Can Better Emotion Regulation Protect Against Interpersonal Violence in Homeless Youth Social Networks?

Abstract

Homeless youth experience all types of violence at higher rates than their housed counterparts. This is typically the result of many contributing factors including childhood experiences of trauma, subsistence survival strategies, and exposure to perpetrators while living on the streets. Reducing violence in the lives of homeless youth is imperative and can contribute to a young person’s ability to safely and successfully exit the streets and lead a long and productive life in society. However, developing public health and social interventions to reduce violence in adolescent and young adult populations is difficult due to the complex interplay of extrinsic and intrinsic drivers of this phenomenon. Los Angeles area homeless youth (N = 366) were asked questions regarding recent violence experiences, emotion regulation, and their social network. Multivariable logistic regressions tested the overall effect of emotion regulation on violence, controlling for age, gender, race, sexual identity, experience of childhood abuse, and data collection site. In this sample, 56% of youth endorsed fighting in the previous year, and those who reported more difficulties with emotion regulation were significantly more likely to fight. In addition, youth who scored below the mean on difficulties with regulation and belonged to networks characterized by low-difficulty peers were 60% less likely to report fighting. Emotion regulation skills represent a malleable target for intervention that may contribute to reduced propensity for violence in this population. Implications for network-based interventions to improve individual emotion regulation and reduce overall violence among homeless youth and other at-risk populations are discussed.

Keywords

homeless youth interpersonal violence emotion regulation social network analysis

Violence and Emotion Regulation

Violence is a complex phenomenon that affects adolescents and young adults across America. It occurs in multiple ways, including interpersonal violence, intimate partner violence, and gang and gun violence. An estimated 1.6 million to 2.8 million youth experience homelessness in the United States (Terry, Bedi, & Patel, 2010). Although violence in the United States has steadily decreased during the past decade, youth in general and homeless youth (HY) in particular remain disproportionately susceptible to violent victimization and perpetration (Office of Victims of Crime, 2013). These youth experience all types of violence at higher rates than their housed counterparts (Eaton et al., 2012; Heerde, Hemphill, & Scholes-Balog, 2014; Petering, Rice, & Rhoades, 2016).

Interpersonal violence is defined as the intentional use of physical force or power between two or more individuals resulting in injury, death, or psychological harm. Both experiencing and perpetrating interpersonal violence while homeless are linked to early childhood risk factors including experiences of trauma such as physical abuse or neglect (Wolfe, Toro, & McCaskill, 1999). Many youth cite escaping a violent family environment as a reason for becoming homeless (Milburn, Rotheram-Borus, Rice, Mallet, & Rosenthal, 2006; Robertson & Toro, 1999; Whitbeck & Hoyt, 1999; Whitbeck, Hoyt, & Ackley, 1997; Zide & Cherry, 1992). This experience and exposure to violence may contribute toward a propensity for violence during adolescence or young adulthood, because HY may reenact violent behaviors learned from previous perpetrators of abuse (Herrenkohl, Sousa, Tajima, Herrenkohl, & Moylan, 2008). Subsistence survival strategies such as stealing, burglary, prostitution, and dealing drugs to obtain money, food, or shelter (Baron, Forde, & Kennedy, 2007; Crawford, Whitbeck, & Hoyt, 2011; Martin et al., 2009; Toro, Dworsky, & Fowler, 2007) and exposure to perpetrators while living on the streets increase the likelihood of experiencing violence (Milburn et al., 2006; Robertson & Toro, 1999; Whitbeck & Hoyt, 1999). Because a street-entrenched life increases exposure to violence, many youth use physical aggression as a tactic to avoid future victimization (Baron, 2004; Baron, Kennedy, & Forde, 2001; Gaetz, 2009). Furthermore, although interpersonal violence occurs in a dyadic space, it is also contagious in social networks, diffusing across social space. A young person’s risk of engaging in violence increases as the proportion of violent peers in his or her network increases (Petering et al., 2016).

The consequences of violence for HY are severe. The proximal consequence of physical injury is a primary health concern for HY (Ensign & Gittelsohn, 1998; Ensign & Panke, 2002; Forst, Harry, & Goddard, 1993), who generally do not proactively seek health services (Ensign & Panke, 2002). Minor, treatable injuries can often escalate into more severe health problems (Ensign & Gittelsohn, 1998; Hwang, 2001; Vanderleest, 2010). Violence can also lead to nonphysical ailments as posttraumatic stress disorder (Bender, Thompson, Ferguson, & Langenderfer, 2014; Fitzpatrick & Boldizar, 1993; Overstreet & Braun, 2000), depression (Latzman & Swisher, 2005; Mazza & Overstreet, 2000), and externalizing behavior (e.g., delinquency and aggression; Overstreet, 2000). Interpersonal violence also limits a youth’s ability to successfully exit homelessness by increasing the risk of interaction with law enforcement, arrest, and imprisonment (Chen, Thrane, Whitbeck, & Johnson, 2006; Miles & Okamoto, 2008; Schwartz, Sorensen, Ammerman, & Bard, 2008; Yoder, Bender, Thompson, Ferguson, & Haffejee, 2014), which can limit employment and housing opportunities. Violence can also result in the temporary or permanent termination of services at agencies designed to support and assist HY in meeting needs or securing housing.

