatrics, 171(2), 142–149.

12.

Chen

Jacobson

K. C.

(2012). Developmental trajectories of substance use from early adolescence to young adulthood: Gender and racial/ethnic differences. Journal of Adolescent Health, 50(2), 154–163.

13.

Choi

H. J.

Temple

J. R.

(2016). Do gender and exposure to interparental violence moderate the stability of teen dating violence? Latent transition analysis. Prevention Science, 17(3), 367–376.

14.

Choi

H. J.

Weston

Temple

J. R.

(2017). A three-step latent class analysis to identify how different patterns of teen dating violence and psychosocial factors influence mental health. Journal of Youth and Adolescence, 46(4), 854–866.

15.

Collins

L. M.

Lanza

S. T.

(2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences (Vol. 718). John Wiley & Sons.

16.

Connell

C. M.

Gilreath

T. D.

Hansen

N. B.

(2009). A multiprocess latent class analysis of the co-occurrence of substance use and sexual risk behavior among adolescents. Journal of Studies on Alcohol and Drugs, 70(6), 943–951.

17.

Connor

J. P.

Gullo

M. J.

White

Kelly

A. B.

(2014). Polysubstance use: Diagnostic challenges, patterns of use and health. Current Opinion in Psychiatry, 27(4), 269–275.

18.

Conway

K. P.

Vullo

G. C.

Nichter

Wang

Compton

W. M.

Iannotti

R. J.

Simons-Morton

(2013). Prevalence and patterns of polysubstance use in a nationally representative sample of 10th graders in the United States. Journal of Adolescent Health, 52(6), 716–723.

19.

Cummings

J. R.

Wen

Druss

B. G.

(2011). Racial/ethnic differences in treatment for substance use disorders among US adolescents. Journal of the American Academy of Child & Adolescent Psychiatry, 50(12), 1265–1274.

20.

Dardis

C. M.

Dixon

K. J.

Edwards

K. M.

Turchik

J. A.

(2015). An examination of the factors related to dating violence perpetration among young men and women and associated theoretical explanations: A review of the literature. Trauma, Violence, & Abuse, 16(2), 136–152.

21.

De Bellis

M. D.

Clark

D. B.

Beers

S. R.

Soloff

P. H.

Boring

A. M.

Hall

… Keshavan

M. S

. (2000). Hippocampal volume in adolescent-onset alcohol use disorders. American Journal of Psychiatry, 157(5), 737–744.

22.

Derogatis

L. R.

(1983). Description and bibliography for the SCL-90-R and other instruments of the psychopathology rating scale series. Johns Hopkins University School of Medicine.

23.

Dierker

L. C.

Vesel

Sledjeski

E. M.

Costello

Perrine

(2007). Testing the dual pathway hypothesis to substance use in adolescence and young adulthood. Drug and Alcohol Dependence, 87(1), 83–93.

24.

Dryfoos

J. G.

(1990). Adolescents at risk: Prevalence and prevention. Oxford University Press.

25.

Duke

A. A.

Smith

K. M.

Oberleitner

Westphal

McKee

S. A.

(2018). Alcohol, drugs, and violence: A meta-meta-analysis. Psychology of Violence, 8(2), 238–249.

26.

Ellis

W. E.

Crooks

C. V.

Wolfe

D. A.

(2009). Relational aggression in peer and dating relationships: Links to psychological and behavioral adjustment. Social Development, 18(2), 253–269.

27.

Enders

C. K.

Bandalos

D. L.

(2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 8(3), 430–457.

28.

Eshelman

Levendosky

A. A.

(2012). Dating violence: Mental health consequences based on type of abuse. Violence and Victims, 27(2), 215–228.

29.

Espelage

D. L.

Davis

J. P.

Basile

K. C.

Rostad

W. L.

Leemis

R. W.

(2018). Alcohol, prescription drug misuse, sexual violence, and dating violence among high school youth. Journal of Adolescent Health, 63(5), 601–607.

