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research-article2014
JIVXXX10.1177/0886260514564161Journal of Interpersonal ViolenceKennedy and Adams
Article
The Effects of Cumulative Violence Clusters on Young Mothers’ School Participation: Examining Attention and Behavior Problems as Mediators
Journal of Interpersonal Violence 1–15 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0886260514564161 jiv.sagepub.com
Angie C. Kennedy1 and Adrienne E. Adams1
Abstract Using a cluster analysis approach with a sample of 205 young mothers recruited from community sites in an urban Midwestern setting, we examined the effects of cumulative violence exposure (community violence exposure, witnessing intimate partner violence, physical abuse by a caregiver, and sexual victimization, all with onset prior to age 13) on school participation, as mediated by attention and behavior problems in school. We identified five clusters of cumulative exposure, and found that the HiAll cluster (high levels of exposure to all four types) consistently fared the worst, with significantly higher attention and behavior problems, and lower school participation, in comparison with the LoAll cluster (low levels of exposure to all types). Behavior problems were a significant mediator of the effects of cumulative violence exposure on school participation, but attention problems were not. Keywords cumulative violence exposure, family violence, sexual victimization, cluster analysis, school participation, adolescent mothers 1Michigan
State University, East Lansing, USA
Corresponding Author: Angie C. Kennedy, School of Social Work, Michigan State University, 655 Auditorium Road, Baker Hall, Room 254, East Lansing, MI 48824, USA. Email: [email protected]
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For many youths, particularly those who are low-income and living in urban areas, exposure to two or more types of violence is common (Arata, Langhinrichsen-Rohling, Bowers, & O’Brien, 2007; Mrug, Loosier, & Windle, 2008). For example, witnessing intimate partner violence (IPV) and physical abuse by a caregiver are likely to co-occur within families (Margolin et al., 2009), while family violence is linked to community violence exposure and sexual victimization, the latter especially among girls (Berzenski & Yates, 2011; Kennedy, 2008). Indeed, female youths are more likely than their male counterparts to report high levels of cumulative violence exposure (Ford, Wasser, & Connor, 2011; Hazen, Connelly, Roesch, Hough, & Landsverk, 2009; Tubman, Oshri, Taylor, & Morris, 2011; Walrath et al., 2004), with adolescent mothers at increased risk (Kennedy, Bybee, & Greeson, in press). Cumulative exposure beginning in childhood may be particularly damaging, as stress sensitization theory suggests, because it sets the stage for heightened reactivity to later adverse experiences, thus increasing the odds of poor outcomes during adolescence and adulthood (Nurius, Uehara, & Zatzick, 2013). Associated poor outcomes include attention problems and attention deficit hyperactivity disorder (ADHD), behavior problems such as aggression and conduct disorder, and low participation in school (Hazen et al., 2009; Kennedy & Bennett, 2006; Margolin, Vickerman, & Gordis, 2010). Low school participation, which has been measured in different ways (e.g., grade failure, suspension or expulsion, dropout, or a composite), is a key risk factor over the life course: Dropping out of school, for example, predicts criminal involvement, poorer health, and reduced lifetime earnings (Henry, Knight, & Thornberry, 2012). For low-income youths of color— particularly adolescent mothers—who are disproportionately likely to drop out of school, the ramifications can be severe and contribute greatly to their cumulative disadvantage over time (Porche, Fortuna, Lin, & Alegria, 2011; Thoits, 2010). How might cumulative violence exposure beginning in childhood influence school participation in adolescence? Recent findings suggest that attention and behavior problems in school may be key mediating mechanisms. Researchers have found that experiencing co-occurring maltreatment is positively associated with attention problems in school (De Bellis, Woolley, & Hooper, 2013; Slade & Wissow, 2007). Hazen and colleagues (2009) used latent profile analysis to identify three profiles of cumulative maltreatment within a sample of 1,131 adolescents. They found that the two highest severity profiles were associated with increased attention problems. Tubman and colleagues’ (2011) study of maltreatment clusters among 300 youths receiving outpatient treatment found similar results: Among the three clusters identified, those in the high- and medium-severity clusters reported significantly
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higher ADHD symptoms. In turn, attention problems are predictive of multiple difficulties, including reduced school participation (Harpin, 2005; Slade & Wissow, 2007): As students experience an inability to concentrate, it can lead to discouragement and disengagement from school, which contributes to low participation and dropping out (Henry et al., 2012). A role for behavior problems in linking cumulative violence exposure with poor school participation has likewise been supported by empirical findings. Childhood co-occurring victimization is well-established as a predictor of behavior problems (Ford et al., 2011; Porche et al., 2011; Mrug et al., 2008; Trickett, Kim, & Prindle, 2011). Berzenski and Yates (2011) used latent class analysis to identify maltreatment patterns in a sample of 2,637 formerly maltreated undergraduates. Of the four classes identified, the class characterized by high levels of co-occurring physical and emotional abuse by a caregiver consistently had the highest rate of conduct problems. Similarly, Romano, Zoccolillo, and Paquette’s (2006) study of 252 pregnant Canadian adolescents employed latent class analysis and identified two classes: those participants with cumulative victimization and those without. They found that the cumulative victimization cluster was nearly four times as likely to report conduct disorder. In one of the few studies to examine community violence in tandem with other exposure types, Mrug and colleagues (2008) found that as cumulative victimization increased, aggression also increased within their sample of 601 adolescents. Once behavior problems are established, they may then lead to poor school participation by increasing the risk of suspension or expulsion, school disengagement, and poor relationships with prosocial peers (Dodge & Pettit, 2003; Porche et al., 2011). The current study seeks to add to the empirical literature on cumulative victimization and school outcomes by examining the relationship between violence exposure clusters and school participation, as mediated by attention and behavior problems in school, within a sample of adolescent mothers. This study complements our earlier work using cluster analysis with the same sample, in which we found that IPV victimization and homelessness were mediators of the effects of cumulative victimization clusters on depression (Kennedy et al., in press). The current study incorporates community violence exposure as a cluster-defining variable, as it appears to be a risk factor for youths’ reduced school participation, specifically adolescent mothers’ (Kennedy & Bennett, 2006; Porche et al., 2011). Grounded in stress sensitization theory, which emphasizes the role of early cumulative adversity and victimization as the key to later poor outcomes (Nurius et al., 2013), we address the following research questions: What are the relationships between clusters of lifetime violence exposure that began during
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childhood (community violence exposure, witnessing IPV, physical abuse by a caregiver, and sexual victimization), attention and behavior problems in school, and school participation within this community sample of pregnant and parenting adolescent women? To what extent do attention and behavior problems explain the association between cumulative violence exposure and school participation?
Method Research Design and Participants A community sample of 205 young women completed a self-administered questionnaire which assessed their experiences with violence and school outcomes. Study participants were eligible if they were between the ages of 16 and 21, pregnant or had previously birthed a child, and able to read English. We recruited participants at three sites in mid-Michigan located in an urban area: a county health department prenatal clinic (69%), a hospital-based prenatal clinic (17%), and a home-visiting program for adolescent mothers (14%). At the clinic sites, participants completed the consent process and the questionnaire in private at the clinic; the home visitors passed out flyers to mothers, who then called the research team to arrange a meeting (typically in participants’ residence) to complete the consent process and questionnaire. The Institutional Review Board approved the study, and participants were compensated $25. The sample (M age = 19.34 years, SD = 1.41) was predominately poor, with the vast majority (89%) reporting receipt of some sort of means-tested public assistance within the past year. Half (50%) of the participants were African American, with 28% White, 15% biracial or multiethnic, and 6% Latina. Average age at first pregnancy was 17.4 years (SD = 1.76), with 72% currently pregnant and 56% reporting they had at least one living child (M number of children = 1.47, SD = .74).
Measures Community violence exposure, beginning in childhood. Exposure to community violence was assessed using an adapted version of the Richters and Martinez (1990) Things I Have Seen and Heard scale, a widely used instrument for measuring youths’ exposure. The original scale includes 20 items that measure both community and family violence; for the current study, we used 8 community violence items, ranging from less severe witnessing to more severe victimization; we added 4 items that assessed witnessing or being robbed, and having a knife or gun used on oneself, in one’s neighborhood.