Reducing violence in the lives of HY is imperative and can contribute to a youth’s ability to safely and successfully exit the streets and lead a long and productive life in society. However, using public health and social interventions to reduce violence in adolescent and young adult populations is difficult (Petering, Wenzel, & Winetrobe, 2014), because this phenomenon of violence is complex with many intrinsic and extrinsic contributing factors. Adaptive emotion regulation may be a protective factor against violence in high-risk contexts. Emotion regulation has been described as coping habits that organize behavioral responses to emotionally salient environmental cues (Gross, 1998). Evidence suggests that adaptive emotion regulation strategies like cognitive reappraisal and problem solving are associated with less severe mental health symptoms (Aldao, Nolen-Hoeksema, & Schweizer, 2010) and less self-injury (Zelkowitz, Cole, Han, & Tomarken, 2016).

Emotional awareness and emotional control in particular have been linked to positive mental and behavioral health outcomes in both clinical and nonclinical youth and young adult samples. Evidence indicates that emotional awareness is a critical determinant of the ability to regulate affect (Dvir, Ford, Hill, & Frazier, 2014). Recent research suggested that emotional awareness and control are associated with reduced suicidality in HY (Barr, Fulginiti, Rhoades, & Rice, 2016) and improvements in hostility, emotional discomfort, and conflict resolution (Sibinga et al., 2011). Although relationships between emotion regulation and interpersonal violence have not been investigated directly in HY, violence has been conceptualized as a maladaptive strategy for coping with affective instability and aversive environmental stimuli that may be amenable to change through improvements in emotion regulation skills (Lubell & Vetter, 2006; Resnick, Ireland, & Borowsky, 2004).

In addition, emotion regulation has been shown to be responsive to intervention. Mindfulness-based interventions like mindfulness-based stress reduction (Kabat-Zinn, 2003) teach participants to observe their thoughts and emotions in the present moment without judgment and have been linked to broad range of improvements in psychological outcomes including emotion regulation skills. Mindfulness-based interventions have demonstrated feasibility in high-risk young adult populations (Greenberg & Harris, 2012; Mendelson et al., 2010) that may be less amenable to programs targeting co-occurring risk behaviors like substance use. As a result, these interventions may hold promise for improving emotion regulation skills in the context of affective distress and environmental cues linked to interpersonal violence in this population (Kliewer et al., 2004; Trosper, Buzzella, Bennett, & Ehrenreich, 2009). Thus, this study examined the relationship between emotional regulation and interpersonal violence in a population of HY and how individual emotional regulation relates to the social networks of HY.

Method

Sample and Procedure

A sample of 481 HY aged 18 to 25 years accessing services from two day-service drop-in centers for HY in Hollywood and Santa Monica, CA, was approached for study inclusion in October 2011 and February 2012. The research team approached all youth who entered the service agencies during the data collection period and invited them to participate in the study. The selected agencies provided weekday services to eligible HY, including basic needs, medical and mental health services, case management, and referrals and connections to other programs such as housing services. Each youth signed a voluntary consent form and a consistent pair of research staff members was responsible for all recruitment to prevent youth from completing the survey multiple times during each data collection period per site. The overall response rate was 80.1%; 19.9% of HY approached declined to participate, 6.2% did not complete the full survey, and 2.6% completed the surveys at both sites 3 months apart. Four participants were excluded because they were younger than 18 to limit the sample to late adolescence and early adulthood. The final sample consisted of 366 participants. The institutional review board of University of Southern California approved all procedures and waived parental consent for minors without parents or guardians.

The study consisted of two parts: a computerized self-administered survey and a social network interview. The computerized survey included an audio-assisted version for participants with low literacy and was available in English or Spanish. The computerized survey included approximately 200 questions and took an average of 1 hr to complete. The social network interview was interactive and conducted by a trained research staff member, as in a previous study (Petering, et al., 2016). Social network interviews took between 15 and 30 min to complete depending on each participant’s personal network size.

Methods for collecting social network data are numerous. One of the most common field techniques is the use of name generators—a question or series of questions designed to elicit the naming of relevant social connections along some specified criterion (Campbell-Barrett & Karen, 1991; Marsden, 2005). Typically, these are free-recall name generators. Respondents are given a prompt that defines some criterion, for instance, a category of persons such as family or friends or types of social exchange relationships (e.g., “Who do you turn to for advice or support?”). Then, respondents are asked to list as many people as they can. In some cases, an upper limit is given regarding the number of names that can be elicited. Most solutions to network recall begin with the understanding that a single-item, free-recall name generator will be most subject to recall bias. Researchers (Brewer, 2000; Brewer, Garrett, & Rinaldi, 2002) have extensively reviewed the topic and suggested and tested several viable solutions to the problem, including nonspecific prompting, reading back lists, semantic cues, multiple elicitation questions, and reinterviewing.

The current study used multiple elicitation questions. The following item was read: “Think about the last month. Who have you interacted with? These can be people you interacted with in person, on the phone, or through the Internet” After youth stopped nominating social connections, an additional 15 prompts to solicit nominees were used as follows: These might be friends; family; people you hang out with, chill with, kick it with, or have conversations with; people you party with—use drugs or alcohol; boyfriend or girlfriend; people you are having sex with; baby mama or baby daddy; case worker; people from school; people from work; old friends from home; people you talk to (on the phone, by email); people from where you are staying (squatting with); people you see at this agency; or other people you know from the street. Interviewers paused between each prompt to allow youth to nominate additional social connections before proceeding to the next prompt.

