Correlates and predictors of violent behavior among adolescent drinkers

JOURNAL OF ADOLESCENT HEALTH 2004;34:480 – 492

ORIGINAL ARTICLE

Correlates and Predictors of Violent Behavior Among Adolescent Drinkers MONICA H. SWAHN, Ph.D. AND JOHN E. DONOVAN, Ph.D.

Purpose: To examine a wide range of demographic characteristics and psychosocial factors to determine the cross-sectional correlates of violence and longitudinal predictors of violent initiation among adolescent drinkers. Methods: We conducted secondary analyses of the 1995 (Time 1) and 1996 (Time 2) in-home surveys of the National Longitudinal Study of Adolescent Health (Add Health). This study included a nationally representative school-based sample (Nⴝ18,924) of adolescents in grades 7–12. The analyses were restricted to adolescent drinkers (n ⴝ 8885). Two logistic regression models were constructed using a backward elimination procedure to identify statistically significant cross-sectional correlates of violence and prospective predictors of violence initiation. Results: Half (49%) of all adolescent drinkers reported violent behavior at Time 1 and 15% of those who were not violent at Time 1 reported initiating violent behavior at Time 2. A total of 14 significant cross-sectional correlates of violence were identified that included measures of alcohol use, drug use and selling, exposure to drugs, delinquency, and poor school functioning. Four variables (high-volume drinking, illicit drug use, low grade point average, and having been suspended and/or expelled from school) were significant longitudinal predictors of the initiation of violent behavior. Conclusions: The factors significantly associated with violence pertain mostly to alcohol use, drug use and selling, exposure to drugs, delinquency, and poor school

From the Division of Violence Prevention, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia (M.H.S.) and Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (J.E.D.). Address correspondence to: Monica H. Swahn, Ph.D., Division of Violence Prevention, Mailstop K 60, National Center for Injury Prevention and Control, CDC, 4770 Buford Highway, Atlanta, GA 303413724. E-mail: [email protected] Manuscript accepted August 4, 2003. 1054-139X/04/$–see front matter doi:10.1016/j.jadohealth.2003.08.018

functioning. However, most of these problems and behaviors tend to occur in closer temporal proximity to violent behavior (i.e., within a year) and do not seem to developmentally precede initiation in violent behavior. © Society for Adolescent Medicine, 2004 KEY WORDS:

Aggressive behaviors Alcohol use Correlates Gender differences Predictors Risk factors Violence

Many studies of adolescents report that alcohol use and violent behavior are linked [1– 4]. Several reports also indicate that alcohol use is more common among adolescents who are violent compared with those who are not [5–7], and that aggressive and violent behaviors are more common among adolescent drinkers than among nondrinkers [8,9]. Additionally, it has been reported that becoming a drinker may be an antecedent to violent behavior [10] as well as other problem behaviors [11]. Several different theories link alcohol use and aggression [12]. Some researchers have argued that alcohol and violence are linked in adolescents because alcohol use and aggression share common causes or risk factors [13]. However, few studies have examined the common risk factors for both alcohol use and aggression in adolescents. Research suggests that family pathology and early childhood victimization are linked to both behaviors [12], as are family social background, family and parental characteristics, individual characteristics, and adolescent peer affiliations [14]. Similarly, it is clear from previ© Society for Adolescent Medicine, 2004 Published by Elsevier Inc., 360 Park Avenue South, New York, NY 10010

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ous research that many risk factors are likely to predict both violent behavior and alcohol use. Likely common risk factors are previous aggressive or delinquent behavior [15,16], poor family functioning and bonding [10,15], delinquent peers [10,15], and academic problems [15,16]. There also are likely potentially unique predictors that are not shared. For adolescent drinking, these may include exposure to alcohol in the home [15,17] and parents’ positive attitudes toward drinking [17]. For violent behavior, these potentially unique risk factors may be parental criminal behavior [16], deviant violent attitudes or antisocial beliefs [10,16], and gang membership [16]. The risk factors that predict violence among all adolescents will most likely also predict violence among drinkers. However, because many of the same risk factors contribute to both adolescent drinking and adolescent violence, drinking status must be controlled to determine the specific risk factors for violence within this population. Moreover, it is important to examine the factors that contribute to violence initiation because the majority (84%) of adolescents and young adults who initiate violent behaviors are already drinkers [10]. Factors associated with drinking status (e.g., drinking patterns, problem drinking, and exposure to alcohol through family and friends) will most likely increase the risk of violence for some adolescent drinkers. These drinking-related factors may also act in combination with other risk factors to further increase the risk for violence among some drinkers. The purpose of this study is to describe the cross-sectional correlates of violence as well as the prospective predictors of violent initiation in a national sample of adolescent drinkers. Psychosocial measures from several domains (i.e., family functioning, mental health, alcohol and drug use behaviors and exposures, delinquency, school functioning, and involvement in activities) that have been empirically or theoretically linked to either alcohol use or violent behavior are examined to determine which factors are most strongly associated with, and predictive of, violent behavior.

