Impact Factor 2.990 | CiteScore 3.5
More on impact ›

ORIGINAL RESEARCH article

Front. Psychol., 25 June 2021 | https://doi.org/10.3389/fpsyg.2021.660973

When Does Rejection Trigger Aggression? A Test of the Multimotive Model

  • 1Social Science Research Center, Data Science for the Social Sciences Laboratory, Mississippi State University, Starkville, MS, United States
  • 2Social Science Research Center, Social Relations Collaborative, Mississippi State University, Starkville, MS, United States

Research has sought to identify the conditions under which rejection leads to retaliation. The Multimotive Model (MMM) proposes that there are three primary behavioral responses to rejection: prosocial (e.g., befriending others), asocial (e.g., withdrawal), and antisocial behavior (e.g., aggression toward others). In this study, we conducted the first full test of the MMM as well as expanded the model. Based on research linking aggression and “perceived groupness,” construal items were added assessing whether the rejection was perceived as extending beyond the individual to one's peers. We also included self-harm behavioral responses as this outcome was not sufficiently captured by existing antisocial or asocial operationalizations. This expanded model was then tested with two high school student samples (Ns of 231 and 374) who reported experiencing aggressive rejection (i.e., experienced physical, verbal, relational, or cyber aggression from peers). The MMM was compared to a saturated model separately in each of the two datasets using structural equation modeling. Results indicate that the saturated model provides a better fit for the data than the MMM across all models examined (all p < 0.001). In part, this is due to certain paths having different associations than hypothesized. For example, perceiving the rejection as carrying a higher cost was predicted to promote prosocial behavior, where instead it predicted asocial responses. Perceived groupness was the strongest predictor of antisocial responses. Self-harm outcomes were significantly and consistently associated with higher perceived costs across the models. These results and others will be discussed in the context of how we can better encourage prosocial and discourage antisocial and self-harm responses to social rejection, including bullying.

Introduction

The Secret Service and Department of Education's joint report on school violence in the United States (Vossekuil, 2004) and related empirical research (e.g., Kupersmidt et al., 1995; Leary et al., 2006) support the finding that social rejection (e.g., bullying, cyberbullying, romantic rejection, ostracism) precedes aggressive behavior. Leary et al. (2003) asserted that a history of chronic or acute peer rejection underlies aggression in schools, including 87% of school shootings. However, most youths experience rejection but do not respond aggressively (Kass, 1999). Although much research has focused on the “rejection-aggression” link [see Hutchinson et al. (2008) for review], rejection can trigger anti-social, pro-social, asocial (Richman and Leary, 2009) or self-harm behaviors (Hinduja and Patchin, 2010). Accordingly, Blackhart et al. (2006) asserted that understanding when and why youth who experience rejection do vs. do not respond aggressively is a pressing question for rejection researchers.

To address this call, Richman and Leary (2009) proposed the Multimotive Model (MMM) which synthesized 40 years of research on the rejection-aggression link to identify moderating variables that could predict whether rejection triggers anti-, pro-, or asocial behavior. To our knowledge, this model is largely untested. In the present paper, we test the MMM (Richman and Leary, 2009) to identify when rejection leads to aggression as opposed to more prosocial or asocial responses. We also expanded the model to explore associations with self-harm related outcomes. Identifying the pathways from rejection experiences to retaliation and or self-harm could facilitate the identification of opportunities for intervention to prevent the escalation of violence in our schools.

Background: Aggressive Rejection

Although several factors have been shown to increase aggression among adolescents, one of the key predictors of aggressive behavior is rejection (Leary et al., 2003). Rejection is a form of communication that conveys to the individual that there is something about him/her that is undesirable that warrants exclusion from social relationships/groups. Rejection can be expressed in multiple forms (e.g., physical or verbal aggression, bullying, shunning, or ostracism). Rejection can be active (where students are explicitly rejected or picked on directly by peers) or passive (where students feel invisible, left out). Whatever form it takes, the research is clear: rejection hurts (Eisenberger and Lieberman, 2004; Eisenberger, 2011; Landa et al., 2020). Chronic and acute social rejection have long-term negative psychological and physical consequences (Prinstein and La Greca, 2004; Modin et al., 2011; Gustafsson et al., 2012).

In the present study, we operationalized aggressive rejection as students self-identifying as having experienced physical, verbal, relational, or cyber aggression at the hands of one's peers. Physical aggression involves attempts to cause harm through hitting, shoving, or kicking others. Verbal aggression involves attempts to cause harm face to face by threatening another's self-concept, such as calling names. Relational aggression involves causing harm through gossip or exclusion from groups. Cyber aggression involves harming another through electronic means such as texting insulting messages or via sharing embarrassing social media posts. Bullied youth are thus included in our operationalization of rejected youth, as they are students who experience these forms of victimization repeatedly.

School Safety and Responses to Aggressive Rejection

Schools are still one of the safest places for children in the United States (May, 2014). Anti-bullying and school violence reduction programs are effective at reducing victimization and violent behavior in schools (Musu-Gillette et al., 2018). Even with rates of victimization declining for youth, still American youth reported 749,400 victimizations (theft and non-fatal violent victimization) on school property and 601,300 incidents away from school property (Musu-Gillette et al., 2018). In a nationally representative study of school safety, one in five (21%) students in U.S. schools reported experiencing traditional bullying (e.g., physical, verbal, relational) while 8% reported experiencing cyber bullying (Musu-Gillette et al., 2018). In a national sample of youth (6th−10th grade), Wang et al. (2009) found the majority of youth to experience verbal bullying (54%), followed by relational (51%), physical (21%), and cyber bullying (14%).

The consequences of these victimization experiences impact multiple spheres of youth's lives, including their psychological, physical, and academic well-being (Esbensen and Carson, 2009; McDougall and Vaillancourt, 2015). And, perhaps not surprisingly, being the target of peer victimization can increase aggressive responding as youth engage in self-defense or retaliation (Frey et al., 2015; Stubbs-Richardson and May, 2020), contributing to a cycle of aggression in schools (Frey and Strong, 2018). Clearly, there is more work to be done to reduce aggression in schools and to improve school responses to bullying (Hinduja and Patchin, 2010, 2019).