At the Santa Monica site, interviewers used a large sheet of paper and colored markers to create a network map for each participant. Individuals whom participants nominated to include in their social network were referred to as their social connections and were written in an arc around each participant’s name, which was written in the center of the paper. At the second site in Hollywood, the research team used an iPad application to create the network map. Research assistants later coded all data from hand-drawn network maps into a corresponding database.

After youth finished nominating connections, attributes of each social connection were then collected, including first and last names, aliases, age, gender, race and ethnicity, visible tattoos, and whether the nominee was a client of the agency. In the paper-and-pencil version, attributes were entered into a spreadsheet on a laptop computer by the interviewer; in the app version, responses were entered directly into the app by the interviewer.

A sociomatrix was created linking participants in the sample. A direct tie from participant i to participant j was recorded if participant i nominated participant j in his or her personal network. Although direct network information was collected, network information is intrinsically indirect (a person who interacts with another) and was treated in this fashion for the current analysis. The initial matrix creation, however, depended on information provided by each youth about personal network alters, and subsequent “matches” were made to other youth in the sample. Matches were based on name, alias, race and ethnicity, gender, approximate age, tattoos, and agency attendance. Two independent reviewers made match decisions for all alters between the ages of 13 and 39 years and not identified as agency staff members. Decision rules were derived from available information and decisions were based on algorithms that included (a) interviewer and recruiter field knowledge (through the compilation of field notes following each data collection period); (b) how well the ego knew the alter (i.e., relative, romantic partner, needle sharer, known for at least 1 year) and whether the alter was identified as a client; and (c) via an Access database and form that formulaically paired possible matches based on names, visible tattoos, and demographic characteristics. If two distinct youth were similar based on all information, for the purposes of this research they were considered to be the same individual. We used a union rule to assign adjacency, such that social relationships existed if either party reported that interaction took place. For example, suppose Youth A nominated Youth B in during an interview. If Youth B and Youth C were similar given identifying characteristics, they were considered to be the same youth and a tie from Youth A to Youth B was added to both the personal network of Youth A and the personal network of Youth B. For all matches, two independent reviewers who were part of the team that collected field data assigned matches. The independent reviewers’ decisions were compared for agreement. Discrepant matches were discussed as a group with the independent reviewers and a third reviewer who also served as an interviewer and recruiter during data collection. Group consensus led to final match decisions. Three questionable matches were left uncoded (hence a conservative matrix of ties). All participants received $20 in cash or gift cards as compensation for their time.

Dependent Variable

The dependent variables were violent behavior and violent injury. Violent behavior was assessed as recent participation in a physical fight. Participants were asked, “During the past 12 months, how many times were you in a physical fight?” Eight ordinal response options ranged from “zero times” to “over 12 times.” Due to skew of the original variable, responses were dichotomized similar to previous literature on youth violence (Duong & Bradshaw 2014; Eaton et al., 2012) to distinguish between participants who had been in no physical fights and participants who had been in at least one physical fight during the previous year. Violent injury was assessed in a similar manner. Participants were asked, “During the past 12 months, how many times were you in a physical fight in which you were injured and had to be treated by a doctor or nurse?” These questions were adopted from the Youth Risk Behavior Survey (Centers for Disease Control and Prevention, 2014) and did not distinguish between victims and perpetrators of violence.

Sociodemographic Variables

Sociodemographic variables included gender, age, race and ethnicity, sexual orientation, and field site. Six participants who identified as transgender were coded based on the gender with which they currently identified (e.g., transgender male to female was coded as female), resulting in a binary category for gender. Age ranged from 18 to 26 years. Participants were coded for the field site where baseline data were collected, either Santa Monica or Hollywood. Participants were asked various questions to assess childhood trauma, including physical abuse (having been hit, punched, or kicked very hard at home, excluding ordinary fights between brothers and sisters); witnessing family violence (seeing a family member being hit, punched, or kicked very hard at home, excluding ordinary fights between brothers and sisters); and sexual abuse (having an adult or someone much older touch their private sexual body parts in an unwanted way). Participants who responded affirmatively to any of these questions were coded as having experienced childhood abuse.

Emotion Regulation

Consistent with previous HY literature (Barr et al., 2016), emotion regulation was measured using the awareness and impulsivity subscales of the Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004). Each subscale features six items measured on a 5-point Likert-type scale. Emotional awareness and control scores were computed by summing responses to each subscale for a possible range of 6 to 30, with higher scores indicating more difficulty regulating emotions (Cronbach’s α = .77).

To measure the tendency of an individual to be connected with other youth who had low DERS scores (homophily), a variable indicated the proportion of peers with a low DERS score to whom an individual was connected. Due to issues of skew, this variable was dichotomized to indicate whether an individual’s network had a proportion of peers with low DERS scores greater than the overall population mean.

Statistical Analysis

Interaction variables for emotion regulation, violence, and social network qualities were tested. No interaction variable was significantly related to report of violence. Prior to the regression analyses, a correlation matrix of all independent variables showed no two variables were significantly correlated at a value greater than .40 or less than –.40, indicating little risk of multicollinearity. Univariable logistic regression was performed to test the relationship of emotion regulation and a well-regulated network. Four unique multivariable logistic regressions were performed with violent behavior and injury from violence as the dependent variables. All models controlled for individual-level variables including age, race and ethnicity, sexual orientation, experience of child abuse, and data collection site. The first set of multivariable logistic regression models included DERS score as the independent variable. The second set of multivariable models included a variable representing individuals with a low DERS score and a low DERS network as the independent variable. All analyses were performed using SAS 9.4.

