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. Author manuscript; available in PMC: 2018 Sep 9.
Published in final edited form as: J Clin Child Adolesc Psychol. 2014 Jan 31;44(3):446–455. doi: 10.1080/15374416.2013.865194

‘Why Me?’: Characterological Self-Blame and Continued Victimization in the First Year of Middle School

Hannah L Schacter 1, Samantha J White 1, Vickie Y Chang 1, Jaana Juvonen 1
PMCID: PMC6129379  NIHMSID: NIHMS539980  PMID: 24483145

Abstract

Objective:

This study examines the role of characterological self-blame as a unique risk factor associated with other known risk factors (depression and its behavioral and social correlates) for continued victimization across the first year of middle school.

Method:

Relying on a large, ethnically diverse sample of 1698 young adolescents (Mage=11.57, SD=.39; 55% female), self-report assessments in the fall and spring included perceptions of victim status, depressive symptoms, friendships, aggression, and responses to a hypothetical victimization vignette assessing both appraisals (characterological self-blame) and behavioral reactions (helpless responding).

Results:

In addition to depression, characterological self-blame emerged as the most consistent unique risk factor for subsequent victimization. Mediation analysis suggested that the continuity of victimization between fall and spring could be partially explained by increases in characterological self-blame and depressive symptoms. Additionally, cross-lagged panel analyses indicated reciprocal relations between peer victimization and characterological self-blame, suggesting cyclical processes.

Conclusions:

The study findings suggest that attribution-retraining in the beginning of middle school might help prevent escalating risk for continued peer victimization.

Keywords: Bullying, Victimization, Mental Health, Early Adolescence

Peer victimization increases in middle school and peaks during the first year in the new setting (Nylund, Bellmore, Nishina, & Graham, 2007; Pellegrini & Long, 2002). In the beginning of middle school, bullying may be relatively random as status hierarchies and alliances are formed, but by the spring bullying likely becomes less frequent and more targeted (Pellegrini & Bartini, 2000). The question is whether we can predict who remains a victim by the end of the year and identify which risk factors might help us account for continued victimization. Prior research suggests that there might be a particular psychological profile (i.e., set of interrelated symptoms) that characterizes a vulnerable population. Many repeatedly victimized youth display internalizing problems (see Reijntjes, Kamphuis, Prinzie, & Telch, 2010), lack friends (e.g., Hodges & Perry, 1999; Hodges, Boivin, Vitaro, & Bukowski, 1999; Malcolm, Jensen-Campbell, Rex-Lear, & Waldrip, 2006), and respond to bullying in a helpless or submissive manner (Salmivalli, Karhunen, & Lagerspetz, 1996). Although recognition of such a profile is helpful, these characteristics likely overlap and may simply reflect depression. In turn, it is not clear how to best intervene with victims (i.e., whether to address depression, teach assertiveness skills, or establish a buddy program), especially as students are starting in a new school setting and their teachers do not yet know them well. To be able to effectively prevent continued victimization as soon as youth start middle school, it is important to identify risk factors that are particularly amenable to intervention during this critical transition.

Emotional, Behavioral, and Social Risk Factors for Peer Victimization

Both depression and withdrawn responses to peer provocation have been associated with prolonged victimization. For example, depression heightens the risk of being victimized, and victimized youth show increases in depressive symptoms (Copeland, Wolke, Angold, & Costello, 2013; Reijntjes et al., 2010; Sweeting, Young, West & Der, 2006). Timid and withdrawn behavioral responses to hostile peer interactions (often correlated with depression) have also been found to predict continued victimization. This was particularly well demonstrated in an observational study of boys with a history of peer relationship problems (Schwartz, Dodge, & Coie, 1993). When playing with unacquainted boys, victimized boys tended to submit to peers’ hostile behaviors and became increasingly targeted across subsequent play sessions. They also became more withdrawn over time. Thus, internalizing symptoms and submissive responses to hostile interactions may operate cylically, leaving youth with such profiles particularly vulnerable to continued peer victimization.