Although rejection can lead to aggressive behavior (Leary et al., 2003), most individuals who experience rejection do not engage in aggressive behavior, instead responding with pro-social behavior (DeWall, 2010; DeWall and Richman, 2011; DeWall et al., 2011; Knowles, 2014) while others who experience rejection choose to withdraw (Schoch et al., 2015; Sommer and Bernieri, 2015). Further, some internalize—engaging in self-harm or suicide (Hinduja and Patchin, 2010)—rather than externalize by lashing out at others (Leary et al., 2003; Reijntjes et al., 2010). After all, lashing out when rejected is somewhat counterintuitive (DeWall and Richman, 2011; Reijntjes et al., 2011; Sinclair et al., 2011). When one experiences a social rejection, it presents a threat to the fundamental need to belong (Baumeister and Leary, 1995; DeWall and Richman, 2011). Aggressing in response to rejection does not increase the aggressor's likelihood of being accepted; in fact, aggression is more likely to lead to further rejection (Leary et al., 2006). Thus, it begs the question why an individual would choose to aggress at all?

Accordingly, a number of researchers have called for the need to address when and why rejection triggers aggression (Blackhart et al., 2006; DeWall and Richman, 2011; Sinclair et al., 2011). In response to this call, Richman and Leary (2009) proposed the MMM to explicate the rejection-aggression link. However, the model remains untested. We seek to remedy this matter in the present research.

The Multimotive Model and the Rejection-Aggression Link

In the MMM, Richman and Leary (2009) suggested that individuals who encounter rejection are motivated to choose between three sets of behaviors. These options include: (1) prosocial behavior–seek acceptance due to heightened sense of desire for social connectedness; (2) antisocial behavior–lash out due to angry, aggressive urges related to self-defense or harming the rejection source; (3) asocial behavior–withdraw due to decreased sense of desire for social connectedness and to avoid future rejection and subsequent hurt feelings.

According to the MMM, the behavioral response one chooses hinges on an individual's construal of the rejection experience. Construals include judgments about the perceived: (1) cost of rejection, (2) availability of alternative relationships, (3) likelihood of being able to repair the relationship, (4) relationship value, (5) chronicity, and (6) rejection unfairness (see Figure 1). For example, according to Richman and Leary (2009) the likelihood of an aggressive response is increased when rejection is perceived as unwarranted (e.g., unfair, insulting, unnecessarily rude, based on inaccurate information); one does not highly value relationships (does not fear what relationships s/he may lose from aggressing); or when one has little hope for relationship repair with the rejecter(s). Ultimately, the behavioral outcome chosen hinges on an individual's construals (i.e., their interpretation of the rejecting event). If this model holds true, potential interventions aimed at altering perceptions could facilitate reduction of aggressive retaliation.

FIGURE 1
www.frontiersin.org

Figure 1. The modified multimotive model-predictions based on the multimotive model including anticipated groupness effect. Solid lines represents anticipated positive relationships. Dashed lines anticipated negative relaionships. Yellow lines and boxes were additions to the multimodel based on work on bullycide and perceivedgroupness effects.

Rejection and Self-harm

When originally proposed, the MMM did not include self-harm as a possible outcome. Arguably, self-harm could be conceived as a sub-type of anti-social responding, just directed toward the self rather than others. Alternatively, it could be viewed as an extreme form of social withdrawal, particularly suicide, as ultimately one would be withdrawing completely from everything. Likely, it has some overlap with both constructs. However, as rejection and bullying both have been increasingly linked to self-harm and suicide (e.g., “bullycide;” Hinduja and Patchin, 2010, 2019), it was an important outcome to consider. Prior research conducted among a sample of 2,000 middle school students found traditional bullying victims (physical, verbal, relational) were 1.7 times and cyberbullying victims were 1.9 times more likely than non-victims to attempt suicide (Hinduja and Patchin, 2010). Youth who are both victims and bullies (i.e., “bully-victims”) were at the greatest risk for suicide (Hay and Meldrum, 2010).

Rejection and Perceived Groupness

Rejection is a social phenomenon–it is a matter of how people relate. Aggression spurred by rejection does not occur within a vacuum. Thus, a model that focuses exclusively on individual impact may be missing context (i.e., group dynamics). Individuals can be targeted because of perceived group membership (Gaertner et al., 2008; Reijntjes et al., 2013; Utley et al., 2021). Likewise, an individual may choose to aggress against others in response to rejection by one because they perceive their rejecters as members of a group (Gaertner et al., 2008). Consequently, if the desired target is not available for victimization, displaced aggression–particularly aggression against those perceived as members of the “hated” group–occurs (Reijntjes et al., 2013).

Accordingly, we believe the MMM would benefit by taking “perceived groupness” (Gaertner et al., 2008) into consideration when trying to understand how rejection from one might trigger aggression against many. Gaertner et al. (2008) examined whether group membership of a rejecter was an important factor in experiencing rejection and found that participants were more likely to aggress against the rejecter when s/he was a member of a clearly defined group to which the participant did not belong [see also Schaafsma and Williams (2012)]. Participants generalized their aggression to other members of the group to which their rejectors belonged, even though those other group members had no direct involvement in the participant's exclusion. When the transgressing group is perceived as more cohesive (i.e., “they are all alike”), this displaced aggression is particularly satisfying to retaliatory aggressors (Sjöström and Gollwitzer, 2015). These findings overlap with a study of mass shooters' diaries and websites (Dutton et al., 2013). Researchers found evidence that mass shooters were obsessed with the perception that specific peer groups had unfairly wronged them (Dutton et al., 2013). For example, “Die Jock Die” was written on the backpacks of the Columbine shooters (Gaertner et al., 2008, p. 958) and Eric Harris was quoted as saying: “Isn't it fun to get the respect that we're going to deserve?” (Twenge and Campbell, 2003, p. 261).

Relatedly, those individuals who perceive they are rejected because of their own group membership are also more likely to engage in anti-social behaviors (Belmi et al., 2015). Lashing out is also more likely when an individual witnesses a member of their own group being targeted by others (Wesselmann et al., 2010; Coyne et al., 2011) because feeling empathy for the victim triggers defensive retaliation (Buffone and Poulin, 2014). In one study, targets of “connected victimization” [i.e., close connections with victimized peers; see also Peters et al. (2011)] were more likely to be disliked by their peers and were more likely to aggress than “isolated victims” (Zimmer-Gembeck et al., 2013). In another study, participants accompanied by co-targets who were excluded during a cyberball game were more aggressive toward rejecters than when sole targets, leading the researchers to conclude that when it comes to the impact of ostracism “there is no safety in numbers” (van Beest et al., 2012, p. 250). Based on this research we add a “perceived groupness” construal to capture the extent to which individuals felt their victimization was perpetrated by a group against their group.

The Current Study

To our knowledge, the present research is the first test of the full MMM within a high school context. Past research on reactions to rejection has typically focused on only one type of behavioral outcome. Only presenting participants with one behavioral option, aggression (e.g., determine the level at which you wish to blast your rejecter with white noise), might artificially inflate the likelihood of that option being used. To better represent the choices that individuals have in the real world, the full spectrum of anti- to pro-social options needs to be available. In addition, our study has the added benefits of:

1) Addressing both direct and indirect victimization, both offline and online.