Results

Descriptive statistics are presented in Table 1. The average age of participants was 21.49 years (SD = 1.98). The majority of participants were male (71.04%); 36.34% of participants identified as White, followed by 29.51% Black, 19.13% mixed or other race, and 14.81% Latino. One quarter of the sample identified as lesbian, gay, bisexual, or queer. In this sample, 39.34% of participants had experienced some type of childhood abuse, 56.32% had engaged in violence in the previous year, and 21.27% had been injured due to violence in the previous year. The average DERS score was 24.31 (SD = 7.30) and 22.68% of the sample qualified as low DERS, meaning their DERS score was at least one standard deviation below the sample mean, indicating that they had low difficulties in regulating emotion. In this sample, 33.56% of participants had a low DERS score and a network that contained greater than the average proportion of individuals with a low DERS score, meaning they had a well-regulated network. Bivariate analyses revealed that having a low DERS score doubled the likelihood of having a well-regulated network (OR = 1.95; 95% confidence interval [CI] = 1.09, 3.49; p < .05.

Table 1.

Descriptive Statistics (N = 366).

	n	%
Age (range: 18-26 years)^a	21.49	1.98
Male	260	71.04
Race and ethnicity
White	133	36.34
Black	108	29.51
Mixed or other	70	19.13
Latino	60	14.81
LGBQ	89	24.72
Site
Hollywood	202	55.19
Santa Monica	164	44.81
Child abuse	144	39.34
Violence	205	56.32
Violent injury	77	21.27
DERS score (range: 3-48)^a	24.31	7.30
Low DERS	83	22.68
Low DERS network	102	27.87
Low DERS and low DERS network	98	33.56
Bivariate Analysis	Low DERS
Bivariate Analysis	Point Estimate	95% CI
Low DERS network	1.953*	[1.09, 3.49]

Note. LGBQ = lesbian, gay, bisexual, or queer; DERS = Difficulties in Emotion Regulation Scale; CI = confidence interval.

Values represent M and SD.

p < .05.

Figures 1 to 3 present visual depictions of the different categories of individual ego networks in relation to emotion regulation. Figure 1 depicts an individual with high difficulty with emotion regulation in a low-difficulty network. Figure 2 depicts an individual with low difficulty in emotion regulation in a high-difficulty network. Figure 3 depicts an individual with low difficulty in emotion regulation in a low-difficulty network. Table 2 presents results from multivariable logistic regressions. Individuals who experienced childhood abuse were 2.33 times more likely to engage in violence (95% CI = 1.44, 3.81; p < .001) and those who reported more difficulties with emotion regulation were significantly more likely to fight (OR = 1.03; 95% CI = 1.00, 1.06; p < .05). Individuals in Hollywood were 2.61 times more likely to be injured from violence (95% CI = 1.33, 5.13; p < .01), and those who experienced child abuse were also more likely to be injured from violence (OR = 2.03; 95% CI = 1.14, 3.64; p < .05). Greater difficulty regulating emotion increased the likelihood of being injured from violence (OR = 1.06; 95% CI = 1.02, 1.11; p < .01). In addition, youth scoring below the mean on difficulties with regulation who also belonged to networks characterized by low-difficulty peers were 60% less likely to report fighting (OR = 0.39; 95% CI = 0.17, 0.92; p < .05).

Figure 1.

High individual difficulty with emotion regulation in a low-difficulty network.

Figure 2.

Low individual difficulty with emotion regulation in a high-difficulty network.

Figure 3.

Low individual difficulty with emotion regulation in a low-difficulty network.

Table 2.

Multivariable Logistic Regressions for Violence and Violent Injury.

	Model 1				Model 2
	Violence		Violent Injury		Violence		Violent Injury
	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
Age	0.94	[0.84, 1.06]	1.00	[0.87, 1.15]	0.93	0.83, 1.04	0.99	[0.87, 1.14]
Male	1.65	[0.99, 2.73]	1.34	[0.70, 2.59]	1.67	1.01, 2.77	1.42	[0.75, 2.67]
Race and ethnicity^a
Black	0.71	[0.38, 1.30]	0.54	[0.25, 1.15]	0.66	0.35, 1.22	0.50	[0.24, 1.07]
Latino	0.87	[0.42, 1.79]	0.91	[0.40, 2.06]	0.83	0.40, 1.71	0.88	[0.39, 1.98]
Mixed or other	0.92	[0.48, 1.75]	0.67	[0.30, 1.50]	0.86	0.45, 1.64	0.62	[0.28, 1.36]
LGBQ	0.87	[0.51, 1.49]	0.86	[0.44, 1.67]	0.81	0.48, 1.38	0.85	[0.45, 1.64]
Site	1.20	[0.70, 2.05]	2.61**	[1.33, 5.11]	1.26	0.74, 2.15	2.68**	[1.38, 5.21]
Child abuse	2.35***	[1.44, 3.82]	2.03*	[1.14, 3.64]	2.36**	1.45, 3.83	2.03*	[1.15, 3.59]
DERS score	1.03*	[1.00, 1.06]	1.06**	[1.02, 1.11]
Low DERS and low DERS network					0.39*	0.17, 0.89	0.42	[0.12, 1.50]
Max-rescaled R²	.10		.11		.10		.08
AIC	478.01		353.44		481.35		365.86
n in final model	354		352		358		356

Note. OR = odds ratio; CI = confidence interval; LGBQ = lesbian, gay, bisexual, or queer; DERS = Difficulties in Emotion Regulation Scale; AIC = Akaike information criterion.