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Participants were asked if they had experienced any of the 12 items within their lifetime, with yes = 1, no = 0; if they endorsed at least one item we asked them how old they were when they first experienced it. Only lifetime exposure that began prior to age 13 was included; we created a total score by summing the items. This variable was used in the clusters; α = .82. Witnessing IPV, beginning in childhood. Lifetime exposure to physical violence between adults with onset prior to age 13 was assessed using the physical assault subscale of the Revised Conflict Tactics Scale (CTS2), which includes 12 items ranging in severity (Straus, Hamby, Boney-McCoy, & Sugarman, 1996). Participants were asked if they had witnessed these 12 types, with yes = 1, no = 0; if they endorsed at least one item, they were asked how old they were when they were first exposed. Only lifetime witnessing that began prior to age 13 was included; we derived a total score by summing the items. The CTS has been used extensively to assess family violence (Straus et al., 1996), including measuring children’s witnessing IPV. This variable was used in the clusters; α = .93. Physical abuse by caregiver, beginning in childhood. Lifetime physical abuse by a parent or adult caregiver beginning prior to age 13 was assessed using the 12-item physical assault subscale of the CTS2 described above (Straus et al., 1996) and coded in the same way (yes = 1, no = 0); likewise, if they endorsed at least one item, they were asked how old they were when they were first abused. Only lifetime physical abuse with onset prior to age 13 was included; we derived a total score by summing the items. The CTS has been widely used to measure physical abuse by a caregiver. This variable was used in the clusters; α = .92. Sexual victimization, beginning in childhood. Lifetime sexual victimization with onset before age 13 was assessed using an adapted version of Russell’s (1983) interview framework. The original framework consists of 20 yes or no items; it has been used extensively with adolescents and adults. Our adapted version is four items, including two items that assessed touching, one item on attempted rape, and one item on rape. Participants were asked if they had experienced each, with yes = 1, no = 0; if they endorsed at least one, they were asked how old they were when they were first sexually victimized. To capture severity, we computed a most severe sexual victimization variable, with touching beginning prior to age 13 as most severe experienced coded as a 1 (reported by 2.4%), attempted rape beginning prior to age 13 coded as a 2 (reported by 12%), and rape beginning prior to age 13 coded as a 3 (reported by 7%); this variable was used in the clusters.
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Attention and behavior problems in school. Attention problems were assessed using an adapted version of Achenbach’s (1991) Youth Self Report (YSR) attention problems subscale; the YSR is a widely used measure of youths’ problems and competencies in different domains (Achenbach, 1991). Participants who were not currently in school reported on the attention problems they experienced when they were last in school. The original subscale contains nine items; our adapted version is seven yes or no items, with yes = 1, no = 0. We computed a total score by summing the items; α = .81. Behavior problems were assessed using an adapted version of Achenbach’s (1991) YSR aggressive problems subscale. Participants who were not currently in school reported on the behavior problems they experienced when they were last in school. The original subscale contains 19 items; our adapted version is 12 yes or no items, with yes = 1, no = 0. We derived a total score by summing the items; α = .77. School participation. Given the cycling nature of adolescent mothers’ school participation (i.e., they are likely to drop out and be suspended/expelled numerous times; Kennedy & Bennett, 2006), we operationalized this as a composite made up of three items: currently attending or has graduated from high school or GED (yes = 1), school dropout history (no history = 1), and school suspension/expulsion history (no history = 1). Summing these three yielded scores from 0 (not currently attending nor has graduated, history of dropout and suspension/expulsion) to 3 (currently attending or has graduated, no history of dropout or suspension/expulsion), with higher scores indicating higher school participation.
Analytic Strategy Prior to cluster analysis, we standardized the variables to minimize differential weighting due to different scales. We used a two-stage approach, with four cluster-defining variables (community violence exposure, witnessing IPV, physical abuse by a caregiver, and sexual victimization). For Stage 1, we used Ward’s method of hierarchical agglomerative cluster analysis on squared Euclidean distances; a five-cluster solution was indicated via inspection of the sequential changes in fusion coefficients. For Stage 2, we started with the initial solution centroids and used K-means cluster analysis to assign cases to the nearest cluster, yielding the smallest possible within-cluster variances. To examine the relationships between cumulative violence exposure clusters and school participation, and determine to what extent attention and behavior problems mediated those relationships, we used Mplus 6 (Muthén & Muthén, 2010) to conduct path analysis and estimate the indirect, direct, and total effects; we used bias-corrected bootstrapping to test the significance of indirect effects
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Table 1. Descriptives and Correlations for Violence Exposure Variables Used for Clusters, Mediators, and School Participation. Variable
M
CV WIPV PAC SxV AttPr BehPr SchPart
1.64 5.06 2.99 0.48 2.36 2.49 1.52
SD
CV
2.90 1.00 4.17 .37*** 3.59 .43*** 0.96 .33*** 2.22 .18* 2.76 .18** 1.04 −.17*
WIPV 1.00 .58*** .21** .29*** .34*** −.23**
PAC
1.00 .27*** .30*** .31*** −.13
SxV
1.00 .23** .20** −.08
AttPr
BehPr
1.00 .61*** −.27***
1.00 −.33***
Note. CV = community violence exposure; WIPV = witnessing intimate partner violence; PAC = physical abuse by a caregiver; SxV = sexual victimization; AttPr = attention problems; BehPr = behavior problems; SchPart = school participation. *p < .05. **p < .01. ***p < .001.
(Preacher & Hayes, 2008). We dummy coded the clusters; the LoAll cluster (low exposure to all four types of violence) was the reference category.
Results Descriptives and Bivariate Correlations Experiences with violence beginning in childhood were widespread, with 30.2% exposed to community violence by age 13, 71.7% witnessing IPV, 57.6% physically abused by a caregiver, and 21.5% sexually victimized. More than three quarters (77.6%) reported exposure to two or more violence types beginning in childhood, with a majority (52.2%) experiencing three to four types by age 13. The average level of attention and behavior problems was 2.36 (SD = 2.22) and 2.49 (SD = 2.76), respectively; participants’ rate of school participation was 1.52 (SD = 1.04). The violence exposure variables were positively associated with one another, with rs ranging from .21 to .58; attention problems, behavior problems, and school participation were correlated with each other, and with almost all of the violence exposure variables (see Table 1).
Cumulative Violence Exposure Clusters and Their Relationship to Primary Variables Sixteen participants (7.8% of the total sample) comprised the first cluster, with high rates of exposure across all four types of violence beginning before
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Figure 1. Rate of each type of childhood violence exposure by cluster.
Note. Though clusters were derived using standardized scores, raw scores are presented for easier interpretation. CV = community violence exposure; WIPV = witnessing IPV; PAC = physical abuse by a caregiver; SxV = sexual victimization; HiAll = high all childhood violence exposure cluster; HiAllbutSxV = high all forms of violence exposure except sexual victimization cluster; HiWIPV = high witnessing IPV cluster; HiSxV = high sexual victimization cluster; LoAll = low all childhood violence exposure cluster.
age 13; this was the HiAll cluster (see Figure 1). The second was made up of 17 participants (8.3% of the sample) with high community violence exposure, witnessing IPV, and physical abuse by caregiver, but very low sexual victimization; this was the HiAllbutSxV cluster. The third consisted of 59 participants (28.8% of the sample) with high witnessing IPV, medium physical abuse by caregiver, low community violence exposure, and no sexual victimization; this was the HiWIPV cluster. The fourth was made up of 23 participants (11.2% of the sample) with high sexual victimization, medium witnessing IPV and physical abuse, and low community violence exposure; we termed this the HiSxV cluster. Ninety participants (43.9% of the sample) made up the fifth cluster, characterized by low exposure to all four types of violence and called the LoAll cluster. We examined the clusters in relation to
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Kennedy and Adams Table 2. Mediators and School Participation by Cumulative Violence Exposure Cluster.