2) Adding self-harm outcome variables.

3) Considering the role of groupness construals.

4) Testing this model in a high school sample that is diverse, largely rural, and lower socioeconomic status.

5) Replicating the survey in two high school samples.

To test the modified theoretical model, we developed instruments specific to operationalizing the construals and behavioral responses. In Year 1, we ran an initial pilot study including these scales and modified them for the subsequent years. The pilot data can be found on the Open Science Framework (OSF, https://osf.io/7wyf3/). We then ran a Year 2 survey which we replicated in Year 3 with a sample recruited from our local high school via active consent procedures. All students were asked about their experiences with physical, verbal, relational, and cyber aggressive rejection in their school. Any student reporting an aggressive rejection experience was asked follow-up questions regarding how they construed the experience and then how they responded (prosocially, antisocially, asocially, or with self-harm). All codebooks are also available on the OSF. Structural equation modeling was then used to test the theoretical model. Hypotheses, for example predicted pathways specified by Richman and Leary, are in Figure 1 as well as included in Table 3. We used SEM to test the model's hypothesized links between construals and behavioral responses. We also anticipated a positive link between perceived groupness and aggressive behavior as indicated by research on group dynamics. As self-harm was not an outcome included in the Multimotive model originally, we had no hypotheses regarding the links between construals and self-harm and thus analyses were exploratory for this fourth type of behavioral response.

Materials and Methods

Demographics

We surveyed high school students about their experiences with physical, verbal, relational, and cyber aggression across three years (see Table 1 for operationalizations). Year 1 included pilot data and is not included in this research paper. Years 2 (N = 374) and 3 (N = 231), depicted in Table 2, consisted of participants from a rural southeastern public high school in the United States. In Year 2, 50% of participants identified as female, 39% as male, and 11% as other/refused. The mean age was 15.9 years (SD = 1.2). Racially/ethnically, 50.8% of participants identified as Black non-Hispanic, 25.9% as White non-Hispanic, 2.7% as Hispanic, and 11.2% as other race/ethnicity. Regarding class standing, 24% of participants were classified as seniors, 25% as juniors, 24% as sophomores, and 17% as freshmen.

TABLE 1
www.frontiersin.org

Table 1. Definitions of types of bullying provided in survey of students.

TABLE 2
www.frontiersin.org

Table 2. Demographic characteristics of the two datasets.

In year 3, 59% of participants identified as female, 39% as male, and 2% as other (see Table 2). The mean age was 16.5 years (SD = 1.5). Regarding race/ethnicity, 58% of participants identified as Black non-Hispanic, 24.7% as White non-Hispanic, 6% as Hispanic, and 10% as other race/ethnicity. Regarding class standing, 39% of participants were classified as seniors, 29% as juniors, 10% as sophomores, and 21% as freshmen.

Materials

Emotional Responses

Participants completed a questionnaire asking about their experiences with physical, verbal, relational, and cyber aggressive rejection over the past 3 months. Participants were asked, “How often did someone from your school engage in physical/verbal/relational/cyber aggression toward you?” Participants responded to the question on a 6-point Likert-type scale, where 1 = never and 6 = all of the time. Participants were also given the option to decline a response. Students whose answers indicated they had experienced aggression from a classmate at least once were presented with questions assessing their emotional appraisal of the experience, such as whether it affected their self-esteem or resulted in any negative affect. In year 2, the constructs were combined into a single scale of 5 items which demonstrated good reliability (Y2: α = 0.93). In year 3, 6 items were included into the affect/self-esteem scale (Y3: α = 0.92). Note, for all variables, please see the Supplementary Table 1 for a list of items that were included or excluded across Years 2 and 3. Year 1 tests included pilot tests of newly created scales. In Year 2, as pilot testing showed some scales were still not strong enough, we added more items to strengthen the scales in Year 3. Ultimately, we added or removed items from scales to obtain the best measures possible for analysis. Thus, Year 3 scales were often shorter than Year 2 scales because, in order to reduce survey fatigue, only the strongest items from Year 2 were carried over to Year 3.

Construals

Participants then answered questions regarding their construal of the bullying they experienced.

Participants answered questions regarding their perceptions of the chronicity of their victimization for each type of victimization they experienced (e.g., “I feel like this type of aggression happens to me all of the time,” and “I feel like this aggression will continue no matter what I do”). In Year 2, three items were used to assess chronicity of victimization, and participants answered using a 7-point Likert-type scale, where 1 = disagree strongly, and 7 = agree strongly. Cronbach's alpha for reliability was 0.68. In year 3, the same three items were used to assess chronicity of victimization, using the same Likert scale. Cronbach's alpha for reliability was 0.83 in year 3.

Participants were asked questions about their perceived relationship value, assessing how much the rejection experience led them to value or devalue relationships in their life (e.g., “Because of this experience, I value the close relationships I have”). In Year 2, three items were used to assess relationship value, and participants answered using a 7-point Likert-type scale, where 1 = disagree strongly, and 7 = agree strongly. Cronbach's alpha for reliability was 0.80 for Year 2. In Year 3, the same three items were included, and participants answered using a 5-point Likert-type scale, where 0 = not at all, and 4 = definitely/very much. Cronbach's alpha for reliability was 0.86 in Year 3.

Participants were asked two to four items about perceived fairness of their victimization, assessing whether or not they perceived it to be unwarranted (e.g., “Do you think the actions this person/persons took toward you were mean?” and “Do you think the actions this person/persons took toward you were unfair?”). Participants responded using a 7-point Likert-type scale, where 0 = completely fair or completely reasonable, and 6 = completely unfair or completely unreasonable to a four-item scale in Year 2 and a two-item scale in Year 3. Cronbach's alpha for reliability was 0.86 in year 2, and 0.82 in year 3.

Participants were asked seven items about their perceived costs of the rejection in Year 2 and were asked 3 items in Year 3. These items assessed how participants perceived any negative effects that may have resulted from their victimization, including social costs (e.g., “How much did this experience have a negative impact on you?” and “How much did this experience cost you in a loss in reputation or status with friends/others?”). Participants responded to each item using a 5-point Likert-type scale, where 0 = not at all, and 4 = definitely. Cronbach's alpha for reliability in Year 2 was 0.91, and 0.87 in Year 3.