White was reference category.

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

Discussion

The purpose of this study was to develop an understanding of how qualities of emotion regulation among HY are related to youth’s engagement in interpersonal violence. This work is particularly important because it advances previous research on HY, focusing on youth’s current psychological qualities rather than demographic characteristics or engagement in co-occurring risk behaviors. Consistent with previous research (Herrenkohl et al., 2008; Petering, et al., 2016; Wolfe et al., 1999), the current study found that childhood experiences of maltreatment are closely linked to later-life engagement in interpersonal violence. Controlling for these experiences, youth in this sample with better emotion regulation skills were less likely to engage in interpersonal violence. Current study results featuring the addition of emotion regulation variables highlight the critical role of psychological skills in youth’s experiences of violence and suggest that violence prevention programming that focuses on building these skills would be effective.

Potential Intervention Approaches

Interpersonal violence can be conceptualized as a maladaptive coping behavior in the context of multiple stressors, including a history of traumatic experiences. Study results indicate that increased risk of violence associated with traumatic experiences can be mitigated if youth demonstrate fewer difficulties with emotional awareness and control, but very little research has focused on interventions that might build these skills among HY. Mindfulness-based interventions represent one line of programming that may positively affect violence in this population. These interventions have demonstrated effectiveness in improving emotion regulation skills in adult and young adult clinical and community samples (Arch & Craske, 2010; Golden & Gross, 2010; Linehan, 2018), and trait mindfulness is associated with greater psychological adjustment following exposure to trauma (Thompson, Arnkoff, & Glass, 2011). Mindfulness-based interventions focused on building emotion regulation capacity might also be more congruent with street culture and face fewer barriers to implementation and retention than interventions targeting co-occurring risk behaviors, like substance abuse.

In addition, the results from the current study suggest that emotion regulation is related to social network properties in a population of HY. Individuals with low difficulty regulating emotion who also belong to a well-regulated network benefited from a stacking effect that reduced their odds of fighting by 60%. Therefore, having individual skills to regulate emotion is important in reducing risk of violence, and having both individual skills and a network that is also well regulated increases this effect. Network-based interventions to improve individual emotion regulation can potentially reduce overall violence among HY and other at-risk populations. Public health interventions using social network models to reduce violence have proven successful. Chicago’s Cure Violence program is an example of an effective violence prevention strategy (Bonner, McLean, & Worden, 2008; Deeney, 2012; National Gang Center, 2013). Created to reduce gang violence, the program is guided by social learning theory, which posits that a learned behavior such as violence is modeled and reinforced by peers (Bandura, 1977). Results from the current study support the use of a similar network-based model for HY to reduce overall violence in a network, particularly if it focuses on emotion regulatory skills and other psychological traits such as impulse control. Future research should explore whether emotion regulation is a transferable skill that can be learned through peer modeling behaviors. Regardless, increasing overall emotional regulation skills in an individual’s peer network will likely have a positive impact on well-being.

Study Limitations and Assets

Potential limitations should be considered when interpreting the results of this study. First, data relied on youth’s self-report. Some items included in the survey were personally sensitive and it is possible that youth may not report truthfully on these items or regarding illegal activity, even after being informed of confidentiality. With regard to network data collection, our name generator used the suggested method of multiple elicitations. Any free-recall name generator is subject to recall bias because youth may forget important social connections at the time of the interview, which can lead to missing network connections. However, multiple elicitation is the most viable solution to this problem, given that no roster or other registry exists that could be used for HY to elicit nominations. In addition, the current study used a cross-sectional design, which limits assumptions of causal order. This is particularly important when considering network observations. Network connections could occur as the result of two social processes: social influence, based on Bandura’s (1977) social learning theory, or social selection (Kandel, 1978; McPherson, Smith-Lovin, & Cook, 2001). Social influence suggests that individual behaviors are modeled and learned through network interactions, whereas social selection suggests that individuals choose to interact with others who already engage in similar behaviors. Without longitudinal data, there is no way to distinguish between peer influence and peer selection. Longitudinal social network data are necessary to determine how the effects of emotion regulation occur in a network, but this type of research is particularly difficult with an unbounded population such as HY. In addition, the measure of violent behavior did not distinguish between perpetration and victimization. Future studies would benefit from a more specific measure of interpersonal violence that assesses severity of violent behavior, explores different types of violent behavior (e.g., assault with a weapon, robbery), and distinguishes between perpetration and victimization. Research has shown that violence is often a bidirectional phenomenon (Farrell, Mehari, Kramer-Kuhn, & Goncy, 2014). In the current study, the measure likely captured bidirectional violence; that is, both perpetration and victimization. In addition, our data contained only two of six DERS subscales. As a result, emotion regulation was operationalized as emotional awareness and control. Although inclusion of the full scale would have been preferable, previous research has demonstrated the predictive and convergent validity of the awareness and control subscales with regard to adolescent mental health and risk behaviors (Barr et al., 2016; Ehring et al., 2008).