Cluster HiAll HiAllbutSxV HiWIPV HiSxV LoAll F(df, N)
Attention Problems
Behavior Problems
School Participation
M (SD)
M (SD)
M (SD)
4.20a (2.38) 3.32a (2.57) 2.44 (2.14) 3.12a (2.52) 1.60b (1.77) 7.10***(4,205)
4.73a (3.57) 3.44a (2.96) 2.89a (2.76) 3.25a (3.18) 1.45b (1.95) 8.10***(4,205)
.69a (0.70) 1.18 (0.95) 1.47b (0.94) 1.78b (0.95) 1.70b (1.12) 4.36**(4,205)
Note. Different superscripts within column indicate significant pairwise differences. HiAll = high all childhood violence exposure cluster; HiAllbutSxV = high all forms of violence exposure except sexual victimization cluster; HiWIPV = high witnessing IPV cluster; HiSxV = high sexual victimization cluster; LoAll = low all childhood violence exposure cluster. *p < .05. **p < .01. ***p < .001.
demographics, attention and behavior problems, and school participation. There were no significant differences by cluster on race or ethnicity, age, pregnant versus parenting, age at first pregnancy, current residence, or total aid receipt, nor were any of these demographics related to school participation. There were significant differences by cluster in mean rate of attention problems, F(4, 205) = 7.10, p < .001; behavior problems, F(4, 205) = 8.10, p = .001; and school participation, F(4, 205) = 4.36, p = .002 (see Table 2).
Model of Indirect, Direct, and Total Effects We examined the indirect effects of the violence clusters via attention and behavior problems, the conditional direct effects, and the total effects (the sum of indirect and conditional direct effects) for each cluster on school participation. The model is saturated; therefore, no fit indices are reported. Indirect, direct, and total effects are shown in Table 3: HiAll, HiAllbutSxV, HiWIPV, and HiSxV all had significant indirect effects pathways to school participation, in comparison with LoAll. The model explains 13% of the variance in attention problems, 14% of the variance in behavior problems, and 16% of the variance in school participation. HiAll cluster. The sum of indirect effects from HiAll to school participation was significant (b = −.40, p = .003). The indirect effect of HiAll on participation through behavior problems was significant (b = −.29, p = .033),
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Table 3. Unstandardized Indirect, Conditional Direct, and Total Effects From Cumulative Violence Exposure Clusters to School Participation. Pathway
b
Sum of indirect effects HiAll→SchPart HiAll→AttPr→SchPart HiAll→BehPr→SchPart Conditional direct effect HiAll→SchPart TOTAL Effect HiAll→SchPart Sum of indirect effects HiAllbutSxV→SchPart HiAllbutSxV→AttPr→SchPart HiAllbutSxV→BehPr →SchPart Conditional direct effect HiAllbutSxV→SchPart TOTAL effect HiAllbutSxV→ SchPart Sum of indirect effects HiWIPV→ SchPart HiWIPV→ AttPr → SchPart HiWIPV→ BehPr → SchPart Conditional direct effect HiWIPV→ SchPart TOTAL effect HiWIPV→ SchPart Sum of indirect effects HiSxV→ SchPart HiSxV→ AttPr → SchPart HiSxV→ BehPr → SchPart Conditional direct effect HiSxV→ SchPart TOTAL effect HiSxV− SchPart
−.40** −.11 −.29* −.61** −1.01*** −.25* −.07 −.18† −.27 −.52* −.16** −.04 −.13* −.06 −.23 −.23* −.07 −.16† .31 .08
Bootstrapped 95% CI [−.75, −.19] [−.38, .08] [−.66, −.10] [−1.04,−.18] [−1.40, −.59] [−.52, −.08] [−.31, −.03] [−.47, −.04] [−.73, −.20] [−1.05, −.01] [−.31, −.08] [−.16, −.02] [−.26, −.04] [−.41, −.25] [−.58, −.09] [−.46, −.07] [−.24, −.04] [−.41, −.04] [−.15, −.76] [−.41, −.54]
Note. LoAll (low all childhood violence exposure) is the reference category for the cluster variables; CI = confidence interval;. HiAll = high all childhood violence exposure cluster; HiAllbutSxV = high all forms of violence exposure except sexual victimization cluster; HiWIPV = high witnessing IPV cluster; HiSxV = high sexual victimization cluster; AttPr = attention problems; BehPr = behavior problems; SchPart = school participation. †p < .10. *p < .05. **p < .01. ***p < .001.
indicating behavior problems as a significant mediator; the indirect effect through attention problems was not significant (b = −.11, p = .315). Compared with the LoAll cluster, HiAll was directly associated with attention problems (b = 2.60, p < .001) and behavior problems (b = 3.28, p < .001), and, after accounting for the direct effects through these, with school participation (b = −.61, p = .005). The total effect of HiAll on school participation was significant (b = −1.01, p < .001). HiAllbutSxV cluster. The sum of indirect effects from HiAllbutSxV (high community violence, witnessing IPV, and physical abuse by caregiver) to school participation was also significant (b = −.25, p = .022). The indirect effect of
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HiAllbutSxV on participation through behavior problems was a trend (b = −.18, p = .069), indicating behavior problems approach significance as a mediator; the indirect effect through attention problems was not significant (b = −.07, p = .348). Compared with the LoAll cluster, HiAllbutSxV was directly associated with attention problems (b = 1.72, p = .008) and behavior problems (b = 1.99, p = .009), but, after accounting for the direct effects through these, not participation (b = −.27, p = .247). The total effect of HiAllbutSxV on school participation was significant (b = −.52, p = .048). HiWIPV cluster. The sum of indirect effects from HiWIPV to school participation was significant (b = −.16, p = .003). The indirect effect of HiWIPV on participation through behavior problems was significant (b = −.13, p = .020), indicating behavior problems as a significant mediator; the indirect effect through attention problems was not significant (b = −.04, p = .349). Compared with the LoAll cluster, HiWIPV was directly associated with attention problems (b = .83, p = .012) and behavior problems (b = 1.44, p < .001), but, after accounting for the direct effects through these, not with participation (b = −.06, p = .713). The total effect of HiWIPV on school participation was not significant (b = −.23, p = .184). HiSxV cluster. The sum of indirect effects from HiSxV to school participation was significant (b = −.23, p = .021). The indirect effect of HiSxV on participation through behavior problems was a trend (b = −.16, p = .074), indicating behavior problems approach significance as a mediator; the indirect effect through attention problems was not significant (b = −.07, p = .347). Compared with the LoAll cluster, HiSxV was directly associated with attention problems (b = 1.51, p = .007) and behavior problems (b = 1.81, p = .010), but, after accounting for the direct effects through these, not with participation (b = .31, p = .185). The total effect of HiSxV on school participation was not significant (b = .08, p = .726).
Discussion Within this high-risk sample of poor young mothers living in an urban area in the Midwest, just over half (52.2%) were exposed to three or four types of violence beginning during childhood. By taking a person-centered approach via cluster analysis, we were able to examine patterns of cumulative violence exposure that suggest considerable heterogeneity. For example, while some clusters were characterized by very high levels of cumulative exposure, the largest cluster (43.9% of the sample) reported low exposure across all types. In exploring the relationships between cluster membership and attention and behavior problems, and school participation, we found that the HiAll cluster (high levels of
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community violence exposure, witnessing IPV, physical abuse by a caregiver, and sexual victimization, all with onset before age 13) consistently fared the worst, with significantly higher attention and behavior problems, and significantly lower school participation, in comparison with the LoAll cluster. As stress sensitization theory suggests, early experiences with cumulative victimization and adversity can have lasting effects because they dysregulate stress response systems, thus reducing the ability to adapt to later adverse events and increasing the risk for poor outcomes (Nurius et al., 2013; Thoits, 2010). For the young women in the HiAll cluster, the demands placed on them by school, coupled with poverty and motherhood, may present an enormous challenge. Unfortunately, poor school participation is likely to further contribute to their cumulative disadvantage over the life course, with ramifications for them and their children (Henry et al., 2012; Porche et al., 2011; Whitman, Borkowski, Keogh, & Weed, 2001). The second study goal was to examine attention and behavior problems as mediators of the clusters’ effects on school participation. We found that behavior problems were a significant mediator of the effects of both HiAll and HiWIPV on participation and approached significance for HiAllbutSxV and HiSxV; however, our results did not support attention problems as a mediator. For those participants who experienced high levels of cumulative violence across all four types (HiAll), or reported high witnessing IPV paired with moderate levels of physical abuse by a caregiver (HiWIPV), behavior problems in school were a key generative mechanism linking childhood-onset cumulative victimization with later participation. High levels of physical abuse by a caregiver in concert with other victimization have been associated with the highest rate of behavior problems (Berzenski & Yates, 2011; Mrug et al., 2008; Romano et al., 2006); our findings add support to this literature. Aggression may have particularly pernicious effects on school participation via several processes: It can lead to rejection by prosocial peers, thus fostering connections with antisocial peers and intimate partners, which in turn are associated with further conduct problems, delinquency, and school dropout; it is linked to school disengagement, itself a key risk for poor school participation; and within the context of school zero tolerance policies that target students of color, it is likely to lead to suspension (APA Zero Tolerance Task Force, 2008; Dodge & Pettit, 2003; Lanza, Rhoades, Nix, Greenberg, & The Conduct Problems Prevention Research Group, 2010; Porche et al., 2011). For many young mothers in the current study, behavior problems in school appear to represent a potent explanatory link between early victimization and later school participation. It should be noted that our study has several limitations. First, because young mothers are a difficult-to-reach population, we used a convenience
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sample recruited from several urban sites in the Midwest; given this, the generalizability of our findings is unknown. Future studies should examine cumulative violence exposure and school outcomes within a diverse array of samples, including rural youths. Second, we relied on participants’ self-report of their violence exposure and school outcomes. Third, several of our clusters were small, which may have resulted in a lack of power to find small or medium effects (e.g., the marginal significance of the indirect effects of both the HiAllbutSxV and HiSxV clusters on school participation through behavior problems). Despite these limitations, our results highlight the importance of examining patterns of cumulative victimization, as well as the mediating mechanisms linking childhood exposure to later academic outcomes, within high-risk samples. Researchers and practitioners should focus their attention on cumulative victimization and its effects on myriad outcomes, because it is the reality for many youths, especially young mothers. 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) received no financial support for the research, authorship, and/or publication of this article.