In addition to emotional problems and maladaptive behavioral responses to bullying, lack of social support may also prolong the plight of victims. For example, youth who lack friendships are more likely to experience victimization (Hodges, Malone & Perry, 1997), and both internalizing and externalizing (i.e., aggression) problems appear to elicit bullying, especially when the targets are lower in social status (Hodges et al., 1999). Additionally, adolescents suffering from depression are likely to be bullied because they have difficulties in establishing friendships (Kochel, Ladd, & Rudolph, 2012), while low level of social support from close friends is also associated with increased externalizing symptoms (Prinstein, Boergers, & Vernberg, 2001). Hence, lack of friends and low perceived social support are associated with emotional problems and therefore may predict continued peer victimization. However, because of the likely overlap among these known emotional, behavioral, and social risk factors, it is unclear how to best prevent continued victimization.

Social Cognitions as Potential Risk Factors

One set of potential risk factors particularly suited for intervention, even during times of transitions, is causal attributions (Walton & Cohen, 2007; Wilson & Linville, 1985). When bullied, youth are likely to wonder why they were targeted. Answers to the “Why me?” question can provide us with important insights about how youth process and react to these negative experiences (Weiner, 1995); such attributions can be more or less adaptive, depending on their stability, locus, and controllability (Weiner, 1985). In particular, much of the research examining how youth make attributions for their plight has been guided by a social information processing (SIP) model, originally proposed by Dodge (1986) and later reformulated by Crick and Dodge (1994). This model outlines the steps involved in the processing of and responding to social cues in the environment. The model posits that youth must successfully encode information, interpret cues, clarify their goals, construct responses, decide on a response, and enact this response. The early phase of social information processing, when victims make attributions about their negative experience, might be critical in helping us account for continued victimization.

Within existing literature on attributions for victimization, characterological self-blame (CSB) has been identified as particularly maladaptive inasmuch as it is related to concurrent levels of emotional problems, including depression (Anderson, Miller, Riger, Dill, & Sedikides, 1994; Graham & Juvonen, 1998). CSB involves perceiving causes of negative experiences as internal, stable, and uncontrollable (e.g., “This sort of thing is more likely to happen to me than to others.”). This type of attribution can be contrasted with the more adaptive behavioral self-blame, which implies that the internal cause is unstable and modifiable (e.g., “I was at the wrong place at the wrong time”). Thus, it is critical to distinguish between behavioral versus characterological self-blame (Anderson et al., 1994; Janoff-Bulman, 1979; Tilghman-Osborne, Cole, Felton, & Ciesla, 2008). Only CSB is associated with the idea that one cannot do anything about bad experiences, a perception that is likely to characterize those youth who continue to get bullied over time.

In summary, past research suggests that depression, aggression, maladaptive reactions to bullying, lack of or unsupportive friendships, and characterological self-blame may all contribute to prolonged peer victimization. However, peer victimization, maladaptive behavioral responses to peer maltreatment, lack of social support, and self-blaming attributions are also associated with depression (Krackow & Rudolph, 2008; Rudolph, Kurlakowsky, & Conley, 2001). Therefore, it is critical to determine which of these risk factors might function as independent (i.e., non-overlapping) predictors of continued victimization across the first year in middle school.

Current Study

To our knowledge, no prior studies have examined the role of characterological self-blaming attributions in continued peer victimization. Based on the SIP model, we hypothesized that characterological self-blame would emerge as a distinct risk factor given its fundamental role in interpreting the experience of victimization. We first examine whether CSB during the fall predicts spring victimization when taking into account other known risk factors (i.e., fall victimization, depression, aggression, maladaptive responses to victimization, lack of friends and friendship support and gender). We then test whether the independent fall predictors of spring victimization can help account for the stability of victimization across the first year in middle school. We expected that students who endorse characterological self-blaming attributions for their victimization experiences during the fall of sixth grade are at risk for continued victimization in the spring. Although CSB is associated with both concurrent peer victimization and depression (Anderson et al., 1994; Graham & Juvonen, 1998), we presumed that a) not all victims self-blame, and b) both depressed and non-depressed youth can endorse characterological self-blame.

Given that attributional beliefs are likely to be shaped in part based on one’s social experiences, it is also important to study the direction of effects between victimization and characterological self-blame. As the final step of the study, we therefore conducted cross-lagged panel analyses to examine the possible cyclical associations between victimization experiences and characterological self-blame. The goal of these analyses was to provide further insights about the emergence and maintenance of such maladaptive attributions. Identifying and targeting both the risk factors and mechanisms that account for stability in victimization (e.g., self-blaming attributions) offers a potentially effective avenue for intervention and prevention during the critical middle school transition.