Participants were asked three items about their perceptions regarding relational repair in Years 2 and 3. These items assessed whether participants believed they may be able to repair the relationship with the person who victimized them, and have a positive relationship with them in the future (e.g., “To what extent do you have any interests in making the relationship you have with this person better?” and “To what extent do you feel you need to have a relationship with the person/persons who did this to you?”). Participants answered using a 5-point Likert-type scale, where 0 = not at all, and 4 = definitely. Cronbach's alpha for reliability was 0.91 in Year 2 and 0.92 in Year 3.

Participants were asked three items about their perceptions regarding alternative relationships in Years 2 and 3. These items assessed whether participants had other individuals they could turn to for social support (e.g., “To what extent do you have other people to whom you can turn to?” and “To what extent do you have other people who will support you?”). Participants responded to each item using a 5-point Likert-type scale, where 0 = not at all, and 4 = definitely. Cronbach's alpha for reliability was 0.95 in Year 2, and 0.95 in Year 3.

Participants were asked 2 items in Year 2 and 2 items in Year 3 about their perceptions of the extent to which groupness was involved in their reported victimization (e.g., “How typical is it for other members of your social group to be targeted by the same person(s) who harmed you?”). Participants responded to items on a 5-point Likert-type scale, where 0 = not at all and 4 = definitely. The scale showed acceptable reliability across the 2 years (Year 2 α = 0.84; Year 3 α = 0.81).

Behavioral Responses

Finally, participants were asked how they have responded to their reported physical, verbal, relational, and cyber aggression. In Years 2 and 3, participants answered four items to assess social withdrawal responses (e.g., “Trying to avoid situations where I have to be with other people”; α = 0.88 in years 2 and 3), three items to assess prosocial responses (e.g., “Trying to make new friends”; α = 0.84 in year 2; α = 0.83 in Year 3), and three items to assess antisocial responses in Year 2 (e.g., “Figuring out a way to get back at them”; α = 0.85 in year 2) and four items to assess antisocial responses in Year 3; α = 0.87 in Year 3). In Years 2 and 3, four items were used to assess self-harm responses (e.g., “Thinking about hurting myself”; α = 0.93 in year 2 and.92 in year 3).

Procedures

For Years 2 and 3, consent and assent forms were prepared for each student enrolled in the school, labeled with the student's name, and distributed to classrooms by the researchers in two rounds. In order to participate, students had to sign the assent form, have a parent sign the consent form, and return the forms to school. Students were instructed to return the signed forms to the main office at school, where the research team would collect them. For returning signed consent and assent forms, students were allowed to choose a small incentive: either a metal water bottle, a USB drive, or a pair of earbuds. The research team used the signed consent and assent forms to compile a list of students, organized by grade, who would be called out of class to complete the survey over a 3-day period.

The research team set up laptop computers in the school auditorium (Y2) or in the cafeteria (Y3) to collect data. At least two seats were skipped between each laptop to facilitate confidentiality. Small groups of students were called out of class to complete the survey throughout the day. Each student's name was verified against the prepared list of students, given instructions for completing the survey, and stationed at a laptop computer. Members of the research team circulated the room during data collection to assist students who had questions, or if any technological issues arose.

Once students completed the survey, they returned to the member of the research team who checked them into the survey. Students were given the opportunity to choose a $10 gift card from Amazon, Apple, or Wal-Mart as compensation for their participation. Students signed a voucher acknowledging they received their gift card and were given a hall pass to return to class.

Analytical Approach

The current manuscript tested the MMM separately in these two samples by comparing the MMM with a mostly saturated model (i.e., a model in which all paths between construals and outcomes were freely estimated). Because these two models are nested, a likelihood ratio test can compare the saturated and MMM. This is a direct test of the MMM with significant results indicating that the MMM does not fit the data. All residual covariances between construals were freely estimated as were all residual covariances between behavioral responses. In the MMM, all paths with a specified valence (i.e., positive or negative) were restricted to correspond to this valence. Given that groupness was not a component of the original MMM, associations including groupness were estimated without any constraint on the path.

Due to issues regarding psychometric fit of scales, two sets of analyses were run in each dataset with the sets of analyses differing by construct measurement with one derived using CFA and the other including all available items. However, the results were similar so only the results of the constructs made using CFA are reported (additional set of results available in Supplementary Table 2).

Initial analyses used CFA to ensure adequate measurement for each construct. For a construct to be considered a sufficient measure, all factor loadings must have been ≥0.7 (indicating ~50% of variance in the item was explained by the latent factor) as well as one of the following indicators of fit: RMSEA below 0.05; RMSEA below 0.08 with CFI and TLI >0.95; or a non-significant chi-square measure of fit. If the measurement model did not fit, items with a loading <0.6 were removed one at a time. If the model still failed to meet criteria, modification indices were used to determine whether residual covariances can improve fit. Residual covariances were added to the model one at a time until the above criteria were met or the modification index for adding a residual covariance was <4. If the measurement model still did not fit, the items with a loading >0.7 were retained. If only two items remained, the loadings of both were restricted to be equal to ensure constructs were locally identified.

Given the interest in self-harm reduction, an additional set of analyses were calculated in which self-harm behaviors were included in the saturated models as an additional behavioral outcome. More information can be found on the analysis plan and model parameters can be found on the Open Science Framework (https://osf.io/7wyf3/).

Results

Modified Analysis Measurement Models

The items included in each latent variable for each dataset are listed in Supplementary Table 2. The difference between measures was generally due to items that were close to the predetermined threshold and were over the threshold in one dataset but not others (e.g., cost of rejection). The latent variables were exactly or almost exactly identical across the two datasets indicating the latent measures capture the same core concept. Structural paths and covariances are depicted in Figure 2.

FIGURE 2
www.frontiersin.org

Figure 2. Structural paths and covariances between latent variables are shown in the model, but not measurement paths. Paths estimated in both the Multimotive Model and saturated model are solid. Blue lines indicate a path that was restricted to be positive in the Multimotive Model and orange lines indicate a path that was restricted to be negative. Dotted lines indicate a path was only estimated in the saturated model.

Modified Analyses

The analyses indicated that the mostly saturated model fit the data better than the MMM in Year 2 [χ2(7) = 41.3, p < 0.001] and [Year 3: χ2(7) = 51.3, p < 0.001]. The saturated model had had good fit in year 2 (RMSEA = 0.050, CFI = 0.94, TLI = 0.93, SRMR = 0.06) and Year 3 (RMSEA = 0.049, CFI = 0.94, TLI = 0.94, SRMR = 0.05). Of note, despite fitting more poorly than the saturated models, the MMM had adequate measures of fit in Year 2 (RMSEA = 0.051, CFI = 0.94, TLI = 0.93, SRMR = 0.07) and Year 3 (RMSEA = 0.052, CFI = 0.94, TLI = 0.93, SRMR = 0.07).