Methods Data were collected as part of the National Longitudinal Study of Adolescent Heath (Add Health) conducted by the Carolina Population Center at the University of North Carolina at Chapel Hill. Details regarding the survey methodology are described elsewhere [18]. In brief, this survey used a multistage

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stratified cluster design resulting in a nationally representative sample of U.S. adolescents selected from 80 high schools and 52 middle schools, both public and private. Incorporating systematic sampling methods and implicit stratification into the Add Health study design ensured that this sample was representative of U.S. schools with respect to region of country, urbanicity, school type, ethnicity, and school size. After local Institutional Review Board approval of the study and receipt of parental consent, each eligible student was asked to complete an in-school survey. The participation rate was 75.6%. All students at each school were then stratified by grade and gender. Within each stratum investigators randomly selected students to be included in a second and much more detailed interview. The selected students were asked to complete a 90-minute in-home interview (Time 1). In addition, one of the student’s parents or caregivers also completed a separate in-home questionnaire. Eighty percent of students selected in this second stage completed an interview sometime between April and December 1995 (n⫽18,924). About 85% of the students also had a parent or caregiver (most often the biological mother) complete a parent questionnaire during the same time period. The majority of those adolescents who participated in the first interview were asked to participate in a second interview (Time 2) 1 year later, between April and August of 1996. However, participants who were in 12th grade during the first interview were not re-interviewed. The completion rate at Time 2 was 88%, resulting in 13,570 participants. The current analysis presents cross-sectional analyses of the first in-home interview (Time 1) and longitudinal analyses using both the first and the second in-home interview (Time 1 and 2).

Measures The study collected information from participants using computer-assisted personal interviewing (CAPI) and audio computer assisted self-interviewing (ACASI) for sensitive information where participants listened to the questions through headphones and gave responses directly on a laptop computer to increase accuracy of reporting. The current investigation examines several demographic and psychosocial factors. The demographic factors that we included were gender, age, and race/ethnicity. Age was grouped into four categories as follows: 12–14 years; 15–16 years; 17–18 years; and

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19 –21 years. Race and ethnicity were combined to create a four-level measure coded as: 1 ⫽ Hispanic; 2 ⫽ Non-Hispanic African-American; 3 ⫽ Non-Hispanic Other (primarily Asian, Pacific Islander and Native American); and 4 ⫽ Non-Hispanic White. Additionally, we examined 39 psychosocial factors from the following 10 domains that appear in the literature: socio-demographic characteristics; family functioning; mental health; alcohol use and problem drinking; exposure to alcohol through peers and family; drug use; exposure to drugs through peers and neighborhood; school functioning; general delinquency; and involvement in activities. Table 1 briefly outlines the domains and additional information about the measures. More detailed information about these measures is available from the authors upon request. Note also that several of the measures are described elsewhere [19].

Sociodemographic Characteristics Three sociodemographic characteristics were assessed. “Family Structure” indicates whether or not the adolescent lives with one or two parents, and whether one of the parents is a stepparent or adoptive parent. “Mother’s Education” indicates the highest level of education that the mother has received (from less than high school to college degree or postgraduate). “Economic Situation” was assessed by asking the adolescent whether their resident mother or their resident father receives public assistance such as welfare (“yes”/“no”).

Family Functioning Three measures of family functioning were included. Adolescent’s “Shared Decision-Making” is a scale that assesses whether or not the adolescent is permitted by their parent to make his/her own decisions about such things as the time to be home on weekend nights or what he/she wears. The scale has three levels: 1 ⫽ adolescent makes all (6 –7) decisions; 2 ⫽ adolescent makes most (4 –5) decisions; and 3 ⫽ adolescent makes none or few (0 –3) decisions. “Relationship with Parents” is a measure that assesses whether or not his/her resident mother/father is warm and loving, if he/she is satisfied with how they communicate, and if he/she is satisfied overall with the relationship to the mother/father. The scale has three levels: 1 ⫽ poor relationship with one or both parent(s); 2 ⫽ neither good nor poor relationship with one or both parent(s); and 3 ⫽ good

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relationship with one or both parent(s). “Family Activities” is based on the type and number of activities and interactions the adolescents engaged in with their mother or father in the past 4 weeks and has three levels: 1 ⫽ 0 –2 activities; 2 ⫽ 3–5 activities; and 3 ⫽ 6 –10 activities.

Mental Health The adolescent’s mental health (excluding alcohol and drug use) was assessed using three measures. “Depression” was assessed by asking participants if they had experienced a number of different symptoms or feelings such as having been bothered by things or having had poor appetite. The summed distribution was dichotomized using the top 25% of the distribution (those most depressed) versus the remaining 75%. Participants were also asked if they had received “Emotional or Psychological Counseling” in the past year (“yes”/“no”). “Self-Esteem” was assessed by asking participants if they perceived themselves to have many good qualities and if they feel socially accepted. The responses were summed and split into the top 25% (indicating low selfesteem) versus the rest.

Alcohol Consumption and Problems Six measures of alcohol consumption and alcohol problems were included. “Usual Drinking Quantity” was reported in terms of number of drinks the adolescent usually drank at each drinking occasion in the past year. The distribution of usual drinking quantity was split into four drinking quantities based on quartiles and resulted in the following categories: (a) 1 drink; (b) 2–3 drinks; (c) 4 – 6 drinks; and (d) 7 or more drinks. “Drinking Frequency” indicated how many days they drank per month. A three-level measure was created to indicate: 1 ⫽ “those who drink less than 1 day/month”; 2 ⫽ “those who drink 2– 8 days/month”; and 3 ⫽ “those who drink more than 9 days per month.” Participants were also asked how many days they drank five or more drinks in a row (“High-Volume Drinking”; HVD). HVD is a four-level variable that is coded as follows: 1 ⫽ “No HVD (do not drink five or more drinks in a row)”; 2 ⫽ “HVD less than 1 day/month”; 3 ⫽ “HVD 2– 8 days/month”; and 4 ⫽ “HVD 9 –30 days/month.” “Unsupervised Drinking” was assessed by asking participants if they ever drink alcohol when they are not with their parents or other adults in their family (“yes”/“no”). “Problem