Method

To examine continued victimization during the first year in middle school, we rely on fall assessment of risk factors for spring victimization during sixth grade. The assessments include self-reported victimization, aggression, depressed mood, attributions of and responses to hypothetical peer victimization vignettes as well as friendship support. Reciprocal friendships were assessed based on mutual peer nominations, and aggression was measured based on teacher ratings.

Participants

Participants were drawn from a three-year longitudinal study of 2,307 students (55% girls) attending 11 public middle schools in Los Angeles metropolitan area. Student eligibility for free or reduced-price lunch programs ranged from 47–87%, indicating that a substantial proportion of the sample was of low socioeconomic background. The students were predominantly ethnic minority, comprised of 47% Mexican-American/Latino, 24% African-American, 11% Asian-American/Pacific Islander, 9% Caucasian, and 8% other (1% of students did not report).

Procedure

In the fall, 6th grade students were recruited from 99 homerooms distributed across 11 middle schools. To minimize the possibility of language or reading concerns, self-contained special education classes and ESL classes were excluded from study participation. Across the 11 participating schools, 75% of the 3,511 consent forms, both in English and Spanish, distributed to students were returned (N = 2,633). Of those students who returned a signed parent consent form, 91% received permission to participate in the study (N = 2,396). Only students with signed parent consent and student assent forms participated in the study. The sample for the current study included students with both fall and spring peer victimization data (n = 1,698). The analysis sample did not differ from those students who only had fall victimization data (n = 273) other than in terms of aggression, t(1,969) = 4.69, p < .001, d=.21 (fall data only group M = 2.86, SD = 1.80, analysis sample M = 2.30, SD = 1.51), suggesting that the current findings might not be generalizable to the most aggressive youth.

Participants completed written questionnaires in a classroom setting during both fall and spring of their first year in middle school. Before completing the questionnaire, students were informed about confidentiality. All questionnaires were administered in English. Students received a small token (e.g., a pen and eraser) and their classroom received $5 per student to be used toward general classroom supplies. The survey and procedures were approved by the University of California, Los Angeles Human Subjects Protection Committee and the school districts.

Measures

We first describe the measures that were used to assess students’ experiences of victimization and aggression. Next, indicators of depression and reactions to victimization are described, followed by friendship measures that were used to assess the presence and quality of participants’ friendships.

Peer victimization.

Peer victimization was measured using a modified version of Neary and Joseph’s Peer Victimization Scale (PVS; 1994), which assessed self-perceptions of being the target of peer aggression. The PVS has been found to be both reliable and valid (Callaghan & Joseph, 1995; Graham & Juvonen, 1998). For the purposes of the current study, it was important to rely on self-reported peer victimization, inasmuch as subjective perceptions of one’s plight were more relevant than peer or teacher perceptions when examining psychological well-being (Panak & Garber, 1992; Juvonen, Nishina, & Graham, 2001). The measure was modified from the PVS to include two additional items—being the target of rumors (a form of indirect or relational victimization) and of property damage/theft. A mean of the six items (scores ranged from 1 to 4) was calculated such that higher scores indicated stronger feelings of being victimized by peers (α = .81).

Aggression.

To control for externalizing problems (particularly relevant for aggressive victims), aggression was measured by homeroom teachers who rated social behavior via a shortened version of the Interpersonal Competence Scale (ICS-T; Cairns, Leung, Gest, & Cairns, 1995). This instrument contains a 3-item subscale that assesses aggression (i.e., starts fights, argues, gets in trouble). Each item was presented as a 7-point scale with higher scores indicating more aggression (α = .89).

Depressive symptoms.

Depressive symptoms were measured using the 10-item short form of the Children’s Depression Inventory (CDI; Kovacs, 1992). The mean of the 10 items (scores range from 0 to 2) was computed with higher scores indicating more depressive symptoms (α = .80).

Responses to victimization scale.

Responses to victimization were measured by presenting participants with a hypothetical scenario depicting the student as the target of peer harassment at school. To assess attributions about and responses to victimization, participants then rated how likely they would be to endorse 32 statements reflecting thoughts, feelings, and behaviors in response to the victimizing incident (Graham & Juvonen, 1998). Students were instructed to imagine the following scenario prior to responding to the items:

“Imagine that you’ve just bought your lunch after waiting in line for a long time. As you are walking away, someone in the line sticks out their foot and trips you. You’re not hurt, but most of your food spills on your clothes. The other kids in line start laughing at you.”