30.

Exner-Cortens

Eckenrode

Rothman

(2013). Longitudinal associations between teen dating violence victimization and adverse health outcomes. Pediatrics, 131(1), 71–78.

31.

Foran

H. M.

O’Leary

K. D.

(2008). Alcohol and intimate partner violence: A meta-analytic review. Clinical Psychology Review, 28(7), 1222–1234.

32.

Foshee

V. A.

Linder

MacDougall

J. E.

Bangdiwala

(2001). Gender differences in the longitudinal predictors of adolescent dating violence. Preventive Medicine, 32(2), 128–141.

33.

Foshee

V. A.

McNaughton Reyes

H. L.

Ennett

S. T.

(2010). Examination of sex and race differences in longitudinal predictors of the initiation of adolescent dating violence perpetration. Journal of Aggression, Maltreatment & Trauma, 19(5), 492–516.

34.

Foshee

V. A.

Reyes

M. L.

Wyckoff

(2009). Approaches to preventing psychological, physical, and sexual partner abuse. In O’Leary

K. D.

Woodin

E. M.

(Eds.), Psychological and physical aggression in couples: Causes and interventions (pp. 165–189). American Psychological Association.

35.

Gidycz

C. A.

Warkentin

J. B.

Orchowski

L. M.

(2007). Predictors of perpetration of verbal, physical, and sexual violence: A prospective analysis of college men. Psychology of Men & Masculinity, 8(2), 79–94.

36.

Gilreath

T. D.

Astor

R. A.

Estrada

J. N.

Johnson

R. M.

Benbenishty

Unger

J. B.

(2014). Substance use among adolescents in California: A latent class analysis. Substance Use & Misuse, 49(1–2), 116–123.

37.

Gómez

A. M.

(2011). Testing the cycle of violence hypothesis: Child abuse and adolescent dating violence as predictors of intimate partner violence in young adulthood. Youth & Society, 43(1), 171–192.

38.

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.

39.

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.

40.

Ihongbe

T. O.

Masho

S. W.

(2018). Gender differences in the association between synthetic cannabinoid use and teen dating violence victimization. Violence and Gender, 5(2), 103–109.

41.

Jessor

(1991). Risk behavior in adolescence: A psychosocial framework for understanding and action. Journal of Adolescent Health, 12(8), 597–605.

42.

Jouriles

E. N.

Choi

H. J.

Rancher

Temple

J. R.

(2017). Teen dating violence victimization, trauma symptoms, and revictimization in early adulthood. Journal of Adolescent Health, 61(1), 115–119.

43.

Karakurt

Silver

K. E.

(2013). Emotional abuse in intimate relationships: The role of gender and age. Violence and Victims, 28(5), 804–821.

44.

Kaysen

Dillworth

T. M.

Simpson

Waldrop

Larimer

M. E.

Resick

P. A.

(2007). Domestic violence and alcohol use: Trauma-related symptoms and motives for drinking. Addictive Behaviors, 32(6), 1272–1283.

45.

Keyes

K. M.

Wall

Cerdá

Schulenberg

O’Malley

P. M.

Galea

… Hasin

D. S.

(2016). How does state marijuana policy affect U.S. youth? Medical marijuana laws, marijuana use and perceived harmfulness: 1991–2014. Addiction, 111(12), 2187–2195.

46.

Kilpatrick

D. G.

Ruggiero

K. J.

Acierno

Saunders

B. E.

Resnick

H. S.

Best

C. L.

(2003). Violence and risk of PTSD, major depression, substance abuse/dependence, and comorbidity: Results from the National Survey of Adolescents. Journal of Consulting and Clinical Psychology, 71(4), 692–700.

47.

Kimerling

Ouimette

Prins

Nisco

Lawler

Cronkite

Moos

R. H.

(2006). Brief report: Utility of a short screening scale for DSM-IV PTSD in primary care. Journal of General Internal Medicine, 21(1), 65–67.