Despite these limitations, the current study contributed new knowledge regarding the relationship of emotional regulation and violence in homeless and other at-risk youth populations. Violence reduction is imperative in this population and others that are similarly at risk. Yet very few interventions address this issue. Emotion regulation skills represent a malleable target for intervention that may contribute to reduced propensity for violence in this population. Violence is caused by and co-occurs with many other factors, such as childhood maltreatment and substance abuse. Reducing childhood maltreatment would likely reduce violence in this population; however, for service providers and others that work in the field of youth homelessness or with other emerging young adult populations, it is not feasible to eliminate previous experiences of maltreatment. Furthermore, using interventions to reduce substance abuse is also very difficult in this population, because substance abuse is frequently a coping mechanism or survival strategy.

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 funding from the National Institue of Mental Health (R01MH093336).

Author Biographies

Robin Petering is interested in improving the lives of young persons who experience homelessness through community-based research, policy advocacy and program implementation. Her research agenda on reducing violence through innovative intervention approaches.

Nicholas Barr is a postdoctoral fellow interested in improving mental and behavioral health outcomes in populations at high risk for adverse experiences. His research agenda focuses on investigating the protective effects of mindfulness and emotion regulation.

Eric Rice is an associate professor at the University of Southern California School of Social Work and the co-founder of the Center for Artificial Intelligence and Society. He is an expert in social network theory, social network analysis, and the application of social network methods to HIV prevention research.

References

Aldao

Nolen-Hoeksema

Schweizer

(2010). Emotion-regulation strategies across psychopathology: A meta-analytic review. Clinical Psychology Review, 30(2), 217-237.

Arch

J. J.

Craske

M. G.

(2010). Laboratory stressors in clinically anxious and non-anxious individuals: The moderating role of mindfulness. Behaviour Research and Therapy, 48(6), 495-505.

Bandura

(1977). Social learning theory. Englewood Cliffs, NJ: Prentice Hall.

Baron

S. W.

(2004). General strain, street youth and crime: A test of Agnew’s revised theory. Criminology, 42, 457-484. doi:10.1111/j.1745-9125.2004.tb00526.x

Baron

S. W.

Forde

D. R.

Kennedy

L. W.

(2007). Disputatiousness, aggressiveness, and victimization among street youths. Youth Violence and Juvenile Justice, 5, 411-425. doi:10.1177/1541204077299582

Baron

S. W.

Kennedy

L. W.

Forde

D. R.

(2001). Male street youths’ conflict: The role of background, subcultural, and situational factors. Justice Quarterly, 18, 759-789. doi:10.1080/07418820100095091

Barr

Fulginiti

Rhoades

Rice

(2016). Can better emotion regulation protect against suicidality in traumatized homeless youth? Archives of Suicide Research, 1-12.

Bender

Ferguson

Thompson

Komlo

Pollio

(2010). Factors associated with trauma and posttraumatic stress disorder among homeless youth in three US cities: The importance of transience. Journal of Traumatic Stress, 23, 161-168. doi:10.1002/jts.20501

Bender

Thompson

Ferguson

Langenderfer

(2014). Substance use predictors of victimization profiles among homeless youth: A latent class analysis. Journal of Adolescence, 37(2), 155-164.

10.

Bonner

H. S.

McLean

S. J.

Worden

R. E.

(2008). CeaseFire Chicago: A synopsis. Retrieved from https://docplayer.net/44260054-F-i-n-n-ceasefire-chicago-a-synopsis-heidi-s-bonner-sarah-j-mclean-ph-d-robert-e-worden-ph-d-october-2008.html

11.

Brewer

D. D.

(2000). Forgetting in the recall-based elicitation of personal and social networks. Social Networks, 22, 29-44.

12.

Brewer

D. D.

Garrett

S. B.

Rinaldi

(2002). Free-listed items are effective cues for eliciting additional items in semantic domains. Applied Cognitive Psychology, 16, 343-358.

13.

Campbell-Barrett

Karen

(1991). Name generators in surveys of personal networks. Social Networks, 13, 203-221. doi:10.1016/0378-8733(91)90006-F

14.

Centers for Disease Control and Prevention. (2014). Behavioral risk factor surveillance system. Retrieved from http://www.cdc.gov/brfss/

15.

Chen

Thrane

Whitbeck

L. B.

Johnson

(2006). Mental disorders, comorbidity, and postrunaway arrests among homeless and runaway adolescents. Journal of Research on Adolescence, 16, 379-402. doi:10.1111/j.1532-7795.2006.00499.x

16.

Crawford

D. M.

Whitbeck

L. B.

Hoyt

D. R.

(2011). Propensity for violence among homeless and runaway adolescents: An event history analysis. Crime & Delinquency, 57, 950-968. doi:10.1177/0011128709335100

17.

Deeney

(2012, June 21). Can street violence be fought like a disease? The Nation. Available from www.thenation.com.

18.

Duong

Bradshaw

(2014). Associations between bullying and engaging in aggressive and suicidal behaviors among sexual minority youth: the moderating role of connectedness. Journal of school health, 84(10), 636-645.

19.

Dvir

Ford

J. D.

Hill

Frazier

J. A.

(2014). Childhood maltreatment, emotional dysregulation, and psychiatric comorbidities. Harvard Review of Psychiatry, 22(3), 149.

20.

Eaton

D. K.

Kann

Kinchen

Shanklin

Flint

K. H.

Hawkins

. . . Wechsler

(2012). Youth risk behavior surveillance—United States, 2011. Morbidity and Mortality Weekly Report, 61(4), 1-162.