References Achenbach, T. M. (1991). Manual for the youth self-report and 1991 profile. Burlington: Department of Psychiatry, The University of Vermont. APA Zero Tolerance Task Force. (2008). Are zero tolerance policies effective in the schools? An evidentiary review and recommendations. American Psychologist, 63, 852-862. Arata, C. M., Langhinrichsen-Rohling, J., Bowers, D., & O’Brien, N. (2007). Differential correlates of multi-type maltreatment among urban youth. Child Abuse & Neglect, 31, 393-415. Berzenski, S. R., & Yates, T. M. (2011). Classes and consequences of multiple maltreatment: A person-centered analysis. Child Maltreatment, 16, 250-261. De Bellis, M. D., Woolley, D. P., & Hooper, S. R. (2013). Neuropsychological findings in pediatric maltreatment: Relationship of PTSD, dissociative symptoms, and abuse/ neglect indices to neurocognitive outcomes. Child Maltreatment, 18, 171-183. Dodge, K. A., & Pettit, G. S. (2003). A biopsychosocial model of the development of chronic conduct problems in adolescence. Developmental Psychology, 39, 349-371. Ford, J. D., Wasser, T., & Connor, D. F. (2011). Identifying and determining the symptom severity associated with polyvictimization among psychiatrically impaired children in the outpatient setting. Child Maltreatment, 16, 216-226.
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Harpin, V. A. (2005). The effect of ADHD on the life of an individual, their family, and community from preschool to adult life. Archives of Disease in Children, 90(Suppl. 1), i2-i7. Hazen, A. L., Connelly, C. D., Roesch, S. C., Hough, R. L., & Landsverk, J. A. (2009). Child maltreatment profiles and adjustment problems in high-risk adolescents. Journal of Interpersonal Violence, 24, 361-378. Henry, K. L., Knight, K. E., & Thornberry, T. P. (2012). School disengagement as a predictor of dropout, delinquency, and problem substance use during adolescence and early adulthood. Journal of Youth and Adolescence, 41, 156-166. Kennedy, A. C. (2008). An ecological approach to examining cumulative violence exposure among urban, African American adolescents. Child and Adolescent Social Work Journal, 25, 25-41. Kennedy, A. C., & Bennett, L. (2006). Urban adolescent mothers exposed to community, family, and partner violence: Is cumulative violence exposure a barrier to school performance and participation? Journal of Interpersonal Violence, 21, 750-773. Kennedy, A. C., Bybee, D., & Greeson, M. R. (in press). Intimate partner violence and homelessness as mediators of the effects of cumulative childhood victimization clusters on adolescent mothers’ depression symptoms. Journal of Family Violence. Lanza, S. T., Rhoades, B. L., Nix, R. L., Greenberg, M. T., & The Conduct Problems Prevention Research Group. (2010). Modeling the interplay of multilevel risk factors for future academic and behavior problems: A person-centered approach. Development and Psychopathology, 22, 313-335. Margolin, G., Vickerman, K. A., & Gordis, E. B. (2010). Violence exposure in multiple interpersonal domains: Cumulative and differential effects. Journal of Adolescent Health, 47, 198-205. Margolin, G., Vickerman, K. A., Ramos, M. C., Serrano, S. D., Gordis, E. B., Iturralde, E., . . . Spies, L. A. (2009). Youth exposed to violence: Stability, co-occurrence, and context. Clinical Child and Family Psychology Review, 12, 39-54. Mrug, S., Loosier, P. S., & Windle, M. (2008). Violence exposure across multiple contexts: Individual and joint effects on adjustment. American Journal of Orthopsychiatry, 78, 70-84. Muthén, L. K., & Muthén, B. O. (2010). Mplus user’s guide (6th ed.). Los Angeles, CA: Muthén & Muthén. Nurius, P. S., Uehara, E., & Zatzick, D. F. (2013). Intersection of stress, social disadvantage, and life course processes: Reframing trauma and mental health. American Journal of Psychiatric Rehabilitation, 16, 91-114. Porche, M. V., Fortuna, L. R., Lin, J., & Alegria, M. (2011). Childhood trauma and psychiatric disorders as correlates of school dropout in a national sample of young adults. Child Development, 82, 982-998. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879-891.
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Richters, J. E., & Martinez, P. (1990). Things I have seen and heard: An interview for young children about exposure to violence. Rockville, MD: Child and Adolescent Disorders Research Branch, Division of Clinical Research, National Institute of Mental Health. Romano, E., Zoccolillo, M., & Paquette, D. (2006). Histories of child maltreatment and psychiatric disorder in pregnant adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 45, 329-336. Russell, D. (1983). The incidence and prevalence of intrafamilial and extrafamilial sexual abuse of female children. In L. E. A. Walker (Ed.), Handbook on sexual abuse of children: Assessment and treatment issues (pp. 19-36). New York, NY: Springer Publishing. Slade, E. P., & Wissow, L. S. (2007). The influence of childhood maltreatment on adolescents’ academic performance. Economics of Education Review, 26, 604-614. Straus, M. A., Hamby, S. L., Boney-McCoy, S., & Sugarman, D. B. (1996). The Revised Conflict Tactics Scale (CTS2): Development and preliminary psychometric data. Journal of Family Issues, 17, 283-316. Thoits, P. A. (2010). Stress and health: Major findings and policy implications. Journal of Health and Social Behavior, 51(Suppl. 1), S41-S53. Trickett, P. K., Kim, K., & Prindle, J. (2011). Variations in emotional abuse experiences among multiply maltreated young adolescents and relations with developmental outcomes. Child Abuse & Neglect, 35, 876-886. Tubman, J. G., Oshri, A., Taylor, H. L., & Morris, S. L. (2011). Maltreatment clusters among youth in outpatient substance abuse treatment: Co-occurring patterns of psychiatric symptoms and sexual risk behaviors. Archives of Sexual Behavior, 40, 301-309. Walrath, C. M., Petras, H., Mandell, D. S., Stephens, R. L., Holden, E. W., & Leaf, P. J. (2004). Gender differences in patterns of risk factors among children receiving mental health services: Latent class analyses. Journal of Behavioral Health Services & Research, 31, 297-311. Whitman, T. L., Borkowski, J. G., Keogh, D. A., & Weed, K. (2001). Interwoven lives: Adolescent mothers and their children. Mahwah, NJ: Lawrence Erlbaum.
Author Biographies Angie C. Kennedy, PhD, is an associate professor of social work at Michigan State University. Her current research focuses on examining patterns of co-occurring and cumulative violence exposure among young women. Adrienne E. Adams, PhD, is an assistant professor of ecological/community psychology at Michigan State University. She has been an advocate and researcher in the violence against women field since 2000. Her research focuses on intervening to prevent and reduce the economic effects of intimate partner abuse on women. She also has expertise in evaluating community-based interventions and victim service programs.