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Table 1. Description of Independent Variables, Time 1 (Drinkers, N ⫽ 8885) Measures Socio-demographic factors Family structure Resident mother’s education Economic situation Family functioning Shared decision-making Relationship to parent(s) Family activities Mental health Depression Psychological counseling Self-esteem Alcohol consumption and problems Usual drinking quantity Drinking frequency High-volume drinking Drink outside family Problem drinking Drug/alcohol abuse treatment Drinking alone Exposure to Alcohol Peer alcohol use Access to alcohol Mother drinking Father drinking Mother’s alcoholism Father’s alcoholism Drug use/selling Illicit drug use Selling drugs Exposure to drugs Peer drug use Drugs in the home Neighborhood drug use/dealing Delinquency Delinquency School functioning Grade point average Days absent Troubles at school Closeness to school Held back Suspended/expelled College expectations Activities TV viewing Sports Exercise Religious youth groups

No. of Items

Cronbach Alpha

Time Frame

Respondent

Current Current

1 1

— —

4 4

Current

Adolescent Parent/ adolescent Adolescent

2

—

2

Current Current

Adolescent Adolescent

7 6

0.63 0.84

3 3

Past month

Adolescent

10

0.55

3

Past week Past year

Adolescent Adolescent

18 1

0.87 —

2 2

Current

Adolescent

6

0.84

2

Past year Past year Past year Ever Past year Past year

Adolescent Adolescent Adolescent Adolescent Adolescent Adolescent

1 1 1 1 8 1

— — — — 0.78 —

4 3 4 2 2 2

Ever

Adolescent

1

—

2

Past year Current Past year Past year Current Current

Adolescent Adolescent Parent Parent Parent Parent

1 1 1 1 1 1

— — — — — —

4 2 3 3 2 2

Ever Past year

Adolescent Adolescent

4 1

0.60 —

2 2

Current Current Current

Adolescent Adolescent Parent

1 1 1

— — —

4 2 2

Past year

Adolescent

10

0.79

2

Current Current Current Current Ever Ever Current

Adolescent Adolescent Adolescent Adolescent Adolescent Adolescent Adolescent

4 1 4 2 1 2 2

0.66 — 0.68 0.75 — — —

3 2 2 2 2 2 2

Past Past Past Past

Adolescent Adolescent Adolescent Adolescent

1 1 1 1

— — — —

3 3 3 3

week week week year

Levels

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Drinking” is a construct that consists of two components: drinking frequently until drunk or intoxicated, and drinking that results in negative consequences such as problems with family, friends, and school [11,20]. Participants were asked how many times they had gotten drunk or gotten into trouble with their parents, school, friends, or dates, or regretted their actions because of drinking. Because these items were highly interrelated (Cronbach alpha ⫽ 0.78), a single dichotomous variable was created to indicate the presence or absence of problem drinking. This construct was developed using a similar approach to that used in a previous investigation [21]. The current construct considers problem drinkers to be those that had been drunk at least 3–12 times in the past year or who had experienced negative consequences of drinking at least twice in the past year in each of three or more areas. Participants were also asked if they ever “Drink Alcohol Alone” (not limited to past 12 months), and if they had received “Drug or Alcohol Abuse Treatment” in the past year.

Exposure to Alcohol Six indicators were used to assess exposure to alcohol. “Peer Alcohol Use” was assessed by asking the adolescent how many of their three best friends drink alcohol at least monthly. “Access to Alcohol” was assessed by asking participants whether or not alcohol is easily available to them in their home. “Father Drinking” and “Mother Drinking” were assessed by asking the parent/caretaker who completed the in-home interview how often they drink alcohol, and also how often their partner/spouse drinks alcohol. Both variables were coded as follows: 1 ⫽ “drink less than once per month”; 2 ⫽ “drink 2– 8 times per month”; and 3⫽ “drink 9 or more days per month”. “Father Alcoholism” and “Mother Alcoholism” were assessed by asking the parent/caretaker who completed the in-home interview whether the biological father or mother had alcoholism (“yes”/ “no”).

Drug Use and Drug Selling Two measures of illicit drug involvement were used. “Illicit Drug Use” is a dichotomous measure that indicates any illicit drug use (marijuana, cocaine, inhalants, or other drugs). “Selling Drugs” is a dichotomous measure indicating whether or not participants had sold marijuana or other drugs.

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Exposure to Illegal Drugs Three measures were used to assess exposure to illegal drugs. “Peer Marijuana Use” was assessed by asking the adolescent how many of their three best friends use marijuana at least once a month. “Access to Illegal Drugs in the Home” was assessed by asking whether or not illegal drugs were easily available to them in their home. “Neighborhood Drug Use/ Dealing” were assessed by asking parents if drug dealers and drug users are a problem in their neighborhood.

General Delinquency The “Delinquency” measure asked about frequency in involvement in behaviors such as painting graffiti, lying to parents, shoplifting, and theft in the past year. The summed distribution was dichotomized using the top 25% (most delinquent) versus the rest.

School Functioning School functioning, academic achievement, school bonding, and college expectations were assessed using seven measures. Current “GPA” (grade point average) is a self-report of the mean grade in four subjects (English or language arts, mathematics, history or social studies, and science) and was coded as follows: 1 ⫽ 1.0 –1.9; 2 ⫽ 2.0 –2.9; 3 ⫽ 3.0 – 4.0. “Days Absent” from school was assessed by asking the students how many days during the school year they had missed school for a full day without an excuse and was split at the top quartile (missing 3 or more days) versus the rest. “Troubles at School” asked participants how often they had trouble getting along with their teacher, paying attention in school, getting their homework done, and getting along with other students. Response categories ranged from “never” to “everyday.” The summed distribution was dichotomized to indicate any versus no troubles in school. “Closeness to School” was a combined measure that asked students if they felt close to people at their school and if they felt like they were a part of the school. A dichotomous measure was created to indicate those who felt close to their school versus those that did not. “Held Back” was assessed by asking the students if they had ever repeated a grade or been held back a grade (“yes”/“no”). “Suspensions/Expulsions” was a combined measure asking about ever receiving an out-of-school suspension and ever being expelled from school. A dichotomous measure was created to indicate any versus no

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suspensions or expulsions. “College Expectations” was a combined measure of asking participants how much they want to go to college and how likely it is that they will go to college. A dichotomous variable was created to indicate those who indicated low likelihood of attending college versus the rest.