48.

Kokkevi

Kanavou

Richardson

Fotiou

Papadopoulou

Monshouwer

… Olszewski

(2014). Polydrug use by European adolescents in the context of other problem behaviours. Nordic Studies on Alcohol and Drugs, 31(4), 323–342.

49.

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.

50.

Leonard

K. E.

(2005). Alcohol and intimate partner violence: When can we say that heavy drinking is a contributing cause of violence?Addiction, 100(4), 422–425.

51.

Leonard

K. E.

Blane

H. T.

(1992). Alcohol and marital aggression in a national sample of young men. Journal of Interpersonal Violence, 7(1), 19–30.

52.

Lipsky

Caetano

(2007). The role of race/ethnicity in the relationship between emergency department use and intimate partner violence: Findings from the 2002 National Survey on Drug Use and Health. American Journal of Public Health, 97(12), 2246–2252.

53.

Lipsky

Caetano

Field

C. A.

Larkin

G. L.

(2006). The role of intimate partner violence, race, and ethnicity in help-seeking behaviors. Ethnicity and Health, 11(1), 81–100.

54.

Mendell

N. R.

Rubin

D. B.

(2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767–778.

55.

Low

Tiberio

S. S.

Shortt

J. W.

Capaldi

D. M.

Eddy

J. M.

(2017). Associations of couples’ intimate partner violence in young adulthood and substance use: A dyadic approach. Psychology of Violence, 7(1), 120–127.

56.

Malik

Sorenson

S. B.

Aneshensel

C. S.

(1997). Community and dating violence among adolescents: Perpetration and victimization. Journal of Adolescent Health, 21(5), 291–302.

57.

Mccabe

S. E.

Cranford

J. A.

Morales

Young

(2006). Simultaneous and concurrent polydrug use of alcohol and prescription drugs: Prevalence, correlates, and consequences. Journal of Studies on Alcohol, 67(4), 529–537.

58.

Moore

B. C.

Easton

C. J.

McMahon

T. J.

(2011). Drug abuse and intimate partner violence: A comparative study of opioid-dependent fathers. American Journal of Orthopsychiatry, 81(2), 218–227.

59.

Moore

T. M.

Stuart

G. L.

Meehan

J. C.

Rhatigan

Hellmuth

J. C.

Keen

S. M.

(2008). Drug abuse and aggression between intimate partners: A meta-analytic review. Clinical Psychology Review, 28(2), 247–274.

60.

Muthén

L. K.

Muthén

B. O.

(1998–2015). Mplus user’s guide (7th ed.).

61.

Nolen-Hoeksema

(2004). Gender differences in risk factors and consequences for alcohol use and problems. Clinical Psychology Review, 24(8), 981–1010.

62.

Nowakowski

Choi

H. J.

Meers

Temple

J. R.

(2016). Inadequate sleep as a mediating variable between exposure to interparental violence and depression severity in adolescents. Journal of Child & Adolescent Trauma, 9(2), 109–114.

63.

Nowotny

K. M.

Graves

J. L.

(2013). Substance use and intimate partner violence victimization among White, African American, and Latina women. Journal of Interpersonal Violence, 28(17), 3301–3318.

64.

Parker

E. M.

Bradshaw

C. P.

(2015). Teen dating violence victimization and patterns of substance use among high school students. Journal of Adolescent Health, 57(4), 441–447.

65.

Pharo

Sim

Graham

Gross

Hayne

(2011). Risky business: Executive function, personality, and reckless behavior during adolescence and emerging adulthood. Behavioral Neuroscience, 125(6), 970–978.

66.

Pihl

R. O.

Hoaken

P. N. S.

(1997). Clinical correlates and predictors of violence in patients with substance use disorders. Psychiatric Annals, 27(11), 735–740.

67.