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research-article2014
JIVXXX10.1177/0886260514564161Journal of Interpersonal ViolenceKennedy and Adams
Article
The Effects of Cumulative Violence Clusters on Young Mothers’ School Participation: Examining Attention and Behavior Problems as Mediators
Journal of Interpersonal Violence 1–15 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0886260514564161 jiv.sagepub.com
Angie C. Kennedy1 and Adrienne E. Adams1
Abstract Using a cluster analysis approach with a sample of 205 young mothers recruited from community sites in an urban Midwestern setting, we examined the effects of cumulative violence exposure (community violence exposure, witnessing intimate partner violence, physical abuse by a caregiver, and sexual victimization, all with onset prior to age 13) on school participation, as mediated by attention and behavior problems in school. We identified five clusters of cumulative exposure, and found that the HiAll cluster (high levels of exposure to all four types) consistently fared the worst, with significantly higher attention and behavior problems, and lower school participation, in comparison with the LoAll cluster (low levels of exposure to all types). Behavior problems were a significant mediator of the effects of cumulative violence exposure on school participation, but attention problems were not. Keywords cumulative violence exposure, family violence, sexual victimization, cluster analysis, school participation, adolescent mothers 1Michigan
State University, East Lansing, USA
Corresponding Author: Angie C. Kennedy, School of Social Work, Michigan State University, 655 Auditorium Road, Baker Hall, Room 254, East Lansing, MI 48824, USA. Email: [email protected]
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For many youths, particularly those who are low-income and living in urban areas, exposure to two or more types of violence is common (Arata, Langhinrichsen-Rohling, Bowers, & O’Brien, 2007; Mrug, Loosier, & Windle, 2008). For example, witnessing intimate partner violence (IPV) and physical abuse by a caregiver are likely to co-occur within families (Margolin et al., 2009), while family violence is linked to community violence exposure and sexual victimization, the latter especially among girls (Berzenski & Yates, 2011; Kennedy, 2008). Indeed, female youths are more likely than their male counterparts to report high levels of cumulative violence exposure (Ford, Wasser, & Connor, 2011; Hazen, Connelly, Roesch, Hough, & Landsverk, 2009; Tubman, Oshri, Taylor, & Morris, 2011; Walrath et al., 2004), with adolescent mothers at increased risk (Kennedy, Bybee, & Greeson, in press). Cumulative exposure beginning in childhood may be particularly damaging, as stress sensitization theory suggests, because it sets the stage for heightened reactivity to later adverse experiences, thus increasing the odds of poor outcomes during adolescence and adulthood (Nurius, Uehara, & Zatzick, 2013). Associated poor outcomes include attention problems and attention deficit hyperactivity disorder (ADHD), behavior problems such as aggression and conduct disorder, and low participation in school (Hazen et al., 2009; Kennedy & Bennett, 2006; Margolin, Vickerman, & Gordis, 2010). Low school participation, which has been measured in different ways (e.g., grade failure, suspension or expulsion, dropout, or a composite), is a key risk factor over the life course: Dropping out of school, for example, predicts criminal involvement, poorer health, and reduced lifetime earnings (Henry, Knight, & Thornberry, 2012). For low-income youths of color— particularly adolescent mothers—who are disproportionately likely to drop out of school, the ramifications can be severe and contribute greatly to their cumulative disadvantage over time (Porche, Fortuna, Lin, & Alegria, 2011; Thoits, 2010). How might cumulative violence exposure beginning in childhood influence school participation in adolescence? Recent findings suggest that attention and behavior problems in school may be key mediating mechanisms. Researchers have found that experiencing co-occurring maltreatment is positively associated with attention problems in school (De Bellis, Woolley, & Hooper, 2013; Slade & Wissow, 2007). Hazen and colleagues (2009) used latent profile analysis to identify three profiles of cumulative maltreatment within a sample of 1,131 adolescents. They found that the two highest severity profiles were associated with increased attention problems. Tubman and colleagues’ (2011) study of maltreatment clusters among 300 youths receiving outpatient treatment found similar results: Among the three clusters identified, those in the high- and medium-severity clusters reported significantly
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higher ADHD symptoms. In turn, attention problems are predictive of multiple difficulties, including reduced school participation (Harpin, 2005; Slade & Wissow, 2007): As students experience an inability to concentrate, it can lead to discouragement and disengagement from school, which contributes to low participation and dropping out (Henry et al., 2012). A role for behavior problems in linking cumulative violence exposure with poor school participation has likewise been supported by empirical findings. Childhood co-occurring victimization is well-established as a predictor of behavior problems (Ford et al., 2011; Porche et al., 2011; Mrug et al., 2008; Trickett, Kim, & Prindle, 2011). Berzenski and Yates (2011) used latent class analysis to identify maltreatment patterns in a sample of 2,637 formerly maltreated undergraduates. Of the four classes identified, the class characterized by high levels of co-occurring physical and emotional abuse by a caregiver consistently had the highest rate of conduct problems. Similarly, Romano, Zoccolillo, and Paquette’s (2006) study of 252 pregnant Canadian adolescents employed latent class analysis and identified two classes: those participants with cumulative victimization and those without. They found that the cumulative victimization cluster was nearly four times as likely to report conduct disorder. In one of the few studies to examine community violence in tandem with other exposure types, Mrug and colleagues (2008) found that as cumulative victimization increased, aggression also increased within their sample of 601 adolescents. Once behavior problems are established, they may then lead to poor school participation by increasing the risk of suspension or expulsion, school disengagement, and poor relationships with prosocial peers (Dodge & Pettit, 2003; Porche et al., 2011). The current study seeks to add to the empirical literature on cumulative victimization and school outcomes by examining the relationship between violence exposure clusters and school participation, as mediated by attention and behavior problems in school, within a sample of adolescent mothers. This study complements our earlier work using cluster analysis with the same sample, in which we found that IPV victimization and homelessness were mediators of the effects of cumulative victimization clusters on depression (Kennedy et al., in press). The current study incorporates community violence exposure as a cluster-defining variable, as it appears to be a risk factor for youths’ reduced school participation, specifically adolescent mothers’ (Kennedy & Bennett, 2006; Porche et al., 2011). Grounded in stress sensitization theory, which emphasizes the role of early cumulative adversity and victimization as the key to later poor outcomes (Nurius et al., 2013), we address the following research questions: What are the relationships between clusters of lifetime violence exposure that began during
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childhood (community violence exposure, witnessing IPV, physical abuse by a caregiver, and sexual victimization), attention and behavior problems in school, and school participation within this community sample of pregnant and parenting adolescent women? To what extent do attention and behavior problems explain the association between cumulative violence exposure and school participation?
Method Research Design and Participants A community sample of 205 young women completed a self-administered questionnaire which assessed their experiences with violence and school outcomes. Study participants were eligible if they were between the ages of 16 and 21, pregnant or had previously birthed a child, and able to read English. We recruited participants at three sites in mid-Michigan located in an urban area: a county health department prenatal clinic (69%), a hospital-based prenatal clinic (17%), and a home-visiting program for adolescent mothers (14%). At the clinic sites, participants completed the consent process and the questionnaire in private at the clinic; the home visitors passed out flyers to mothers, who then called the research team to arrange a meeting (typically in participants’ residence) to complete the consent process and questionnaire. The Institutional Review Board approved the study, and participants were compensated $25. The sample (M age = 19.34 years, SD = 1.41) was predominately poor, with the vast majority (89%) reporting receipt of some sort of means-tested public assistance within the past year. Half (50%) of the participants were African American, with 28% White, 15% biracial or multiethnic, and 6% Latina. Average age at first pregnancy was 17.4 years (SD = 1.76), with 72% currently pregnant and 56% reporting they had at least one living child (M number of children = 1.47, SD = .74).
Measures Community violence exposure, beginning in childhood. Exposure to community violence was assessed using an adapted version of the Richters and Martinez (1990) Things I Have Seen and Heard scale, a widely used instrument for measuring youths’ exposure. The original scale includes 20 items that measure both community and family violence; for the current study, we used 8 community violence items, ranging from less severe witnessing to more severe victimization; we added 4 items that assessed witnessing or being robbed, and having a knife or gun used on oneself, in one’s neighborhood.