Based on two sets of exploratory factor analyses of the 32 items for both fall and spring responses, five factors were identified tapping characterological self-blame, behavioral self-blame, helpless responses to victimization, hostile responses to victimization, and avoidance (see Chang, 2007). Only the CSB subscale consisting of six items (e.g., “This sort of thing is more likely to happen to me than to other kids,” “If I were a cooler kid, I would not get picked on,” “I know this will happen to me again,” “Kids do this to me because they know I won’t cause trouble…because I won’t get back at them…because other kids also treat me this way.” α = .75) and the helpless responding subscale consisting of four items (e.g., “Feel like crying, helpless, scared, put down.” α = .76) were used for the current analyses. Based on past research (Graham and Juvonen, 1998), these two subscales are most conceptually consistent and robust. The other three subscales (behavioral self-blame, hostile responding to bullying, and avoidance) did not have adequate psychometric properties (α < .70) and were not included in the analyses.

Friendship support.

Friendship support was measured with three items: “I can count on my friends when things go wrong,” “I have friends with whom I can share my joys and sorrows,” and “I can talk about my problems with my friends.” These items were similar to those measuring support and closeness used in other friendship quality scales (e.g., Friendship Qualities Scale, Bukowski, Hoza, & Boivin, 1994; Friendship Quality Questionnaire, Parker & Asher, 1993). Students responded to the statements using a 5-point scale from “Not at all” to “All the time.” The scale mean was calculated such that higher scores reflected greater friend support (α = .71).

Friendships.

To determine the availability of a reciprocal friend, participants were presented with a roster that contained the names of all the students in their homeroom (typically 25–30 students). Using this roster, students nominated up to four of their classmates whom they “like to hang out with.” Both same and opposite sex nominations were allowed. Students who nominated each other were identified as reciprocal friends. The majority of participants (81%) reported at least one reciprocal friend.

Results

The results are divided into three sections. After examining the descriptive information mainly regarding the bivariate correlations among all variables during fall of sixth grade, we turn to the results of the main analyses. In the first set of analyses, we include all key variables to test the associations among fall risk factors and spring victimization. Based on the non-overlapping or independent risk factors identified, we then test a mediational model of continued victimization. In the final section, we describe the cross-lagged analyses between peer victimization and CSB across fall and spring.

Descriptive Findings

To examine the interrelations among all main variables, bivariate correlations were computed and appear in Table 1. As anticipated, all risk factors were related to concurrent perceptions of peer victimization. The same variables were also related to depressive symptoms. Of all the risk factors, victimization showed the strongest associations with characterological self-blame (r = .35, p < .01) and depressive symptoms (r = .37, p < .01) in the fall of sixth grade. Additionally, characterological self-blame and depressive symptoms were moderately related (r=.29, p<.01). The bivariate associations are consistent with prior research, but indeed raise questions about the independent power of these risk factors in predicting subsequent peer victimization.

Table 1.

Correlations between Peer Victimization, Responses to Victimization subscales, Psychosocial Maladjustment, and Friendship measures at Fall Assessment

Fall variables 1 2 3 4 5 6
1. Peer Victimization
2. Aggression .15**
3. Depressive Symptoms .37** .08**
4. CSB .35** .02 .29**
5. Helpless .17** −.07** .20** .41**
6. Friend Support −.23** −.08** −.32** −.20** −.02
7. Reciprocal Friend −.07** −.01 −.12** −.05 .01 .10**
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Note: CSB = Characterological Self-Blame.

**

p < .01

To test the underlying assumption guiding this research that peer victimization decreases between the fall and spring of sixth grade (possibly reflecting the fact that it becomes increasingly targeted), a paired samples t-test was conducted. As expected, reports of victimization decreased significantly from fall (M= 2.05, SD=.77) to spring (M=1.98, SD=.77), t (1697)=3.60, p < .001, d=.17. To better understand these differences, we also compared the prevalence of the most victimized youth (1 SD > mean) identified at each time point. The overlap between the fall and spring victims shows that about half (47%) of the students victimized in the fall continue to be victimized by the spring of the first year in middle school. The next set of main analyses help us then assess which risk factors place some youth at higher risk for victimization than others by the end of the sixth grade.