Pihl

R. O.

Peterson

(1995). Drugs and aggression: Correlations, crime and human manipulative studies and some proposed mechanisms. Journal of Psychiatry and Neuroscience, 20(2), 141–149.

68.

Prins

Ouimette

(2004). The primary care PTSD screen (PC-PTSD): Development and operating characteristics.Primary Care Psychiatry, 9(4), 151.

69.

Reingle

J. M.

Jennings

W. G.

Connell

N. M.

Businelle

M. S.

Chartier

(2014). On the pervasiveness of event-specific alcohol use, general substance use, and mental health problems as risk factors for intimate partner violence. Journal of Interpersonal Violence, 29(16), 2951–2970.

70.

Reuterman

N. A.

Burcky

W. D.

(1989). Dating violence in high school: A profile of the victims. Psychology: A Journal of Human Behavior, 26(4), 1–9.

71.

Reyes

H. L. M.

Foshee

V. A.

Bauer

D. J.

Ennett

S. T.

(2014). Proximal and time-varying effects of cigarette, alcohol, marijuana and other hard drug use on adolescent dating aggression. Journal of Adolescence, 37(3), 281–289.

72.

Rhodes

K. V.

Houry

Cerulli

Straus

Kaslow

N. J.

McNutt

L. A.

(2009). Intimate partner violence and comorbid mental health conditions among urban male patients. The Annals of Family Medicine, 7(1), 47–55.

73.

Rojas-Gaona

C. E.

Hong

J. S.

Peguero

A. A.

(2016). The significance of race/ethnicity in adolescent violence: A decade of review, 2005–2015. Journal of Criminal Justice, 46, 137–147.

74.

Rothman

E. F.

McNaughton Reyes

Johnson

R. M.

LaValley

(2012). Does the alcohol make them do it? Dating violence perpetration and drinking among youth. Epidemiologic Reviews, 34(1), 103–119.

75.

Schneider

Burnette

M. L.

Ilgen

M. A.

Timko

(2009). Prevalence and correlates of intimate partner violence victimization among men and women entering substance use disorder treatment. Violence and Victims, 24(6), 744–756.

76.

Schnurr

M. P.

Lohman

B. J.

(2008). How much does school matter? An examination of adolescent dating violence perpetration. Journal of Youth and Adolescence, 37(3), 266–283.

77.

Shih

R. A.

Miles

J. N.

Tucker

J. S.

Zhou

A. J.

D’Amico

E. J.

(2010). Racial/ethnic differences in adolescent substance use: Mediation by individual, family, and school factors. Journal of Studies on Alcohol and Drugs, 71(5), 640–651.

78.

Shorey

R. C.

Moore

T. M.

McNulty

J. K.

Stuart

G. L.

(2016). Do alcohol and marijuana increase the risk for female dating violence victimization? A prospective daily diary investigation. Psychology of Violence, 6(4), 509–518.

79.

Shorey

R. C.

Stuart

G. L.

Cornelius

T. L.

(2011). Dating violence and substance use in college students: A review of the literature. Aggression and Violent Behavior, 16(6), 541–550.

80.

Smith

P. H.

Homish

G. G.

Leonard

K. E.

Cornelius

J. R.

(2012). Intimate partner violence and specific substance use disorders: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions. Psychology of Addictive Behaviors, 26(2), 236–245.

81.

Spear

L. P.

(2000). The adolescent brain and age-related behavioral manifestations. Neuroscience & Biobehavioral Reviews, 24(4), 417–463.

82.

Steele

C. M.

Josephs

R. A.

(1990). Alcohol myopia: Its prized and dangerous effects. American Psychologist, 45(8), 921–933.

83.

Stith

S. M.

Smith

D. B.

Penn

C. E.

Ward

D. B.

Tritt

(2004). Intimate partner physical abuse perpetration and victimization risk factors: A meta-analytic review. Aggression and Violent Behavior, 10(1), 65–98.

84.