Negative affect/self-esteem was related to all construals (|B| > 0.25, p < 0.001) for all associations except for the association between alternative relationships regressed on negative affect/self-esteem in year 2 (B = 0.08, p = 0.14). The saturated model indicated several paths that were in the opposite direction than predicted by the MMM (see Table 3). Specifically, predicting asocial responses, alternative relationships and relationship value were in the opposite direction than predicted. Predicting antisocial responses, relationship repairability and relationship value (Year 2 only) were in the opposite direction than predicted. Groupness was not significantly associated with prosocial or asocial responses, but was the strongest predictor of antisocial responses (β's = 0.23 and 0.35).

TABLE 3
www.frontiersin.org

Table 3. Standardized structural path loadings from modified analyses, all paths estimated.

Associations With Self-harm

In the Year 2 model (RMSEA = 0.050, CFI = 0.94, TLI = 0.93, SRMR = 0.06), self-harm was associated with cost (β = 0.57, p < 0.001) and unfairness (β = 0.10, p = 0.048). In the Year 3 model (RMSEA = 0.049, CFI = 0.94, TLI = 0.93, SRMR = 0.05), self-harm was associated with cost (β = 0.56, p < 0.001), and chronicity (β = 0.20, p = 0.02).

Discussion

A better understanding of why youth respond to aggressive rejection can improve school responses to peer aggression, including bullying prevention programs (Frey et al., 2015). One way to begin to decrease aggressive responses connected to rejection is to understand which factors make youth more likely to respond aggressively compared to prosocial responding. Thus, this would allow for the development of interventions that discourage the former and encourage the latter. In the current study, we tested a novel theoretical model that hypothesized relations between certain perceptual factors and antisocial (retaliatory) behavior compared to prosocial (befriending others), and asocial (avoiding social events or people) responses to rejection. Although only a handful of the variables identified by the model proved useful in the predicted directions, we did find some significant relationships between factors included in the MMM and, specifically, for prosocial responding. Further, our amendment to the model wherein we included means to assess the perceived groupness of the rejection proved useful in predicting antisocial responses. Lastly, our addition of self-harm as a fourth type of behavioral response to aggression provides some groundwork for future studies examining this outcome.

Key Findings

The self-esteem and negative affect predictors were significantly associated with all construal's in the model, except for alternative relationship in Year 2. However, as noted, the MMM did not play out according to many of its predicted pathways for Year 2 or 3 data, and few hypothesized associations were significant. None of the hypothesized associations in the MMM were significant predictors of aggression.

Speculation about failure to reach significant levels should be made with caution. The absence of a finding doesn't mean there were not existing relationships, rather just that they were not found using the existing sample, method, and instruments. The work on the rejection-aggression link, however, typically only examines one outcome (e.g., antisocial behavior, prosocial behavior, or self-harm behavior) where participants are not given the full spectrum of behavioral responses available to them outside of a laboratory setting. As such, studies upon which the theory was based may be suffering from a sort of mono-operational bias, though not necessarily due to the use of a single measurement but rather due to the examination of a singular outcome (even if measured multiple ways, e.g., aggressive thoughts and aggressive behavior). If only given a hammer, participants see everything as a nail, so to speak. As such, the likelihood of aggressive responding might be inflated in past studies, but as participants were not given other options, we do not know if they would have chosen to reach out instead of lash out. A model that predicts pathways between rejection and different outcomes might be better grounded in research that allows for multiple behavioral responses - not just to use a hammer or not use a hammer–in their methods.

As we provide the first test of the full model, however, it remains to be seen if different measures, methods, or samples might yield different results. For example, prior studies on which the MMM was based also consisted primarily of participants who were primarily white, educated, industrialized, rich, and democratic. Meanwhile, our study applies MMM to explain youth responses to aggressive rejection in a low income, racially diverse, and rural, Southeastern high school context. As such, we recommend future tests of this model be applied to different populations of study (e.g., adults) that also address an array of rejection types (e.g., romantic rejection, workplace rejection, discrimination) and employs an experimental design.

Nevertheless, there were some significant associations for each of the four outcomes: prosocial, asocial, antisocial, and self-harm that can inform theory and practice for anti-bullying interventions. For example, the results from the saturated models suggested that reducing victims' perceptions of the costs of aggressive rejection may reduce self-harm and asocial behavior. Further, addressing the group dynamics—such as whether individuals are targeted because of their group identity—could further help reduce aggressive responses. We discuss these and other significant pathways and then we discuss theory and policy implications for those associations.

Relational repair (i.e., perceptions of the likelihood that one could restore a relationship with the rejecter) and valuing relationships were two consistent significant predictors of prosocial responding across Years 2 and 3. Alternative relationships (i.e., having other relationships, especially supportive relationships) was also a significant predictor of prosocial responding in Year 2. In the modified analyses, relational value also held up as a significant factor in Years 2 and 3. These findings point to the possibility of teaching youth the importance of relationships and could help motivate prosocial over antisocial responses when rejected. For example, anti-bullying programs based on Social-Emotional Learning (SEL) provide evidenced based approaches to helping youth build skills in self-awareness, self-management, social awareness, relationship skills, and responsible decision-making. Further, SEL based programs have success in reducing problem behaviors in school, such as bullying. These programs are effective because they give youth the skillsets that they need to better engage in conflict resolution and relationship repair when problems are presented (Li et al., 2011; Guo et al., 2015; Oberle et al., 2016; Stalker et al., 2018).

Costs of the rejection (e.g., perceiving a loss in status/friendship/reputation) was the only significant factor that upheld across Years 2–3 for asocial responding. The greater the costs, the more likely students were to retreat. Relationship value and perceived chronicity were also significant in one of the 2 years. This pattern persisted in the modified analyses with the exception of chronicity being significant across both samples. Thus chronic, costly rejection experiences appear to promote social withdrawal. To re-engage youth, measures could be put in place to ameliorate perceptions of the costs associated with the experience, and to implement interventions that reduce aggressive rejection in the schools particularly for youth who are frequent targets.

When it comes to antisocial responding, the only significant predictor was perceived groupness (i.e., perceiving the rejection as extending beyond just a rejection of the individual to also being a rejection of their friends or social identity) and cost in 1 year of the modified analyses. Perceived groupness was not originally included in the MMM but is one we felt was important to add based on a line of research finding this factor to be associated with aggression (Twenge and Campbell, 2003; Gaertner et al., 2008). The importance of this variable could be indicating the presence of co-victimization (Schaafsma and Williams, 2012; Sjöström and Gollwitzer, 2015), such that youth are accurately perceiving that those they care about are also being rejected and victimized. Alternatively, it could be that youth are perceiving that they belong to a group marginalized by school culture. Either way, intergroup conflict theories, such as social identity theory, could thus be useful to integrate into more research on aggressive rejection, including bullying to highlight potential paths for intervention. Considerable work has been conducted on how to improve intergroup relations in the presence of conflict which could inform interventions.