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Participants were asked if they had experienced any of the 12 items within their lifetime, with yes = 1, no = 0; if they endorsed at least one item we asked them how old they were when they first experienced it. Only lifetime exposure that began prior to age 13 was included; we created a total score by summing the items. This variable was used in the clusters; α = .82. Witnessing IPV, beginning in childhood. Lifetime exposure to physical violence between adults with onset prior to age 13 was assessed using the physical assault subscale of the Revised Conflict Tactics Scale (CTS2), which includes 12 items ranging in severity (Straus, Hamby, Boney-McCoy, & Sugarman, 1996). Participants were asked if they had witnessed these 12 types, with yes = 1, no = 0; if they endorsed at least one item, they were asked how old they were when they were first exposed. Only lifetime witnessing that began prior to age 13 was included; we derived a total score by summing the items. The CTS has been used extensively to assess family violence (Straus et al., 1996), including measuring children’s witnessing IPV. This variable was used in the clusters; α = .93. Physical abuse by caregiver, beginning in childhood. Lifetime physical abuse by a parent or adult caregiver beginning prior to age 13 was assessed using the 12-item physical assault subscale of the CTS2 described above (Straus et al., 1996) and coded in the same way (yes = 1, no = 0); likewise, if they endorsed at least one item, they were asked how old they were when they were first abused. Only lifetime physical abuse with onset prior to age 13 was included; we derived a total score by summing the items. The CTS has been widely used to measure physical abuse by a caregiver. This variable was used in the clusters; α = .92. Sexual victimization, beginning in childhood. Lifetime sexual victimization with onset before age 13 was assessed using an adapted version of Russell’s (1983) interview framework. The original framework consists of 20 yes or no items; it has been used extensively with adolescents and adults. Our adapted version is four items, including two items that assessed touching, one item on attempted rape, and one item on rape. Participants were asked if they had experienced each, with yes = 1, no = 0; if they endorsed at least one, they were asked how old they were when they were first sexually victimized. To capture severity, we computed a most severe sexual victimization variable, with touching beginning prior to age 13 as most severe experienced coded as a 1 (reported by 2.4%), attempted rape beginning prior to age 13 coded as a 2 (reported by 12%), and rape beginning prior to age 13 coded as a 3 (reported by 7%); this variable was used in the clusters.
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Attention and behavior problems in school. Attention problems were assessed using an adapted version of Achenbach’s (1991) Youth Self Report (YSR) attention problems subscale; the YSR is a widely used measure of youths’ problems and competencies in different domains (Achenbach, 1991). Participants who were not currently in school reported on the attention problems they experienced when they were last in school. The original subscale contains nine items; our adapted version is seven yes or no items, with yes = 1, no = 0. We computed a total score by summing the items; α = .81. Behavior problems were assessed using an adapted version of Achenbach’s (1991) YSR aggressive problems subscale. Participants who were not currently in school reported on the behavior problems they experienced when they were last in school. The original subscale contains 19 items; our adapted version is 12 yes or no items, with yes = 1, no = 0. We derived a total score by summing the items; α = .77. School participation. Given the cycling nature of adolescent mothers’ school participation (i.e., they are likely to drop out and be suspended/expelled numerous times; Kennedy & Bennett, 2006), we operationalized this as a composite made up of three items: currently attending or has graduated from high school or GED (yes = 1), school dropout history (no history = 1), and school suspension/expulsion history (no history = 1). Summing these three yielded scores from 0 (not currently attending nor has graduated, history of dropout and suspension/expulsion) to 3 (currently attending or has graduated, no history of dropout or suspension/expulsion), with higher scores indicating higher school participation.
Analytic Strategy Prior to cluster analysis, we standardized the variables to minimize differential weighting due to different scales. We used a two-stage approach, with four cluster-defining variables (community violence exposure, witnessing IPV, physical abuse by a caregiver, and sexual victimization). For Stage 1, we used Ward’s method of hierarchical agglomerative cluster analysis on squared Euclidean distances; a five-cluster solution was indicated via inspection of the sequential changes in fusion coefficients. For Stage 2, we started with the initial solution centroids and used K-means cluster analysis to assign cases to the nearest cluster, yielding the smallest possible within-cluster variances. To examine the relationships between cumulative violence exposure clusters and school participation, and determine to what extent attention and behavior problems mediated those relationships, we used Mplus 6 (Muthén & Muthén, 2010) to conduct path analysis and estimate the indirect, direct, and total effects; we used bias-corrected bootstrapping to test the significance of indirect effects
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Table 1. Descriptives and Correlations for Violence Exposure Variables Used for Clusters, Mediators, and School Participation. Variable
M
CV WIPV PAC SxV AttPr BehPr SchPart
1.64 5.06 2.99 0.48 2.36 2.49 1.52
SD
CV
2.90 1.00 4.17 .37*** 3.59 .43*** 0.96 .33*** 2.22 .18* 2.76 .18** 1.04 −.17*
WIPV 1.00 .58*** .21** .29*** .34*** −.23**
PAC
1.00 .27*** .30*** .31*** −.13
SxV
1.00 .23** .20** −.08
AttPr
BehPr
1.00 .61*** −.27***
1.00 −.33***
Note. CV = community violence exposure; WIPV = witnessing intimate partner violence; PAC = physical abuse by a caregiver; SxV = sexual victimization; AttPr = attention problems; BehPr = behavior problems; SchPart = school participation. *p < .05. **p < .01. ***p < .001.
(Preacher & Hayes, 2008). We dummy coded the clusters; the LoAll cluster (low exposure to all four types of violence) was the reference category.
Results Descriptives and Bivariate Correlations Experiences with violence beginning in childhood were widespread, with 30.2% exposed to community violence by age 13, 71.7% witnessing IPV, 57.6% physically abused by a caregiver, and 21.5% sexually victimized. More than three quarters (77.6%) reported exposure to two or more violence types beginning in childhood, with a majority (52.2%) experiencing three to four types by age 13. The average level of attention and behavior problems was 2.36 (SD = 2.22) and 2.49 (SD = 2.76), respectively; participants’ rate of school participation was 1.52 (SD = 1.04). The violence exposure variables were positively associated with one another, with rs ranging from .21 to .58; attention problems, behavior problems, and school participation were correlated with each other, and with almost all of the violence exposure variables (see Table 1).
Cumulative Violence Exposure Clusters and Their Relationship to Primary Variables Sixteen participants (7.8% of the total sample) comprised the first cluster, with high rates of exposure across all four types of violence beginning before
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Figure 1. Rate of each type of childhood violence exposure by cluster.
Note. Though clusters were derived using standardized scores, raw scores are presented for easier interpretation. CV = community violence exposure; WIPV = witnessing IPV; PAC = physical abuse by a caregiver; SxV = sexual victimization; HiAll = high all childhood violence exposure cluster; HiAllbutSxV = high all forms of violence exposure except sexual victimization cluster; HiWIPV = high witnessing IPV cluster; HiSxV = high sexual victimization cluster; LoAll = low all childhood violence exposure cluster.
age 13; this was the HiAll cluster (see Figure 1). The second was made up of 17 participants (8.3% of the sample) with high community violence exposure, witnessing IPV, and physical abuse by caregiver, but very low sexual victimization; this was the HiAllbutSxV cluster. The third consisted of 59 participants (28.8% of the sample) with high witnessing IPV, medium physical abuse by caregiver, low community violence exposure, and no sexual victimization; this was the HiWIPV cluster. The fourth was made up of 23 participants (11.2% of the sample) with high sexual victimization, medium witnessing IPV and physical abuse, and low community violence exposure; we termed this the HiSxV cluster. Ninety participants (43.9% of the sample) made up the fifth cluster, characterized by low exposure to all four types of violence and called the LoAll cluster. We examined the clusters in relation to
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Kennedy and Adams Table 2. Mediators and School Participation by Cumulative Violence Exposure Cluster.