Activities Involvement in activities was assessed using four measures. Participants were asked three questions concerning how many times in the past week they had “Watched Television” or played video games, played an active “Sport” (e.g., baseball, softball, basketball, soccer, swimming, football), and “Exercised” (jogging, walking, karate, jumping rope, gymnastics, dancing). These three variables were coded to indicate any or no involvement in each of the three activities. Involvement with “Religious Youth Groups” was assessed by asking participants if they attended youth activities or youth groups associated with churches, synagogues or other places of worship in the past year (“never,” “less than once a month,” “once a week or more”).

Violence Outcomes Involvement in any violent behavior is based on a six-item measure that asked the participants if they engaged in different forms of violence in the past year: 1 ⫽ “serious physical fighting”; 2 ⫽ “injuring someone in a physical fight”; 3 ⫽ “robbing someone”; 4 ⫽ “group fighting;” 5 ⫽ “pulling a knife or gun on someone”; and 6 ⫽ “shooting or stabbing someone.” The inter-item reliability for the violence measures were Cronbach alpha ⫽ 0.77 and Cronbach alpha ⫽ 0.80 at Time 1 and Time 2, respectively. The violence measure is dichotomized to indicate engaging in any violence. In the cross-sectional sample assessed at Time 1, 4234 adolescent drinkers (48%) were violent. In the longitudinal sample assessed at Time 2, 2148 adolescent drinkers were violent (36%). (Note that those participants who were in 12th grade at Time 1 were not included for participation in the Time 2 survey, which likely explains the differences in the prevalence of violence between Time 1 and Time 2.)

Analysis Analyses were limited to current drinkers. To be classified as a “current drinker,” two criteria needed

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to be met. First, drinkers must have consumed a drink of beer, wine, or liquor (not just a sip or a taste of somebody else’s drink) more than two or three times in their lives [11]. Second, drinkers must have consumed alcohol in the past year. Respondents who did not meet both of these criteria were classified as “nondrinkers” and were excluded from the analyses. The sample of current drinkers represents about half (47.0%; n ⫽ 8885) of the adolescents in the probability sample at Time 1. Data were weighted to provide national estimates. Weights were assigned to each participant based on grade, gender, and race. They were also assigned according to the sampling frame, which over sampled adolescents who had a disability, who were Puerto Rican, Cuban, Chinese, or who were AfricanAmerican with highly educated parents. The SUDAAN statistical software package was employed to accommodate the complex sampling design and to provide accurate standard errors for the analyses [22]. Cross-sectional and longitudinal multivariate logistic regression models were computed. The crosssectional and longitudinal analyses were similar in that all 39 independent variables were assessed at Time 1. In the cross-sectional analyses, the outcome was also measured at Time 1. In the longitudinal analyses, the outcome was measured at Time 2. In the longitudinal analyses, the sample was restricted to just those adolescents at Time 1 who reported no involvement in any violent behavior. Thus, the longitudinal analyses predicted the initiation of violent behavior between Time 1 and Time 2. Before initiating analyses, all of the 39 variables were organized into 10 loosely defined domains, which were described earlier. The primary purpose of these domains was simply to create groupings of variables that would facilitate building an empirically driven model of statistically significant predictors of violent behavior. Next, a 3-step backward elimination strategy was used to create a multivariate logistic regression model. First, logistic regression analyses for both the cross-sectional and longitudinal outcome measures were performed separately within each of the 10 variable domains while controlling for age, gender, and race/ethnicity. These analyses determined which of the variables within each domain should be included in the multivariate model. The purpose of entering all variables within each domain simultaneously was to reduce the number of models computed, to identify the most important predictors in each domain, and to identify variables not statistically important. All variables

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with a p value (Wald Chi-square) equal to, or greater than, .15 were excluded from further analysis. The purpose of keeping variables not meeting the p ⫽ .05 criteria was that such stringent criteria may not identify all variables that may become important in the multivariate model. Removing unimportant variables, however, was important to achieve data reduction. Second, all variables that met criteria in Step 1 were entered simultaneously in a multivariate model while controlling for age, gender, and race/ethnicity. All variables with a p value greater than .05 were removed from the model, one at a time, until the model contained only significant variables. Additionally, interaction analyses were performed to determine if the significant predictor variables in the final model were moderated by age, gender, or race/ethnicity. Thus, interaction terms between each significant factor and age, gender, and race/ethnicity for the cross-sectional and longitudinal outcome measures were computed and examined in the multivariate models. Preliminary analyses revealed low variability or small sample sizes for some of the independent measures (e.g., drinking frequency, high volume drinking, GPA). Accordingly, such measures were dichotomized to provide more efficient analyses.