The most consistently significant factor linked to self-harm was costs. Self-harm was also associated with relational repair, unfairness, and groupness, although inconsistently across the 2 years of data. In the latent model, costs and unfairness were significant while in the Year 3 latent and manifest models, costs and chronicity became significant. It appears then that self-harm bears more similarity to asocial responding than antisocial responding at least in terms of the factors to which it is connected. Self-harm, including risky behavior and suicidal ideation, may be on the extreme end of a continuum of asocial responses where perceived cost of the rejection is the strongest link.

Prior Research and Novelty

This study offers the first test of the MMM (Richman and Leary, 2009) among a sample of students in a Southeastern high school. The MMM set out to explain when rejection leads to antisocial, asocial, and prosocial behavior. While many of the hypothesized paths in the MMM were not supported by the current study, we identified several other characteristics that may be incorporated into future interventions.

Additionally, we extended the model to also include a self-harm outcome, as many studies find a link between bullying and self-harm (Hay and Meldrum, 2010; Hinduja and Patchin, 2010). Our study also revealed the importance of perceived costs in terms of increasing the likelihood of self-harm. Thus, affecting either perceptions of costs or instrumentally reducing costs (e.g., compensating students for lost material costs where applicable) could help address both social withdrawal and prevent self-harm.

Further, our addition of perceived groupness proved to be a significant predictor to include in the model, particularly since it was the only variable significantly linked to aggression. It is noteworthy that it is one's group identity, as opposed to the rejecter's perceived groupness, that was associated with antisocial responding. Meaning, it was the extent to which individuals felt they were being targeted as part of a group rather than they were being targeted by a group that led to retaliation. Perhaps then youth are retaliating out of the perception that they are protecting their peer group rather than simply engaged in self-defense (Stubbs-Richardson and May, 2020). Defending others has more noble associations than personal revenge.

Shortcomings and Limitations

Some of the study limitations include that neither Years 2 nor 3 provide large samples. However, both studies included a full consent procedure where both parents and students had to consent and assent for student participation.

The generalizability of the sample is limited given that this study was conducted in one Southeastern high school. Still many studies on rejection and bullying do not include diverse samples. Our study included 51 to 58% of students who identified as African American across Years 2 and 3. African American samples are often overlooked (Peskin et al., 2006) in studies on bullying in high schools and in studies on the rejection-aggression link. Another limitation is that our data makes use of self-report survey methodology which required the development of all new scales to test the MMM. Year 1 allowed us to pilot and improve some of the measures included in the model prior to testing the data in Years 2 and 3, but some measures could likely be improved further. Nevertheless, we believe the replication of findings uncovered in Years 2 through 3 helps to reduce some of the limitations found in creating new scales and using self-report methodology, and it strengthens the findings overall. Finally, the reports of victimization in the current study are reflective of the actual experiences that students have providing increased external reliability; however, this also meant that reported experiences vary widely across the sample.

Theoretical Implications

We found the MMM not to be a good fit in terms of explaining antisocial and asocial responding; however, it does a better job explaining prosocial behavior. Although one factor that explained increased prosocial behavior—alternative relationships—was proposed to explain an increase in asocial behavior, not prosocial behavior. We believe future research should use the model to test a variety of types of rejection (e.g., romantic) across varying age samples to see if different results are met with the MMM. Further variations in the operationalization of different MMM variables could be employed.

In terms of using the MMM to explain responses to aggressive rejection, we also believe testing this model in other samples should be conducted to ensure our findings are not specific to a Southeastern rural high school context. However, based on some of our findings, perceived groupness should be included in future tests of the model to explain the likelihood of antisocial behavior. What proved important for the perceived groupness variable was how much youth felt like they—and notably their friends—were being targeted because of their group identity. Follow-up studies should continue to include the perceived groupness of the rejecter given experimental studies have shown this factor to matter (Gaertner et al., 2008). Further, the inclusion of assessment of both victim and perpetrator group identity variables would be consistent with classifying aggressive rejection in schools as an intergroup conflict.

Policy Implications

Overall, declines in school aggression and bullying over time may in part be due to successful bullying prevention programs in schools. From 2015 to 2016, 76% of schools offered training for school personnel on the types of bullying, including physical, relational, and verbal (Musu-Gillette et al., 2018). More can be done.

Our research can inform prevention programs in a number of ways. Specifically, our findings would suggest that there is a need to reduce the perception of perceived costs (loss in reputation or status), perception that one's peer group or friends are being attacked (perceived groupness), and improve school relationships by teaching students conflict resolution skills which have been shown to be an effective component of prior anti-bullying prevention programs (Frey et al., 2009; Low et al., 2010). We believe prevention programs that teach emotion regulation and conflict resolution skills which have been linked to reductions in bullying (Beets et al., 2009; Frey et al., 2009; Li et al., 2011) could also help students repair and value peer relationships more, which according to our study, would also increase prosocial behaviors. These two variables, relational repair and relational value, were significant predictors of prosocial responding. Thus, our research suggests that teaching students emotion regulation and conflict resolution skills could go a long way to helping students repair relationships, which should lead to increased prosocial behavior and a likely reduction in retaliatory behaviors in response to rejection as found in prior research (Frey et al., 2015).