Stuart

G. L.

Moore

T. M.

Elkins

S. R.

O’Farrell

T. J.

Temple

J. R.

Ramsey

S. E.

Shorey

R. C.

(2013). The temporal association between substance use and intimate partner violence among women arrested for domestic violence. Journal of Consulting and Clinical Psychology, 81(4), 681–690.

85.

Substance Abuse and Mental Health Services Administration. (2016). 2015 National Survey on Drug Use and Health: Detailed Tables. Substance Abuse and Mental Health Services Administration.

86.

Sullivan

T. P.

Weiss

N. H.

Flanagan

J. C.

Willie

T. C.

Armeli

Tennen

(2016). PTSD and daily co-occurrence of drug and alcohol use among women experiencing intimate partner violence. Journal of Dual Diagnosis, 12(1), 36–42.

87.

Temple

J. R.

Shorey

R. C.

Fite

Stuart

G. L.

V. D.

(2013). Substance use as a longitudinal predictor of the perpetration of teen dating violence. Journal of Youth and Adolescence, 42(4), 596–606.

88.

Testa

Brown

W. C.

(2015). Does marijuana use contribute to intimate partner aggression? A brief review and directions for future research. Current Opinion in Psychology, 5, 6–12.

89.

Testa

Livingston

J. A.

Leonard

K. E.

(2003). Women’s substance use and experiences of intimate partner violence: A longitudinal investigation among a community sample. Addictive Behaviors, 28(9), 1649–1664.

90.

Tomczyk

Isensee

Hanewinkel

(2016). Latent classes of polysubstance use among adolescents: A systematic review. Drug and Alcohol Dependence, 160, 12–29.

91.

Verdejo-Garcıa

A. J.

López-Torrecillas

de Arcos

F. A.

Pérez-Garcıa

(2005). Differential effects of MDMA, cocaine, and cannabis use severity on distinctive components of the executive functions in polysubstance users: A multiple regression analysis. Addictive Behaviors, 30(1), 89–101.

92.

Vermunt

J. K.

(2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18(4), 450–469.

93.

Vezina

Hebert

(2007). Risk factors for victimization in romantic relationships of young women: A review of empirical studies and implications for prevention. Trauma, Violence, & Abuse, 8(1), 33–66.

94.

Virkkunen

Linnoila

(1993). Brain serotonin, type II alcoholism, and impulsive violence. Journal of Studies on Alcohol, 11, 163–169.

95.

Wanner

Vitaro

Carbonneau

Tremblay

R. E.

(2009). Cross-lagged links among gambling, substance use, and delinquency from midadolescence to young adulthood: Additive and moderating effects of common risk factors. Psychology of Addictive Behaviors, 23(1), 91–104.

96.

White

H. R.

(1997). Alcohol, illicit drugs, and violence. In Stoff

D. M.

Maser

J. D.

, (Eds.), Maser, Handbook of antisocial behavior (pp. 511–523). John Wiley & Sons Inc.

97.

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), 277–293.

98.

Wolfe

D. A.

Crooks

Jaffe

Chiodo

Hughes

Ellis

…Donner

(2009). A school-based program to prevent adolescent dating violence: A cluster randomized trial. Archives of Pediatrics & Adolescent Medicine, 163(8), 692–699.

99.

Wolfe

D. A.

Wekerle

Scott

Straatman

A. L.

Grasley

(2004). Predicting abuse in adolescent dating relationships over 1 year: The role of child maltreatment and trauma. Journal of Abnormal Psychology, 113(3), 406–415.

100.

Wolitzky-Taylor

K. B.

Ruggiero

K. J.

Danielson

C. K.

Resnick

H. S.

Hanson

R. F.

Smith

D. W.

… Kilpatrick

D. G.

(2008). Prevalence and correlates of dating violence in a national sample of adolescents. Journal of the American Academy of Child & Adolescent Psychiatry, 47(7), 755–762.