Results Correlates of Violent Behavior Violent behavior was common among adolescents who consume alcohol. The weighted prevalence estimate indicated that 48.8% (or about 5.1 million U.S. adolescent drinkers) engaged in some form of violent behavior in the past year. The prevalence of violent behavior was much higher for males (60.0%) than for females (37.3%), and also varied by age and race/ ethnicity (Table 2). The highest prevalence of violent behavior was observed among adolescents who had been suspended and expelled (79.9%), those in the top 25% on delinquent behavior (76.9%), and those who had sold drugs (75.5%) (analyses not shown). In contrast, the lowest prevalence of violent behavior was observed among those who had no trouble in school (32.6%), who had never been suspended or expelled (36.5%), who had a GPA between 3.0 and 4.0 (37.4%), or who did not report any illegal drug use (38.7%) (analyses not shown). Based on the initial analyses for the correlates of violent behavior, 8 of the 39 variables examined were not included in the second model-building step (i.e., shared decision-making, unsupervised drinking,

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mother’s drinking, mother’s alcoholism, closeness to school, TV viewing, and sports). A logistic regression analysis was then performed, which included the 31 remaining factors. After all nonsignificant correlates were removed, 14 variables remained in the final model assessing the cross-sectional correlates of violent behavior (Table 3). These significant correlates were: having a mother with less than a college education, depression, usually drinking seven or more drinks, frequent drinking, drinking alcohol alone, using illicit drugs, selling drugs, problems with drugs in the neighborhood (mother’s report), engaging in delinquent behavior, having trouble in school, repeating a grade in school, having been suspended or expelled from school, having low college expectations, and exercising weekly. These indicators all significantly increased the risk of engaging in violent behavior. Note that the reference group for age consists of those persons aged 19 –21 years. The preliminary interaction analyses of the correlates of violent behavior with age, gender, and race/ ethnicity revealed that five terms met the inclusion criteria for the multivariate model-building step. Of these five, two remained significant in the final multivariate model (Table 2). The first interaction term involved gender and the second term involved age. There were no significant interactions with race/ethnicity. The first interaction indicated that males who exercised weekly were less likely to engage in violent behavior. Stratified analyses (not shown) by gender showed that exercising weekly significantly increased risk of physical fighting for females (ORadj. ⫽ 1.74; 95% CI: 1.25–2.41) but not for males (ORadj. ⫽ 1.10; 95% CI: 0.86 –1.42). The second interaction indicated that adolescents 17 to 18 years of age who reported drinking 2–30 days per month had an increased risk of engaging in violent behavior. Stratified analyses (not shown) by age showed that drinking 2–30 days per month significantly increased the risk of violent behavior for those 17–18 years of age (ORadj. ⫽ 1.71; 95% CI: 1.31–2.22), but not for those 12–14 years (ORadj. ⫽ 1.33; 95% CI: 0.92–1.93) or 15–16 years of age (ORadj. ⫽ 0.93; 95% CI: 0.71–1.23). Test of the model fit (comparing model ⫺2 log likelihoods) suggested that the inclusion of the two interactions significantly improved the model (␹23 ⫽ 38.67; p ⬍ .001). Antecedent Predictors of Initiating Violent Behavior by Time 2 Among those adolescent drinkers who did not engage in any violent behavior at Time 1, 15.4% re-

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Table 2. Demographic Characteristics and Prevalence of Reporting Violent Behavior in the Cross-sectional and Prospective Samples Cross-sectional Sample (n ⫽ 8885)

Measures Gender Male Female Age (years) 12–14 15–16 17–18 19 –21 Missing Race/ethnicity Hispanic Non-Hispanic African-Am. Non-Hispanic Other Non-Hispanic White Missing Family structure Biological parents Two parents One parent Other Mother’s education ⬍ High school High school/GED ⬎ High school College degree Missing Receive welfare No Yes Missing

Prospective Sample (n ⫽ 2990)

N

Population Weighting (%)

Percent Reporting Initiating Violence

60.03 37.25

1125 1865

37.75 62.25

20.34 12.37

20.97 35.77 40.56 2.71

59.69 50.22 41.58 52.67

495 1408 1057 28 2

21.87 45.64 31.85 0.64

23.37 14.01 12.09 4.88

1580 1398 664 5201 42

11.54 11.98 4.43 72.05

58.90 62.14 48.26 44.89

461 365 199 1951 14

9.37 8.59 4.22 77.82

19.88 15.18 17.21 14.66

4309 1228 2754 594

49.83 12.51 30.66 7.01

44.19 48.19 55.17 54.14

1592 410 850 138

54.73 13.42 27.79 4.06

14.68 12.94 16.86 22.80

1483 2604 2510 2003 285

15.79 32.86 29.52 21.84

59.07 50.75 48.20 38.86

441 852 837 796 64

13.41 31.03 29.34 26.22

19.74 16.19 15.88 11.36

7747 861 277

90.26 9.74

47.17 64.02

2698 231 61

92.91 7.09

15.02 21.38

N

Population Weighting (%)

Percent Reporting Violence

4390 4495

50.63 49.37

1422 3426 3809 226 2

ported engaging in violent behavior at Time 2 (1 year later). There were demographic differences with respect to who initiated violent behavior at Time 2. Males were more likely than females to initiate violent behavior (20.3% and 12.4%, respectively). Initiation of violence also varied by age (Table 1). However, there was almost no variation for initiation of violence based on race/ethnicity. The highest prevalence of violent behavior at Time 2 was observed among adolescents who had been suspended or expelled from school (42.2%), those who reported high-volume drinking 9 or more days a month (31.4%), and those who reported drinking alcohol more than 9 days per month (29.3%) (analyses not shown). In contrast, the lowest prevalence of initiating violent behavior was observed among those adolescents who had a GPA between 3.0 and 4.0 (11.2%), those who had a college-edu-

cated mother (11.4%), those who attended weekly church youth groups (11.5%), those who did not report any trouble in school (11.6%), and those adolescents who did not engage in any sports (11.9%) (analyses not shown). Based on the initial domain screening for the predictors of initiating violent behavior by Time 2, 26 of the variables were removed from further analyses (i.e., economic situation, shared decision-making, relationship to parents, family activities, psychological counseling, usual drinking quantity, unsupervised drinking, problem drinking, substance abuse treatment, drinking alone, access to alcohol, mother drinking, mother has alcoholism, selling drugs, access to drugs, neighborhood drug use/dealing, days absent from school, trouble at school, closeness to school, held back in school, college expectations, TV viewing, involvement in sports, exercise, participa-