Cluster HiAll HiAllbutSxV HiWIPV HiSxV LoAll F(df, N)
Attention Problems
Behavior Problems
School Participation
M (SD)
M (SD)
M (SD)
4.20a (2.38) 3.32a (2.57) 2.44 (2.14) 3.12a (2.52) 1.60b (1.77) 7.10***(4,205)
4.73a (3.57) 3.44a (2.96) 2.89a (2.76) 3.25a (3.18) 1.45b (1.95) 8.10***(4,205)
.69a (0.70) 1.18 (0.95) 1.47b (0.94) 1.78b (0.95) 1.70b (1.12) 4.36**(4,205)
Note. Different superscripts within column indicate significant pairwise differences. HiAll = high all childhood violence exposure cluster; HiAllbutSxV = high all forms of violence exposure except sexual victimization cluster; HiWIPV = high witnessing IPV cluster; HiSxV = high sexual victimization cluster; LoAll = low all childhood violence exposure cluster. *p < .05. **p < .01. ***p < .001.
demographics, attention and behavior problems, and school participation. There were no significant differences by cluster on race or ethnicity, age, pregnant versus parenting, age at first pregnancy, current residence, or total aid receipt, nor were any of these demographics related to school participation. There were significant differences by cluster in mean rate of attention problems, F(4, 205) = 7.10, p < .001; behavior problems, F(4, 205) = 8.10, p = .001; and school participation, F(4, 205) = 4.36, p = .002 (see Table 2).
Model of Indirect, Direct, and Total Effects We examined the indirect effects of the violence clusters via attention and behavior problems, the conditional direct effects, and the total effects (the sum of indirect and conditional direct effects) for each cluster on school participation. The model is saturated; therefore, no fit indices are reported. Indirect, direct, and total effects are shown in Table 3: HiAll, HiAllbutSxV, HiWIPV, and HiSxV all had significant indirect effects pathways to school participation, in comparison with LoAll. The model explains 13% of the variance in attention problems, 14% of the variance in behavior problems, and 16% of the variance in school participation. HiAll cluster. The sum of indirect effects from HiAll to school participation was significant (b = −.40, p = .003). The indirect effect of HiAll on participation through behavior problems was significant (b = −.29, p = .033),
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Table 3. Unstandardized Indirect, Conditional Direct, and Total Effects From Cumulative Violence Exposure Clusters to School Participation. Pathway
b
Sum of indirect effects HiAll→SchPart HiAll→AttPr→SchPart HiAll→BehPr→SchPart Conditional direct effect HiAll→SchPart TOTAL Effect HiAll→SchPart Sum of indirect effects HiAllbutSxV→SchPart HiAllbutSxV→AttPr→SchPart HiAllbutSxV→BehPr →SchPart Conditional direct effect HiAllbutSxV→SchPart TOTAL effect HiAllbutSxV→ SchPart Sum of indirect effects HiWIPV→ SchPart HiWIPV→ AttPr → SchPart HiWIPV→ BehPr → SchPart Conditional direct effect HiWIPV→ SchPart TOTAL effect HiWIPV→ SchPart Sum of indirect effects HiSxV→ SchPart HiSxV→ AttPr → SchPart HiSxV→ BehPr → SchPart Conditional direct effect HiSxV→ SchPart TOTAL effect HiSxV− SchPart
−.40** −.11 −.29* −.61** −1.01*** −.25* −.07 −.18† −.27 −.52* −.16** −.04 −.13* −.06 −.23 −.23* −.07 −.16† .31 .08
Bootstrapped 95% CI [−.75, −.19] [−.38, .08] [−.66, −.10] [−1.04,−.18] [−1.40, −.59] [−.52, −.08] [−.31, −.03] [−.47, −.04] [−.73, −.20] [−1.05, −.01] [−.31, −.08] [−.16, −.02] [−.26, −.04] [−.41, −.25] [−.58, −.09] [−.46, −.07] [−.24, −.04] [−.41, −.04] [−.15, −.76] [−.41, −.54]
Note. LoAll (low all childhood violence exposure) is the reference category for the cluster variables; CI = confidence interval;. HiAll = high all childhood violence exposure cluster; HiAllbutSxV = high all forms of violence exposure except sexual victimization cluster; HiWIPV = high witnessing IPV cluster; HiSxV = high sexual victimization cluster; AttPr = attention problems; BehPr = behavior problems; SchPart = school participation. †p < .10. *p < .05. **p < .01. ***p < .001.
indicating behavior problems as a significant mediator; the indirect effect through attention problems was not significant (b = −.11, p = .315). Compared with the LoAll cluster, HiAll was directly associated with attention problems (b = 2.60, p < .001) and behavior problems (b = 3.28, p < .001), and, after accounting for the direct effects through these, with school participation (b = −.61, p = .005). The total effect of HiAll on school participation was significant (b = −1.01, p < .001). HiAllbutSxV cluster. The sum of indirect effects from HiAllbutSxV (high community violence, witnessing IPV, and physical abuse by caregiver) to school participation was also significant (b = −.25, p = .022). The indirect effect of
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HiAllbutSxV on participation through behavior problems was a trend (b = −.18, p = .069), indicating behavior problems approach significance as a mediator; the indirect effect through attention problems was not significant (b = −.07, p = .348). Compared with the LoAll cluster, HiAllbutSxV was directly associated with attention problems (b = 1.72, p = .008) and behavior problems (b = 1.99, p = .009), but, after accounting for the direct effects through these, not participation (b = −.27, p = .247). The total effect of HiAllbutSxV on school participation was significant (b = −.52, p = .048). HiWIPV cluster. The sum of indirect effects from HiWIPV to school participation was significant (b = −.16, p = .003). The indirect effect of HiWIPV on participation through behavior problems was significant (b = −.13, p = .020), indicating behavior problems as a significant mediator; the indirect effect through attention problems was not significant (b = −.04, p = .349). Compared with the LoAll cluster, HiWIPV was directly associated with attention problems (b = .83, p = .012) and behavior problems (b = 1.44, p < .001), but, after accounting for the direct effects through these, not with participation (b = −.06, p = .713). The total effect of HiWIPV on school participation was not significant (b = −.23, p = .184). HiSxV cluster. The sum of indirect effects from HiSxV to school participation was significant (b = −.23, p = .021). The indirect effect of HiSxV on participation through behavior problems was a trend (b = −.16, p = .074), indicating behavior problems approach significance as a mediator; the indirect effect through attention problems was not significant (b = −.07, p = .347). Compared with the LoAll cluster, HiSxV was directly associated with attention problems (b = 1.51, p = .007) and behavior problems (b = 1.81, p = .010), but, after accounting for the direct effects through these, not with participation (b = .31, p = .185). The total effect of HiSxV on school participation was not significant (b = .08, p = .726).
Discussion Within this high-risk sample of poor young mothers living in an urban area in the Midwest, just over half (52.2%) were exposed to three or four types of violence beginning during childhood. By taking a person-centered approach via cluster analysis, we were able to examine patterns of cumulative violence exposure that suggest considerable heterogeneity. For example, while some clusters were characterized by very high levels of cumulative exposure, the largest cluster (43.9% of the sample) reported low exposure across all types. In exploring the relationships between cluster membership and attention and behavior problems, and school participation, we found that the HiAll cluster (high levels of
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community violence exposure, witnessing IPV, physical abuse by a caregiver, and sexual victimization, all with onset before age 13) consistently fared the worst, with significantly higher attention and behavior problems, and significantly lower school participation, in comparison with the LoAll cluster. As stress sensitization theory suggests, early experiences with cumulative victimization and adversity can have lasting effects because they dysregulate stress response systems, thus reducing the ability to adapt to later adverse events and increasing the risk for poor outcomes (Nurius et al., 2013; Thoits, 2010). For the young women in the HiAll cluster, the demands placed on them by school, coupled with poverty and motherhood, may present an enormous challenge. Unfortunately, poor school participation is likely to further contribute to their cumulative disadvantage over the life course, with ramifications for them and their children (Henry et al., 2012; Porche et al., 2011; Whitman, Borkowski, Keogh, & Weed, 2001). The second study goal was to examine attention and behavior problems as mediators of the clusters’ effects on school participation. We found that behavior problems were a significant mediator of the effects of both HiAll and HiWIPV on participation and approached significance for HiAllbutSxV and HiSxV; however, our results did not support attention problems as a mediator. For those participants who experienced high levels of cumulative violence across all four types (HiAll), or reported high witnessing IPV paired with moderate levels of physical abuse by a caregiver (HiWIPV), behavior problems in school were a key generative mechanism linking childhood-onset cumulative victimization with later participation. High levels of physical abuse by a caregiver in concert with other victimization have been associated with the highest rate of behavior problems (Berzenski & Yates, 2011; Mrug et al., 2008; Romano et al., 2006); our findings add support to this literature. Aggression may have particularly pernicious effects on school participation via several processes: It can lead to rejection by prosocial peers, thus fostering connections with antisocial peers and intimate partners, which in turn are associated with further conduct problems, delinquency, and school dropout; it is linked to school disengagement, itself a key risk for poor school participation; and within the context of school zero tolerance policies that target students of color, it is likely to lead to suspension (APA Zero Tolerance Task Force, 2008; Dodge & Pettit, 2003; Lanza, Rhoades, Nix, Greenberg, & The Conduct Problems Prevention Research Group, 2010; Porche et al., 2011). For many young mothers in the current study, behavior problems in school appear to represent a potent explanatory link between early victimization and later school participation. It should be noted that our study has several limitations. First, because young mothers are a difficult-to-reach population, we used a convenience
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sample recruited from several urban sites in the Midwest; given this, the generalizability of our findings is unknown. Future studies should examine cumulative violence exposure and school outcomes within a diverse array of samples, including rural youths. Second, we relied on participants’ self-report of their violence exposure and school outcomes. Third, several of our clusters were small, which may have resulted in a lack of power to find small or medium effects (e.g., the marginal significance of the indirect effects of both the HiAllbutSxV and HiSxV clusters on school participation through behavior problems). Despite these limitations, our results highlight the importance of examining patterns of cumulative victimization, as well as the mediating mechanisms linking childhood exposure to later academic outcomes, within high-risk samples. Researchers and practitioners should focus their attention on cumulative victimization and its effects on myriad outcomes, because it is the reality for many youths, especially young mothers. 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) received no financial support for the research, authorship, and/or publication of this article.