Predicting Spring Victimization

To explore whether previously identified risk factors for victimization contribute to subsequent victimization across the first year of middle school, a hierarchical multiple regression analysis was performed. We controlled for gender in the analysis because prior research suggests differences in risk factors between boys and girls (Kochenderfer & Ladd, 1997). Gender interactions (e.g., gender x helpless responding) were also tested. Because the gender moderator effects were not significant, they were omitted from the final analyses. While controlling for fall self-perceived victimization (fall SPV) and gender, self-perceived victimization in the spring (spring SPV) was regressed on teacher-rated aggression, depressive symptoms, characterological self-blame (CSB), helpless responses, and friendship support and the number of reciprocal friends. Predictors were standardized to reduce multicollinearity. Results showed that although fall SPV was the strongest predictor of spring SPV (β=.42, p <.001), both depressive symptoms (β =.12, p <.001) and CSB (β=.13, p <.001) significantly predicted spring SPV (see Table 2). These results suggest that both depressive symptoms and characterological self-blame increase the risk of victimization by the spring of 6th grade over and above fall victimization. In other words, depression and characaterological self-blame during fall contributed to the continuity of victimization during the first year in middle school.

Table 2.

Fall Psychosocial Maladjustment, Responses, and Protective Factors Predicting Spring Victimization (N =1522)

Predictors B (SE) β R2,ΔR2
Step 1 .29***
 Gender −.10 (.03) −.07**
 Fall Victimization .32 (.02) .42***
Step 2 .04***
 Fall Aggression .03(.02) .04
 Fall Depression .09 (.02) .12***
 Fall CSB .15 (.03) .13***
 Fall Helpless Responding .03 (.03) .03
 Fall Friend Support −.03(.02) −.04
 Fall Reciprocal Friend −.01 (.04) −.01
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Note. Final model: F (8, 1513) = 93.11, p < .001

CSB = Characterological Self-Blame

**

p<.01,

***

p < .001

Mediation

To be able to examine the mechanisms underlying continued victimization, we tested whether fall CSB and depressive symptoms mediate the association between fall SPV and spring SPV. Bootstrapping analyses were conducted using methods described by Preacher and Hayes (2008) for estimating direct and indirect effects with multiple mediators. For this model, spring SPV was entered as the dependent variable, fall SPV was entered as the independent variable, and fall CSB and depressive symptoms were entered as proposed mediators in the Preacher and Hayes SPSS macro for testing multiple mediators with bootstrapping.

The bootstrap results showed that the direct effect of fall SPV on spring SPV (total effect =.41, p <.001) remained significant when CSB and depression were in the model (direct effect = .33, p <.001), indicating that the effect of fall SPV on spring SPV was partially mediated (see Figure 1). The analyses revealed, with 95% confidence, that the total indirect effect fall SPV on spring SPV through CSB and depressive symptoms was significant, with a point estimate of .08 and a 95% bias-corrected bootstrap confidence interval of .06 to .09. Results showed a specific indirect effect of CSB with a point estimate of .04 and a 95% bias-corrected CI of .03 to .05, and depressive symptoms with a point estimate of .04 and a 95% bias-corrected CI of .02 to .05. In sum, the two risk factors partially mediated the association between fall SPV and spring SPV.

Figure 1.

Figure 1.

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Two-mediator model examining fall depressive symptoms and CSB as mediators of the association between fall and spring victimization. Note. *** p < .001 CSB = Characterological Self-Blame

Cross-Lagged Model

While CSB increases the risk of future victimization and partly accounts for the continuity of victimization during the first year in middle school, it is not clear what gives rise to such self-blaming attributions. Assuming that negative social experiences are necessary for anyone to blame themselves, we tested the directionality of the effects between fall and spring victimization. We expected that while such attributions may make youth increasingly vulnerable to victimization, frequent experiences of victimization should also increase the tendency for youth to believe that the causes of such experiences are internal and uncontrollable.

To test whether such reciprocal or cyclical associations exist, cross-lagged analyses were conducted. First, the paths from fall CSB to spring SPV (CSBf→SPVs) and fall SPV to spring CSB (SPVf→CSBs) were freely estimated. We then tested a model where these paths were constrained to be equal to each other. Although it is not necessary for the paths to be equal, a comparison of these two models (based on a chi square difference test) showed that they were not significantly different from each other, Δχ2(1)= 3.15, p = .08. These findings suggest that the paths are comparable across both directions, each making a similar contributions to model fit.

The final cross-lagged model in which the CSBf→SPVs and SPVf→CSBs paths are constrained to be equal to each other appears in Figure 2. The model fit the data very well and results suggested that fall CSB and SPV significantly contribute to spring SPV and CSB, respectively (see Table 3 for model coefficients). In other words, while tendency to blame oneself increases the risk of subsequent victimization, victimization experiences also increase the likelihood of blaming oneself.