21.

Ehring

Fischer

Schnülle

Bösterling

Tuschen-Caffier

(2008). Characteristics of emotion regulation in recovered depressed versus never depressed individuals. Personality and Individual Differences, 44(7), 1574-1584.

22.

Ensign

Gittelsohn

(1998). Health and access to care: Perspectives of homeless youth in Baltimore City, U.S.A. Social Science & Medicine, 47, 2087-2099. doi:10.1016/S0277-9536(98)00273-1

23.

Ensign

Panke

(2002). Barriers and bridges to care: Voices of homeless female adolescent youth in Seattle, Washington, USA. Journal of Advanced Nursing, 37, 166-172. doi:10.1046/j.1365-2648.2002.02067.x

24.

Farrell

A. D.

Mehari

K. R.

Kramer-Kuhn

Goncy

E. A.

(2014). The impact of victimization and witnessing violence on physical aggression among high-risk adolescents. Child Development, 85, 1694-1710. doi:10.1111/cdev.12215

25.

Fitzpatrick

K. M.

Boldizar

J. P.

(1993). The prevalence and consequences of exposure to violence among African-American youth. Journal of the American Academy of Child & Adolescent Psychiatry, 32, 424-430. doi:10.1097/00004583-199303000-00026

26.

Forst

M. L.

Harry

Goddard

P. A.

(1993). A health-profile comparison of delinquent and homeless youths. Journal of Health Care for the Poor and Underserved, 4, 386-400.

27.

Gaetz

(2009). Whose safety counts? Street youth social exclusion and criminal victimization. In Hulchanski

J. D.

Campsie

Chau

S. B. Y.

Hwang

S. H.

Paradis

(Eds.), Finding home: Policy options for addressing homelessness in Canada (pp. 282-305). Retrieved from http://www.homelesshub.ca/findinghome

28.

Goldin

P. R.

Gross

J. J.

(2010). Effects of mindfulness-based stress reduction (MBSR) on emotion regulation in social anxiety disorder. Emotion, 10, 83.

29.

Gratz

K. L.

Roemer

(2004). Multidimensional assessment of emotion regulation and dysregulation: Development, factor structure, and initial validation of the difficulties in emotion regulation scale. Journal of Psychopathology and Behavioral Assessment, 26, 41-54.

30.

Greenberg

M. T.

Harris

A. R.

(2012). Nurturing mindfulness in children and youth: Current state of research. Child Development Perspectives, 6(2), 161-166.

31.

Gross

J. J.

(1998). The emerging field of emotion regulation: An integrative review. Review of General Psychology, 2, 271-299. doi:10.1037=1089-2680. 2.3.271

32.

Heerde

J. A.

Hemphill

S. A.

Scholes-Balog

K. E.

(2014). “Fighting” for survival: A systematic review of physically violent behavior perpetrated and experienced by homeless young people. Aggression and Violent Behavior, 19, 50-66. doi:10.1016/j.avb.2013.12.002

33.

Herrenkohl

T. I.

Sousa

Tajima

E. A.

Herrenkohl

R. C.

Moylan

C. A.

(2008). Intersection of child abuse and children’s exposure to domestic violence. Trauma, Violence, & Abuse, 9, 84-99. doi:10.1177/1524838008314797

34.

Hwang

S. W.

(2001). Homelessness and health. Canadian Medical Association Journal, 164, 229-233.

35.

Kabat–Zinn

(2003). Mindfulness–based interventions in context: past, present, and future. Clinical Psychology: Science and Practice, 10, 144-156.

36.

Kandel

D. B.

(1978). Homophily, selection, and socialization in adolescent friendships. American Journal of Sociology, 84, 427-436.

37.

Kliewer

Cunningham

J. N.

Diehl

Parrish

K. A.

Walker

J. M.

Atiyeh

Mejia

(2004). Violence exposure and adjustment in inner-city youth: Child and caregiver emotion regulation skill, caregiver–child relationship quality, and neighborhood cohesion as protective factor. Journal of Clinical Child and Adolescent Psychology, 33, 477-487.

38.

Latzman

R. D.

Swisher

R. R.

(2005). The interactive relationship among adolescent violence, street violence, and depression. Journal of Community Psychology, 33, 355-371.

39.

Linehan

M. M.

(2018). Cognitive-behavioral treatment of borderline personality disorder. New York, NY: Guilford Publications.

40.

Lubell

K. M.

Vetter

J. B.

(2006). Suicide and youth violence prevention: The promise of an integrated approach. Aggression and Violent Behavior, 11(2), 167-175.

41.

Marsden

P. V.

(2005). Recent developments in network measurement. In Models and methods in social network analysis (pp. 8-30). Cambridge, United Kingdom: Cambridge university press.

42.

Martin

Palepu

Wood

Montaner

Kerr

(2009). Violence among street-involved youth: The role of methamphetamine. European Addiction Research, 15, 32-38. doi:10.1159/000173007

43.

Mazza

J. L.

Overstreet

(2000). Children and adolescents exposed to community violence: A mental health perspective for school psychologists. School Psychology Review, 29, 86–101.

44.

McPherson

Smith-Lovin

Cook

J. M.

(2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415-444. doi:10.1146/annurev.soc.27.1.415

45.