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Table 3. Multivariate Logistic Regression Models of the Correlates of Violent Behavior Among Adolescent Drinkers (n ⫽ 7905) Model Without Interaction Terms Measures Gender Male Female Age (yrs) 12–14 15–16 17–18 19 –21 Race/ethnicity Hispanic Non-Hispanic African-Am. Non-Hispanic Other Non-Hispanic White Mother ⬍ college degree Depression Usually drinks ⱖ7 drinks in each drinking occasion Drinks 2–30 days/month Drinks alone Any illicit drug use Sell drugs Neighborhood drug use/ dealing Delinquency Troubles at school Held back in school Suspended/expelled Low college expectations Exercise weekly Exercise weekly* male Drinks 2–30 days/month* 17–18 years Intercept Overall Model Wald F ␹2 ⫺2 Log Likelihood Ratio ⫺2 Log Likelihood

B

ORadj.

Model With Interaction Terms

(95% CI)

B

ORadj.

(95% CI)

0.86***

2.36 1.00

(2.02–2.76)

1.22***

3.38 1.00

(2.30 – 4.97)

1.43*** 0.74* 0.28

4.17 2.10 1.32 1.00

(2.29 –7.58) (1.18 –3.73) (0.73–2.42)

1.18*** 0.64* ⫺0.09

3.26 1.89 0.91 1.00

(1.66 – 6.41) (1.01–3.54) (0.47–1.78)

0.38** 0.68*** 0.12

(1.16 –1.85) (1.59 –2.45) (0.80 –1.59)

0.39** 0.69*** 0.14

(1.13–1.58) (1.08 –1.50) (1.20 –1.65)

0.29*** 0.24** 0.35***

1.47 2.00 1.16 1.00 1.34 1.27 1.41

(1.16 –1.86) (1.61–2.48) (0.82–1.63)

0.29*** 0.24** 0.34***

1.47 1.97 1.13 1.00 1.34 1.27 1.41

(1.14 –1.58) (1.08 –1.49) (1.20 –1.66)

0.24*** 0.35*** 0.24*** 0.39** 0.22**

1.27 1.42 1.27 1.48 1.25

(1.12–1.44) (1.22–1.65) (1.11–1.54) (1.15–1.92) (1.06 –1.48)

⫺0.26 0.34*** 0.24*** 0.40** 0.23**

0.77 1.41 1.27 1.49 1.26

(0.36 –1.65) (1.21–1.63) (1.11–1.45) (1.14 –1.94) (1.07–1.48)

0.90*** 0.42*** 0.38*** 0.79*** 0.56*** 0.27**

2.47 1.53 1.46 2.21 1.75 1.31

(1.96 –3.11) (1.28 –1.82) (1.22–1.73) (1.88 –2.59) (1.48 –2.06) (1.08 –1.58)

0.91*** 0.43*** 0.39*** 0.80*** 0.57*** 0.54** ⫺0.43* 0.81*

2.49 1.54 1.47 2.23 1.76 1.72 0.65 2.24

(1.98 –3.13) (1.29 –1.83) (1.24 –1.76) (1.90 –2.62) (1.49 –2.08) (1.23–2.39) (0.43– 0.99) (1.02– 4.90)

⫺3.34*** 56.57*** 2258.84** 8685.06

⫺3.37*** 49.65*** 2297.50** 8646.39

* p ⬍ .05; ** p ⬍ .01; *** p ⬍ .001.

tion in religious youth groups). The remaining 13 variables were included in the second model-building step. After all nonsignificant predictors were removed, only 4 of these 13 variables remained in the final model (Table 4). Significant predictors of initiating violent behavior were any high-volume drinking, illicit drug use, lower GPA, and having been suspended and/or expelled from school, all of which increased the risk of initiating violent behavior by Time 2 . Preliminary interaction analyses with age, gender, and race/ethnicity showed that three interaction terms should be included in the multivariate modelbuilding step. Two of these interaction terms became nonsignificant when included in the multivariate

model. The sole remaining interaction indicated that non-Hispanic African-Americans who reported any high-volume drinking were at increased risk of initiating violent behavior (Table 3). Stratified analyses (not shown) by race/ethnicity showed that highvolume drinking significantly increased the risk of violent behavior for African-American adolescents (ORadj. ⫽ 2.90; 95% CI: 1.53–5.50) and for Hispanic adolescents (ORadj. ⫽ 2.28; 95% CI: 1.01–5.17), but not for white adolescents (ORadj. ⫽ 1.27; 95% CI: 0.86 – 1.89) or adolescents representing other minority groups (ORadj. ⫽ 1.55; 95% CI: 0.26 –9.08). Test of the model fit suggested that the inclusion of the interaction term significantly improved the fit of the model (␹23 ⫽ 8.32; p ⬍ .05).

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Table 4. Multivariate Logistic Regression Models of the Predictors of Violent Behavior among Adolescent Drinkers (n ⫽ 2828) Model Without Interaction Terms Measures Gender Male Female Age (years) 12–14 15–16 17–18 Race/ethnicity Hispanic Non-Hispanic African-American Non-Hispanic Other Non-Hispanic White Any high-volume drinking (HVD) Any illicit drug use Grade point average ⬍ 3.0 Any Suspension/Expulsions Any HVD* non-Hispanic African-American Intercept Overall model Wald F ␹2 ⫺2 Log likelihood ratio ⫺2 Log likelihood

B

ORadj.