Figure 2.

Figure 2.

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Cross-lagged model examining whether characterological self-blame and victimization equally contribute to subsequent experiences of victimization and characterological self-blame, respectively. Note. * p < .05

Table 3.

Estimate coefficients for the cross-lagged model.

Model χ2 df p CFI RMSEA NFI SRMR
Estimated 85.209 1 <.001 .933 .224 .932 .053
Constrained 88.355 1 <.001 .931 .160 .930 .057
Constrained Final Model† 2.948 1  .08 .998 .034 .998 .011
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†

Note. Model included T2 synchronous correlation.

Discussion

To complement past research on risk factors for persistent peer victimization (e.g., Kochenderfer-Ladd & Skinner, 2002; Salmivalli et al., 1996), the current findings help us understand which youth experience more than episodic peer maltreatment in the beginning of middle school. Our findings suggest that youth who are bullied, depressed, and self-blaming in the fall are most likely to be bullied by the end of the school year. Given that both depression and characterological self-blame partly accounted for the continuity of victimization between the fall and spring, the results offer insights into how to decrease the risk for continued victimization during the first year of middle school. Because youth have just started in a new school setting where teachers and staff do not necessarily know them well, it might be most efficacious to change their interpretations about their social experiences to prevent continued victimization. Moreover, given the partial overlap between characterological self-blame and depression, it is possible that an intervention conveying that bullying problems are temporary and controllable (rather than stable and uncontrollable by the victim) might not only help decrease the risk of continued victimization, but also alleviate concurrent depression. Importantly, these findings suggest that attributional retraining may also be applicable to non-depressed youth who are at risk of continued victimization.

As expected, the mean levels of self-reported victimization were higher in the fall than spring, suggesting that bullying becomes increasingly targeted during the first year in middle school. This might reflect the keenness of bullies in detecting the “safest” victims. Interestingly, however, overt behaviors and behavioral responses to victimization (aggression, helpless responses to victimization), and lack of friends did not independently contribute to subsequent victimization when taking into account depression and CSB. These results underscore the need to examine multiple risk factors simultaneously. This has been one of the limitations of past research: the potential overlap among different risk factors (e.g., depression and helpless responding, friendships) is impossible to assess when only a limited number are examined in any one study. Yet, from an intervention perspective, any such overlap needs to be understood when specifying the mechanisms that could be most effectively targeted.

Our finding that depressive symptoms increased the risk for subsequent harassment even when baseline level of victimization was taken into account is consistent with past literature (e.g., Kochenderfer-Ladd & Wardrop, 2001). We extend past research by demonstrating that other known risk factors may merely reflect such emotional difficulties (e.g., Kochel et al., 2012), whereas characterological self-blame (although also related to depression) was the only other unique factor accounting for continued victimization between the fall and spring of the sixth grade. This is consistent with the findings indicating that persistent victims of bullying are significantly less likely to report feeling in control of victimization experiences compared with those who are victimized only for a short time (Hunter & Boyle, 2002). Although we do not know how these youth act when victimized, these findings suggest that the ways victims construe their plight can self-perpetuate the way peers treat them.

Although attributions have been examined as they relate to peer victimization and subsequent maladjustment (Graham & Juvonen, 1998; Graham, Bellmore & Mize, 2006; Prinstein, Cheah, & Guyer, 2005), we are not aware of any studies investigating how they are related to the duration of victimization. By focusing specifically on characterological self-blame, our findings suggest that victims who attribute their plight to stable, internal, and uncontrollable causes are at greater risk for continued victimization. In contrast, behavioral self-blame, or attributing victimization to internal but unstable and modifiable causes, may actually be adaptive for victims (Janoff-Bulman, 1982). Therefore, when studying more general self-blame (e.g., “I must have done something to make this happen.”), it is possible to observe no association between victimization and self-blame (Perren, Ettekal, & Ladd, 2013). In light of our current findings, we believe that future studies of self-blaming attributions need to carefully differentiate between types of self-blame.

Why might characterological self-blame then account for continued peer victimization? The sense that one cannot do anything about one’s plight may raise expectations about negative future encounters (e.g., Downey & Feldman, 1996; Zimmer-Gembeck & Nesdale, 2013). Alternatively, such maladaptive attributions of one’s negative social experiences may lower the threshold for interpreting future encounters with peers as targeted intimidation (i.e., increasing sensitivity or salience of possible victimization). These specific processes need to be further explored in subsequent research.