Mendelson

Greenberg

M. T.

Dariotis

J. K.

Gould

L. F.

Rhoades

B. L.

Leaf

P. J.

(2010). Feasibility and preliminary outcomes of a school-based mindfulness intervention for urban youth. Journal of Abnormal Child Psychology, 38, 985-994.

46.

Milburn

N. G.

Rotheram-Borus

M. J.

Rice

Mallet

Rosenthal

(2006). Cross-national variations in behavioral profiles among homeless youth. American Journal of Community Psychology, 37, 63-76. doi:10.1007/s10464-005-9005-4

47.

Miles

B. W.

Okamoto

S. K.

(2008). The social construction of deviant behavior in homeless and runaway youth: Implications for practice. Child and Adolescent Social Work Journal, 25, 425.

48.

National Gang Center. (2013). Cure violence (formerly CeaseFire). Retrieved from https://www.nationalgangcenter.gov/SPT/Programs/139

49.

Office of Victims of Crime. (2013). 2013 national crime victims’ rights week resource guide (Section 6. Statistical overviews). Washington, DC: U.S. Department of Justice.

50.

Overstreet

(2000). Exposure to community violence: Defining the problem and understanding the consequences. Journal of Child and Family Studies, 9, 7-25. doi:10.1023/A:1009403530517

51.

Overstreet

Braun

(2000). Exposure to community violence and post–traumatic stress symptoms: Mediating factors. American Journal of Orthopsychiatry, 70, 263-271.

52.

Petering

Rice

Rhoades

(2016). Violence in the social networks of homeless youths: Implications for network-based prevention programming. Journal of Adolescent Research, 31, 582-605.

53.

Petering

Wenzel

Winetrobe

(2014). Systematic review of current intimate partner violence prevention programs and applicability to homeless youth. Journal of the Society for Social Work and Research, 5, 107-135.

54.

Resnick

M. D.

Ireland

Borowsky

(2004). Youth violence perpetration: what protects? What predicts? Findings from the National Longitudinal Study of Adolescent Health. Journal of Adolescent Health, 35, 424-e1.

55.

Robertson

M. J.

Toro

P. A.

(1999). Homeless youth: Research, intervention, and policy. In Fosberg

L. B.

Dennis

D. L.

(Eds.), Practical lessons: The 1998 National Symposium on Homelessness Research (pp. 3-1-3-32). Washington, DC: U.S. Department of Housing and Urban Development and U.S. Department of Health and Human Services.

56.

Schwartz

Sorensen

H. K.

Ammerman

Bard

(2008). Exploring the relationship between homelessness and delinquency: A snapshot of a group of homeless youth in San Jose, California. Child and Adolescent Social Work Journal, 25, 255.

57.

Sibinga

E. M.

Kerrigan

Stewart

Johnson

Magyari

Ellen

J. M.

(2011). Mindfulness-based stress reduction for urban youth. The Journal of Alternative and Complementary Medicine, 17, 213-218.

58.

Terry

M. J.

Bedi

Patel

(2010). Healthcare needs of homeless youth in the United States. Journal of Pediatric Sciences, 2, e17.

59.

Thompson

R. W.

Arnkoff

D. B.

Glass

C. R.

(2011). Conceptualizing mindfulness and acceptance as components of psychological resilience to trauma. Trauma, Violence, & Abuse, 12, 220-235.

60.

Toro

P. A.

Dworsky

Fowler

P. J.

(2007). Homeless youth in the United States: Recent research findings and intervention approaches. Retrieved from http://www.huduser.org/publications/pdf/p6.pdf

61.

Trosper

S. E.

Buzzella

B. A.

Bennett

S. M.

Ehrenreich

J. T.

(2009). Emotion regulation in youth with emotional disorders: Implications for a unified treatment approach. Clinical Child and Family Psychology Review, 12, 234-254.

62.

Vanderleest

(2010). Medically uninsured and the homeless. In Johnson

N. J.

Johnson

L. P.

(Eds.), The care of the uninsured in America (pp. 153-160). New York, NY: Springer.

63.

Whitbeck

L. B.

Hoyt

D. R.

(1999). Nowhere to grow: Homeless and runaway adolescents and their families. Hawthorne, NY: Aldine de Gruyter.

64.

Whitbeck

L. B.

Hoyt

D. R.

Ackley

K. A.

(1997). Abusive family backgrounds and later victimization among runaway and homeless adolescents. Journal of Research on Adolescence, 7, 375-392. doi:10.1207/s15327795jra0704_2

65.

Wolfe

S. M.

Toro

P. A.

McCaskill

P. A.

(1999). A comparison of homeless and matched housed adolescents on family environment variables. Journal of Research on Adolescence, 9, 53-66. doi:10.1207/s15327795jra0901_3

66.

Yoder

J. R.

Bender

Thompson

S. J.

Ferguson

K. M.

Haffejee

(2014). Explaining homeless youths’ criminal justice interactions: Childhood trauma or surviving life on the streets? Community Mental Health Journal, 50, 135-144. doi:10.1007/s10597-013-9690-7

67.

Zelkowitz

R. L.

Cole

D. A.

Han

G. T.

Tomarken

A. J.

(2016). The incremental utility of emotion regulation but not emotion reactivity in nonsuicidal self–injury. Suicide and Life–Threatening Behavior, 46, 545-562.

68.

Zide

M. R.

Cherry

A. L.

(1992). A typology of runaway youths: An empirically based definition. Child and Adolescent Social Work Journal, 9, 155-168. doi:10.1007/BF00755230