Another key element to all anti-bullying programs is the role of social support. This is also evidenced by the importance of a number of significant relationship variables such as relational value and relational repair, and sometimes alternative relationships as associated with increased prosocial responding. Students need to know that they can count on others for support and that the larger school climate along with peers, teachers, administrators can offer this support to them (Grapin et al., 2016). When social support is successfully implemented, it has likewise been found to increase prosocial behavior and decrease school safety concerns (Grapin et al., 2016). Finally, our study also highlights the importance of decreasing the influence of group affects and dynamics in schools as connected to retaliation for bullying as prior research has found (Gaertner et al., 2008; Frey et al., 2015). Addressing group dynamics in bullying would likely lead to reduced antisocial behavior and retaliatory behavior in response to aggression (Frey et al., 2015). To reduce intergroup aggression, an integration of both effective methods that reduce aggressive behavior and improve intergroup relations is needed (Hage et al., 2017; Palmer and Abbott, 2018). Some examples exist (Levy and Killen, 2010) including: changing social norms (Aboud and Joong, 2010; Perkins et al., 2011), getting students to recognize common superordinate group identity to counter segregated self-categorization (Gaertner et al., 2010), increasing intergroup contact to reduce negative attitudes (Griffin et al., 2012; Tauriac et al., 2013), modeling prosocial bystander interventions (Aboud and Joong, 2010), training youth to recognize multiple categorizations to combat dualistic us vs. them categorization (Cameron and Rutland, 2010), and affirming diversity and positive aspects of group identities to prevent out-group derogation (Wittig, 2010). Each of these approaches primarily addresses one contributing factor, not multiple factors. Thus, integrating these factors could provide a strong intervention (Aronson, 2000; Wernick et al., 2017). This could create a more positive social environment where students could begin to care for one another regardless of associated groups and their group membership. Finally, we wish to comment on the importance of reducing costs. We believe this is again connected to challenging present social norms that allow bullying to be acceptable in the first place. Second, it may be particularly important to ameliorate the associated costs such as loss in reputation or status for individuals who may already be at risk for isolation and self-harm.

Conclusion

Our study makes a number of unique contributions (1) starting with being the first to test the full MMM, (2) plus conducting this study in two samples of diverse high school students, (3) who have experienced physical, verbal, relational, and/or cyber aggression, in addition to (4) examining the roles of groupness and (5) the outcome of self-harm. Our results suggest that there is high value to be placed on the importance of relationships and relationship skill-building when it comes to encouraging prosocial responding. Our study also highlights the importance of reducing the perception of costs associated with aggression, such as the loss in status, friendship, rank or “place” within a school. Anti-bullying prevention programs focused on social support could help to alleviate some of the perceived costs associated with aggressive rejection, including bullying. Reducing perceived costs could alleviate social pains youth experience, thereby reducing asocial and potentially self-injurious behavior. Finally, of importance to reducing antisocial behavior is reducing the likelihood that individuals perceive they are being targeted due to a social identity, have friends being co-victimized, or that others are targeting their peer groups. Prior research has found peers are likely to retaliate on the behalf of their friends (Frey et al., 2015), thus attending to and reducing group dynamics associated with aggression in schools could go a long way to reducing antisocial responses that ultimately contribute to cycles of aggression in schools.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Mississippi State University Institutional Review Board. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

Author Contributions

MS-R and HS contributed to the overall study design of the paper. BP contributed to the analyses of the paper. JU contributed to the methods of data collection and initial cleaning of the data associated with the paper. HS led on the literature review. MS-R led on the discussion section of the paper. All authors contributed to the overall editing and final version of the manuscript.

Funding

This work was supported by the National Institute of Justice under Grant No. 2015-CK-BX-0004.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.660973/full#supplementary-material

References

Aboud, F. E., and Joong, A. (2010). “Intergroup name-calling and conditions for creative assertive bystanders,” in Intergroup Attitudes and Relations in Childhood Through Adulthood, eds S. R. Levy and M. Killen (New York, NY: Oxford University Press), 249–260.

Google Scholar

Aronson, E. (2000). Nobody Left to Hate: Teaching Compassion After Columbine. New York, NY: Owl Books.

Google Scholar

Baumeister, R. F., and Leary, M. R. (1995). The need to belong: desire for interpersonal attachments as a fundamental human motivation. Psychol. Bull. 117:497. doi: 10.1037/0033-2909.117.3.497

PubMed Abstract | CrossRef Full Text

Beets, M. W., Flay, B. R., Vuchinich, S., Snyder, F. J., Acock, A., Li, K. K., et al. (2009). Use of a social and character development program to prevent substance use, violent behaviors, and sexual activity among elementary-school students in Hawaii. Am. J. Public Health 99, 1438–1445. doi: 10.2105/AJPH.2008.142919

PubMed Abstract | CrossRef Full Text | Google Scholar

Belmi, P., Barragan, R. C., Neale, M. A., and Cohen, G. L. (2015). Threats to social identity can trigger social deviance. Pers. Soc. Psychol. Bull. 41, 467–484. doi: 10.1177/0146167215569493

PubMed Abstract | CrossRef Full Text | Google Scholar

Blackhart, G. C., Baumeister, R. F., and Twenge, J. M. (2006). “Rejection's impact on self- defeating, prosocial, antisocial, and self-regulatory behaviors,” in Self and Relationships: Connecting Intrapersonal and Interpersonal Processes, eds K. D. Vohs, and E. J. Finkel (New York, NY: The Guilford Press), 237–253.

Google Scholar

Buffone, A. E., and Poulin, M. J. (2014). Empathy, target distress, and neurohormone genes interact to predict aggression for others–even without provocation. Pers. Soc. Psychol. Bull. 40, 1406–1422. doi: 10.1177/0146167214549320

PubMed Abstract | CrossRef Full Text | Google Scholar

Cameron, L., and Rutland, A. (2010). “An integrative approach to changing children's intergroup attitudes,” in Intergroup Attitudes and Relations in Childhood Through Adulthood, eds S. R. Levy and M. Killen (New York, NY: Oxford University Press), 191–203.

Google Scholar

Coyne, S. M., Nelson, D. A., Robinson, S. L., and Gunderson, N. C. (2011). Is viewing ostracism on television distressing? J. Soc. Psychol. 151, 213–217. doi: 10.1080/00224540903365570

CrossRef Full Text | Google Scholar

DeWall, C. N. (2010). Forming a basis for acceptance: excluded people form attitudes to agree with potential affiliates. Soc. Influence 5, 245–260. doi: 10.1080/15534511003783536

CrossRef Full Text | Google Scholar

DeWall, C. N., Deckman, T., Pond Jr, R. S., and Bonser, I. (2011). Belongingness as a core personality trait: how social exclusion influences social functioning and personality expression. J. Pers. 79, 1281–1314. doi: 10.1111/j.1467-6494.2010.00695.x

PubMed Abstract | CrossRef Full Text | Google Scholar

DeWall, C. N., and Richman, S. B. (2011). Social exclusion and the desire to reconnect. Soc. Pers. Psychol. Compass 5, 919–932. doi: 10.1111/j.1751-9004.2011.00383.x

CrossRef Full Text | Google Scholar

Dutton, D. G., White, K. R., and Fogarty, D. (2013). Paranoid thinking in mass shooters. Aggress. Viol. Behav. 18, 548–553. doi: 10.1016/j.avb.2013.07.012

CrossRef Full Text | Google Scholar

Eisenberger, N. I. (2011). Why rejection hurts: what social neuroscience has revealed about the brain's response to social rejection. Brain 3:1. doi: 10.1093/oxfordhb/9780195342161.013.0039