But are there factors, other than bullying experiences, that give rise to victims blaming themselves for their plight? When examining the contextual school factors that may heighten self-blaming attributions, Graham and colleagues (2009) demonstrated that when victims of peer harassment belong to an ethnic majority group, as opposed to a minority group in their school, they are more likely to blame themselves for peer maltreatment. The authors presume that unlike ethnic minority youth (who may attribute their maltreatment to prejudice or discrimination against their group), members of the ethnic majority are likely to attribute their victimization experiences to internal factors, when most of their same-ethnicity peers are not victimized. Thus, certain contextual and situational factors can promote particular attributions (e.g., “It must be me”) over time. Examination of the associations among social experiences, contextual or situational factors, maladaptive appraisals, and internalizing problems over longer periods of time will enable us to better understand their complex (e.g., reciprocal and interactive) relations.

In terms of limitations, the reliance on hypothetical scenarios and self-reports, particularly in regard to attributions and behavioral responses, may depend too heavily on youth accurately assessing their own implicit cognitive processes (Prinstein et al., 2005). The need for approaches examining children’s in vivo appraisal processes, for instance with the use of daily reports that minimize memory biases (Espinoza, Gonzales, & Fuligni, 2012; Nishina & Juvonen, 2005), is an important direction for future research. By relying predominantly on self-reports, the current findings are also limited to offering explanations only for the intrapsychological mechanisms implicated in continued peer victimization. Additional data, especially from peers, are needed to complement the current measurement approach. Also, incorporation of additional contextual factors (e.g., classroom organizational practices, ethnic representation) into the analyses would further enrich the current approach as suggested above. Finally, it should be noted that the effect sizes were small.

In regard to the present findings, the power of characterological self-blame in predicting future victimization across initial harassment levels and depressive symptoms suggests that attributions are a potential avenue for intervention. When youth come to believe that it is “only them” who are bullied, they may feel particularly distressed. For example, analyses of daily report data suggest that humiliation is heightened when youth believe that only them were bullied, not when they realize that also others were targeted (Nishina & Juvonen, 2005). Other studies show that the association between victimization and emotional distress is stronger in orderly, as opposed to disorderly classrooms where victimization is more rampant (Bellmore, Witkow, Graham, & Juvonen, 2004), suggesting that the sense of “it’s only me” is heightened in classrooms with low victimization. Shifting the attention away from oneself and realizing that other students also get bullied may help decrease self-blame. However, helping youth recognize that they are not the only ones bullied may not be sufficient. Clearly, youth also need help in learning how to effectively respond to bullying not only “right then and there” but also afterwards. Nevertheless, an attributional approach to changing victims’ subjective interpretations offers an underutilized intervention method that can play an important role in helping to prevent the cycle of peer victimization.

Maladaptive attributions are especially likely to surface under unfamiliar and challenging conditions, such that interventions during critical transitions are particularly effective for promoting change (Haynes, Daniels, Stupinsky, Perry, & Hladkyj, 2008; Yeager & Walton, 2011). Interventions that target the attributional dimensions of stability and controllability appear to be especially promising for effecting change in maladaptive cognitions. In fact, attributional retraining has been effectively implemented to reduce aggressive tendencies among youth (Yeager, Miu, Powers & Dweck, 2013) and improve coping abilities among sexually abused children (see Celano, Hazzard, Campbell, & Lang, 2002 for review). Additionally, there are good models of how to implement attributional retraining as universal prevention programs during transition years in the achievement domain. For example, Wilson and Linville (1982, 1985) as well as Perry and colleagues (2010) have used attributional retraining videos to help improve incoming college students’ academic performance. Students who watched videos of upperclassmen talking about their academic difficulties during their freshman year being temporary (rather than persistent) and controllable (rather than uncontrollable) showed improvements in academic performance over time. Thus, extending these approaches that target maladaptive attributions, especially during the critical transition to middle school, may be an especially effective way to prevent youth from experiencing ongoing cycles of bullying.

Acknowledgements:

The earlier version of this manuscript was derived from Dr. Chang’s doctoral dissertation and was supported by the APA Minority Fellowship Program as well as T32 postdoctoral fellowship (5 T32 NIH MH019938). The study was supported by the National Science Foundation and the National Institutes of Child Health.

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