References Achenbach, T. M. (1991). Manual for the youth self-report and 1991 profile. Burlington: Department of Psychiatry, The University of Vermont. APA Zero Tolerance Task Force. (2008). Are zero tolerance policies effective in the schools? An evidentiary review and recommendations. American Psychologist, 63, 852-862. Arata, C. M., Langhinrichsen-Rohling, J., Bowers, D., & O’Brien, N. (2007). Differential correlates of multi-type maltreatment among urban youth. Child Abuse & Neglect, 31, 393-415. Berzenski, S. R., & Yates, T. M. (2011). Classes and consequences of multiple maltreatment: A person-centered analysis. Child Maltreatment, 16, 250-261. De Bellis, M. D., Woolley, D. P., & Hooper, S. R. (2013). Neuropsychological findings in pediatric maltreatment: Relationship of PTSD, dissociative symptoms, and abuse/ neglect indices to neurocognitive outcomes. Child Maltreatment, 18, 171-183. Dodge, K. A., & Pettit, G. S. (2003). A biopsychosocial model of the development of chronic conduct problems in adolescence. Developmental Psychology, 39, 349-371. Ford, J. D., Wasser, T., & Connor, D. F. (2011). Identifying and determining the symptom severity associated with polyvictimization among psychiatrically impaired children in the outpatient setting. Child Maltreatment, 16, 216-226.
Downloaded from jiv.sagepub.com at University of British Columbia Library on November 14, 2015
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Harpin, V. A. (2005). The effect of ADHD on the life of an individual, their family, and community from preschool to adult life. Archives of Disease in Children, 90(Suppl. 1), i2-i7. Hazen, A. L., Connelly, C. D., Roesch, S. C., Hough, R. L., & Landsverk, J. A. (2009). Child maltreatment profiles and adjustment problems in high-risk adolescents. Journal of Interpersonal Violence, 24, 361-378. Henry, K. L., Knight, K. E., & Thornberry, T. P. (2012). School disengagement as a predictor of dropout, delinquency, and problem substance use during adolescence and early adulthood. Journal of Youth and Adolescence, 41, 156-166. Kennedy, A. C. (2008). An ecological approach to examining cumulative violence exposure among urban, African American adolescents. Child and Adolescent Social Work Journal, 25, 25-41. Kennedy, A. C., & Bennett, L. (2006). Urban adolescent mothers exposed to community, family, and partner violence: Is cumulative violence exposure a barrier to school performance and participation? Journal of Interpersonal Violence, 21, 750-773. Kennedy, A. C., Bybee, D., & Greeson, M. R. (in press). Intimate partner violence and homelessness as mediators of the effects of cumulative childhood victimization clusters on adolescent mothers’ depression symptoms. Journal of Family Violence. Lanza, S. T., Rhoades, B. L., Nix, R. L., Greenberg, M. T., & The Conduct Problems Prevention Research Group. (2010). Modeling the interplay of multilevel risk factors for future academic and behavior problems: A person-centered approach. Development and Psychopathology, 22, 313-335. Margolin, G., Vickerman, K. A., & Gordis, E. B. (2010). Violence exposure in multiple interpersonal domains: Cumulative and differential effects. Journal of Adolescent Health, 47, 198-205. Margolin, G., Vickerman, K. A., Ramos, M. C., Serrano, S. D., Gordis, E. B., Iturralde, E., . . . Spies, L. A. (2009). Youth exposed to violence: Stability, co-occurrence, and context. Clinical Child and Family Psychology Review, 12, 39-54. Mrug, S., Loosier, P. S., & Windle, M. (2008). Violence exposure across multiple contexts: Individual and joint effects on adjustment. American Journal of Orthopsychiatry, 78, 70-84. Muthén, L. K., & Muthén, B. O. (2010). Mplus user’s guide (6th ed.). Los Angeles, CA: Muthén & Muthén. Nurius, P. S., Uehara, E., & Zatzick, D. F. (2013). Intersection of stress, social disadvantage, and life course processes: Reframing trauma and mental health. American Journal of Psychiatric Rehabilitation, 16, 91-114. Porche, M. V., Fortuna, L. R., Lin, J., & Alegria, M. (2011). Childhood trauma and psychiatric disorders as correlates of school dropout in a national sample of young adults. Child Development, 82, 982-998. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879-891.
Downloaded from jiv.sagepub.com at University of British Columbia Library on November 14, 2015
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Richters, J. E., & Martinez, P. (1990). Things I have seen and heard: An interview for young children about exposure to violence. Rockville, MD: Child and Adolescent Disorders Research Branch, Division of Clinical Research, National Institute of Mental Health. Romano, E., Zoccolillo, M., & Paquette, D. (2006). Histories of child maltreatment and psychiatric disorder in pregnant adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 45, 329-336. Russell, D. (1983). The incidence and prevalence of intrafamilial and extrafamilial sexual abuse of female children. In L. E. A. Walker (Ed.), Handbook on sexual abuse of children: Assessment and treatment issues (pp. 19-36). New York, NY: Springer Publishing. Slade, E. P., & Wissow, L. S. (2007). The influence of childhood maltreatment on adolescents’ academic performance. Economics of Education Review, 26, 604-614. Straus, M. A., Hamby, S. L., Boney-McCoy, S., & Sugarman, D. B. (1996). The Revised Conflict Tactics Scale (CTS2): Development and preliminary psychometric data. Journal of Family Issues, 17, 283-316. Thoits, P. A. (2010). Stress and health: Major findings and policy implications. Journal of Health and Social Behavior, 51(Suppl. 1), S41-S53. Trickett, P. K., Kim, K., & Prindle, J. (2011). Variations in emotional abuse experiences among multiply maltreated young adolescents and relations with developmental outcomes. Child Abuse & Neglect, 35, 876-886. Tubman, J. G., Oshri, A., Taylor, H. L., & Morris, S. L. (2011). Maltreatment clusters among youth in outpatient substance abuse treatment: Co-occurring patterns of psychiatric symptoms and sexual risk behaviors. Archives of Sexual Behavior, 40, 301-309. Walrath, C. M., Petras, H., Mandell, D. S., Stephens, R. L., Holden, E. W., & Leaf, P. J. (2004). Gender differences in patterns of risk factors among children receiving mental health services: Latent class analyses. Journal of Behavioral Health Services & Research, 31, 297-311. Whitman, T. L., Borkowski, J. G., Keogh, D. A., & Weed, K. (2001). Interwoven lives: Adolescent mothers and their children. Mahwah, NJ: Lawrence Erlbaum.
Author Biographies Angie C. Kennedy, PhD, is an associate professor of social work at Michigan State University. Her current research focuses on examining patterns of co-occurring and cumulative violence exposure among young women. Adrienne E. Adams, PhD, is an assistant professor of ecological/community psychology at Michigan State University. She has been an advocate and researcher in the violence against women field since 2000. Her research focuses on intervening to prevent and reduce the economic effects of intimate partner abuse on women. She also has expertise in evaluating community-based interventions and victim service programs.
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