Model With Interaction Terms

(95% CI)

B

ORadj.

(95% CI)

0.55***

1.73

(1.26 –2.36)

0.53***

1.71

(1.26 –2.32)

1.26*** 0.37*

3.52 1.44 1.00

(2.42–5.13) (1.00 –2.08)

1.25*** 0.37*

3.48 1.45 1.00

(2.39 –5.07) (1.01–2.10)

1.43 0.91 1.25 1.00 1.43 1.43 1.68 1.79

(0.90 –2.27) (0.58 –1.44) (0.55–2.85)

⫺0.01 ⫺0.55 0.09

(0.50 –1.96) (0.29 –1.14) (0.44 –2.74)

(1.02–2.01) (1.07–1.92) (1.19 –2.39) (1.25–2.55)

0.19 0.37* 0.53** 0.57** 1.00** ⫺3.16*** 49.41*** 182.47** 2224.31

0.99 0.58 1.10 1.00 1.21 1.45 1.70 1.77 2.72

0.36 ⫺0.09 0.23 0.36* 0.36* 0.52** 0.58*** ⫺2.09*** 59.99*** 174.15** 2232.63

(0.83–1.78) (1.08 –1.94) (1.20 –2.41) (1.24 –2.54) (1.32–5.60)

* p ⬍ .05; ** p ⬍ .01; *** p ⬍ .001.

Discussion Half (49%) of all adolescent drinkers reported engaging in violent behavior. The analyses revealed that a relatively broad array of factors is associated with violent behavior among adolescent drinkers. The factors significantly associated with violence pertain mostly to alcohol use, drug use and selling, exposure to drugs, delinquency, and poor school functioning. These factors have been identified in previous research as important predictors of violent behavior [16,23,24]. Two of the correlates of violent behavior identified in our study (drinking alone and weekly exercising) have not been previously identified as correlates of violent behavior and therefore warrant further investigation. The interaction analyses also showed that weekly exercising was a risk factor for involvement in violent behavior for females but not for males, and that frequent drinking was a risk factor for involvement in violent behavior for older adolescents (i.e., those aged 17–18 years) but not for younger adolescents. Involvement in violent behavior at Time 2, among those who did not report violent behavior at Time 1, was not common. This most likely reflects the fact that involvement in violent behavior is relatively stable across time [25]. Only a few of the variables examined were significant longitudinal predictors of

the initiation of violent behavior. These predictors were high-volume drinking, illicit drug use, low GPA, and having been suspended or expelled from school. The final model also included one significant interaction, indicating that high-volume drinking was a particularly strong predictor of violent behavior among African-American adolescent drinkers. This finding is consistent with previous prospective research on young adult males that found that the relationship between alcohol consumption and violent behavior was stronger among African-Americans than among Whites [26]. The cross-sectional and longitudinal models of violence included both similar and different predictors of violence. For example, depression, usual drinking quantity, drinking alone, selling drugs, neighborhood drug use, delinquency, trouble in school, low college expectations, and frequent exercising (for girls) were significant cross-sectional correlates of violence, but were not significant longitudinal predictors of initiating violence. This finding implies that many of these problems and behaviors occur in close temporal proximity (i.e., within a year) to violent behavior but do not developmentally precede involvement in violent behavior. Previous research shows that many of these behaviors or problems tend to co-occur with violent behavior in a

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“problem behavior syndrome” [11,27]. Subsequently, the presence of these problem behaviors may facilitate a lifestyle that maintains involvement in violent behavior. It may also be the case that some of these problems (e.g., trouble in school, depression, and drinking alone) may be a reaction to, or a strategy to cope with, involvement in violent behavior. Additionally, several alcohol factors (e.g., usual drinking quantity and high-volume drinking) were either correlates or predictors of violent behavior. These findings suggest that intake levels play an important role not only situationally in relationship to involvement in violent behavior, but also as a developmental precursor to the initiation of violent behavior. Alcohol use can influence involvement in violent behavior in several ways. Situationally, highvolume drinking can result in disinhibition, which may increase the likelihood of a verbal argument escalating into a physical fight. Developmentally, heavy alcohol use may increase involvement in violent behavior over time, as persons who drink heavily and frequently are more likely to socialize with those who also drink heavily, perhaps resulting in more opportunities to engage in violent behavior. Illicit drug use and having been suspended or expelled from school were both predictors and correlates of violent behaviors. The psychopharmacological properties of some illicit drugs may facilitate involvement in violent behavior. However, illicit drug use is most likely linked to violent behavior (both as a correlate and a predictor) because of the illegal and competitive nature of the drug market [28]. Several of the explanatory domains identified in the literature as correlates or predictors of violent behavior were not important in this sample of current drinkers. For example, among the demographic characteristics, household structure and economic situation were not statistically significant in any of the multivariate models. Similarly, in terms of family functioning, the quality of the adolescent–parent relationship and the level of participation in family activities were not significant correlates or predictors of violent behavior. Mental-health factors (e.g., having received psychological counseling or reporting poor self-esteem) were also not statistically significant in the current analyses. Many of the alcohol and drug-exposure variables (e.g., access to alcohol in the home, frequency of mother’s alcohol consumption, frequency of father’s alcohol consumption, mother having alcoholism, father having alcoholism, peer drug use, and access to drugs) were also not statis-

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tically important. Finally, several measures of school functioning (e.g., truancy from school, and poor bonding to school) were not statistically significant correlates or predictors of violent behavior. There are several potential reasons why these factors were not statistically important in any of the models. First, many of the factors that were not identified as statistically significant correlates or predictors of violent behavior may have been captured by other similar variables included in the model. For example, truancy from school and poor school bonding were not statistically significant correlates of violent behavior in the cross-sectional analyses, but similar variables such as reporting trouble in school, having been held back in school, and having been suspended and expelled were significant. Second, many of the factors that were not identified as correlates or predictors of violence were most likely associated with adolescent drinking (e.g., access to alcohol in the home, frequency of mother’s and father’s alcohol consumption) and may therefore not have been significant predictors in these analyses because all of the adolescents were drinkers. Third, some of these factors may be linked with violent behavior among drinkers but were not selected for inclusion in our empirically driven model-building strategy. The current investigation extends previous literature in four important areas. First, unlike most previous investigations, this study documents both the correlates of violent behavior and the prospective predictors of violent initiation among adolescents who consume alcohol. Second, the current investigation includes a range of psychosocial factors that have been linked with either alcohol use or violent behavior, or both, but have rarely been examined simultaneously in multivariate analyses. Third, the current database (National Longitudinal Study of Adolescent Health) is unique in that it is one of very few current longitudinal studies that have been designed to measure both alcohol use and involvement in violent behaviors adequately while also measuring a wide range of psychosocial factors. Fourth, the investigation is based on a large national probability sample of adolescents permitting multivariate analyses of relatively rare events that is not possible with smaller datasets Limitations Several limitations of the present study should be noted. First, most of the data are from participants’ self-reports and are not corroborated by other

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sources of information. However, ACASI survey administration, which has been shown to increase valid and accurate reporting [29], was used to collect the data on violence and alcohol-related behaviors. Second, the term “initiation” of violence was defined somewhat loosely. Initiation of violent behavior should, strictly speaking, refer to the first time someone ever engaged in these behaviors. However, the interview questions did not ask about “ever” involvement in violent behavior. Some adolescents could have engaged in these behaviors before Time 1 but not during the 1-year time frame captured by the Time 1 interview. Therefore, some participants could have been classified erroneously as not yet having initiated violence. This limitation is mitigated, however, by the fact that violent behavior is highly stable across time and previous violent behavior is the best predictor of later violent behavior [25,30]. Thus, a person who did not engage in violent behavior at Time 1 was unlikely to have engaged in violent behavior previously. Third, many of the independent variables used in the current analyses were dichotomized, some before the analyses were initiated and others as a result of preliminary analyses indicating low variability or small cell sizes. Although dichotomizing variables results in loss of information, there are many important benefits of dichotomization. For example, many statistical techniques assume that variables are normally distributed and that a linear relationship exists between the predictor and outcome variables. These assumptions may be violated when examining certain behaviors (e.g., delinquency) that often have very skewed distributions and may not have a linear relationship with the outcome measures [31]. Using dichotomized data for both the predictor and outcome variables can minimize the violations of these assumptions except when there is a U-shape or other curvilinear relationship present. Also, dichotomization facilitates a ‘risk factor’ approach, enabling the quantification of risk for the development of intervention and prevention programs. Therefore, the use of dichotomized variables was the most appropriate strategy given the purposes of the current investigation. Lastly, the current investigation relied on a purely empirical assessment of the associations between psychosocial factors and involvement in violent behaviors among drinkers. Although prior theoretical or empirical support was established for the variables included in the investigation, the findings were derived only through empirical analyses without theoretical considerations. Information from this investigation can be used to guide future research as well as to guide

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the development of theoretical and explanatory models.

Conclusions The results from this investigation indicate that many adolescents at high risk for engaging in violent behavior experience a broad range of problems and behaviors including depression, difficulties in school, heavy and frequent alcohol use, and involvement and exposure to illegal drug use and selling. However, most of these problems and behaviors tend to occur in closer temporal proximity to violent behavior (i.e., within a year) and do not seem to developmentally precede initiation in violent behavior. More research is needed to disentangle the factors that precede involvement in violent behaviors versus those factors that co-occur with violent behavior. This paper is part of a dissertation completed at the University of Pittsburgh, Graduate School of Public Health by the first author under the direction of the second author. The dissertation committee members, Drs. Nancy Day, Edward Mulvey, and Joseph Costantino need special recognition for their thoughtful advice and helpful comments during the development and writing of this research. This research is based on data from the Add Health project, a program project designed by J. Richard Udry (PI) and Peter Bearman, and funded by grant P01-HD31921 from the National Institute of Child Health and Human Development to the Carolina Population Center, University of North Carolina at Chapel Hill, with cooperative funding participation by the National Cancer Institute; the National Institute of Alcohol Abuse and Alcoholism; the National Institute on Deafness and Other Communication Disorders; the National Institute on Drug Abuse; the National Institute of General Medical Sciences; the National Institute of Mental Health; the National Institute of Nursing Research; the Office of AIDS Research, NIH; the Office of Behavior and Social Science Research, NIH; the Office of the Director, NIH; the Office of Research on Women’s Health, NIH; the Office of Population Affairs, DHHS; the National Center for Health Statistics, Centers for Disease Control and Prevention, DHHS; the Office of Minority Health, Office of Public Health and Science, DHHS; the Office of the Assistant Secretary for Planning and Evaluation, DHHS; and the National Science Foundation. Persons interested in obtaining data files from the National Longitudinal Study of Adolescent Health should contact Jo Jones, Carolina Population Center, 123 West Franklin Street, Chapel Hill, NC 27516-3997 (E-mail [email protected]).

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