CrossRef Full Text | Google Scholar

Eisenberger, N. I., and Lieberman, M. D. (2004). Why rejection hurts: a common neural alarm system for physical and social pain. Trends Cogn. Sci. 8, 294–300. doi: 10.1016/j.tics.2004.05.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Esbensen, F. A., and Carson, D. C. (2009). Consequences of being bullied: results from a longitudinal assessment of bullying victimization in a multisite sample of American students. Youth Soc. 41, 209–233. doi: 10.1177/0044118X09351067

CrossRef Full Text | Google Scholar

Frey, K. S., Hirschstein, M. K., Edstrom, L. V., and Snell, J. L. (2009). Observed reductions in school bullying, nonbullying aggression, and destructive bystander behavior: a longitudinal evaluation. J. Educ. Psychol. 101:466. doi: 10.1037/a0013839

CrossRef Full Text | Google Scholar

Frey, K. S., Pearson, C. R., and Cohen, D. (2015). Revenge is seductive, if not sweet: why friends matter for prevention efforts. J. Appl. Dev. Psychol. 37, 25–35. doi: 10.1016/j.appdev.2014.08.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Frey, K. S., and Strong, Z. H. (2018). Aggression predicts changes in peer victimization that vary by form and function. J. Abnor. Child Psychol. 46, 305–318. doi: 10.1007/s10802-017-0306-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Gaertner, L., Iuzzini, J., and O'Mara, E. M. (2008). When rejection by one fosters aggression against many: multiple-victim aggression as a consequence of social rejection and perceived groupness. J. Exp. Soc. Psychol. 44, 958–970. doi: 10.1016/j.jesp.2008.02.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Gaertner, S. L., Dovidio, J. F., Guerra, R., Rebelo, M., Monteiro, M. B., Riek, B. M., et al. (2010). “The common in-group identity model: applications to children and adults,” in Intergroup Attitudes and Relations in Childhood Through Adulthood, eds S. R. Levy, and M. Killen (New York, NY: Oxford University Press), 204–219.

Google Scholar

Grapin, S. L., Sulkowski, M. L., and Lazarus, P. J. (2016). A multilevel framework for increasing social support in schools. Contemp. Sch. Psychol. 20, 93–106. doi: 10.1007/s40688-015-0051-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Griffin, S. R., Brown, M., and Warren, N. M. (2012). Critical education in high schools: the promise and challenges of intergroup dialogue. Equity Excell. Educ. 45, 159–180. doi: 10.1080/10665684.2012.641868

CrossRef Full Text | Google Scholar

Guo, S., Wu, Q., Smokowski, P. R., Bacallao, M., Evans, C. B., and Cotter, K. L. (2015). A longitudinal evaluation of the positive action program in a low-income, racially diverse, rural county: effects on self-esteem, school hassles, aggression, and internalizing symptoms. J. Youth Adolesc. 44, 2337–2358. doi: 10.1007/s10964-015-0358-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Gustafsson, P. E., Janlert, U., Theorell, T., Westerlund, H., and Hammarström, A. (2012). Do peer relations in adolescence influence health in adulthood? Peer problems in the school setting and the metabolic syndrome in middle-age. PLoS ONE 7:e39385. doi: 10.1371/journal.pone.0039385

PubMed Abstract | CrossRef Full Text | Google Scholar

Hage, S. M., Schwartz, J. P., and DeMartino, S. C. (2017). “International issues in the ethics of prevention,” in The Cambridge Handbook of International Prevention Science, eds M. Israelashvili, and J. L. Romano (Cambridge: Cambridge University Press), 63–80. doi: 10.1017/9781316104453.005

CrossRef Full Text

Hay, C., and Meldrum, R. (2010). Bullying victimization and adolescent self-harm: testing hypotheses from general strain theory. J. Youth Adolesc. 39, 446–459. doi: 10.1007/s10964-009-9502-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Hinduja, S., and Patchin, J. W. (2010). Bullying, cyberbullying, and suicide. Arch. Suic. Res. 14, 206–221. doi: 10.1080/13811118.2010.494133

CrossRef Full Text | Google Scholar

Hinduja, S., and Patchin, J. W. (2019). Connecting adolescent suicide to the severity of bullying and cyberbullying. J. Sch. Viol. 18, 333–346. doi: 10.1080/15388220.2018.1492417

CrossRef Full Text | Google Scholar

Hutchinson, M., Wilkes, L., Vickers, M., and Jackson, D. (2008). The development and validation of a bullying inventory for the nursing workplace. Nurse Res. 15, 19–29. doi: 10.7748/nr2008.01.15.2.19.c6326

PubMed Abstract | CrossRef Full Text | Google Scholar

Kass, S. (1999). Bullying widespread in middle school, say three studies. APA Monit. 30, 1–2. Available online at: http://www.apa.org/monitor/oct99/cf3.html

Knowles, M. L. (2014). Social rejection increases perspective taking. J. Exp. Soc. Psychol. 55, 126–132. doi: 10.1016/j.jesp.2014.06.008

CrossRef Full Text | Google Scholar

Kupersmidt, J. B., Burchinal, M., and Patterson, C. J. (1995). Developmental patterns of childhood peer relations as predictors of externalizing behavior problems. Dev. Psychopathol. 7, 825–843. doi: 10.1017/S0954579400006866

CrossRef Full Text | Google Scholar

Landa, A., Fallon, B. A., Wang, Z., Duan, Y., Liu, F., Wager, T. D., et al. (2020). When it hurts even more: the neural dynamics of pain and interpersonal emotions. J. Psychosom. Res. 128:109881. doi: 10.1016/j.jpsychores.2019.109881

PubMed Abstract | CrossRef Full Text | Google Scholar

Leary, M. R., Kowalski, R. M., Smith, L., and Phillips, S. (2003). Teasing, rejection, and violence: case studies of the school shootings. Aggress. Behav. 29, 202–214. doi: 10.1002/ab.10061

CrossRef Full Text | Google Scholar

Leary, M. R., Twenge, J. M., and Quinlivan, E. (2006). Interpersonal rejection as a determinant of anger and aggression. Pers. Soc. Psychol. Rev. 10, 111–132. doi: 10.1207/s15327957pspr1002_2

PubMed Abstract | CrossRef Full Text | Google Scholar

Levy, S. R., and Killen, M (Eds.). (2010). Intergroup Attitudes and Relations in Childhood Through Adulthood. New York, NY: Oxford University Press.

Google Scholar

Li, K.-K., Washburn, I. J., DuBois, D. L., Vuchinich, S., Ji, P., Brechling, V., et al. (2011). Effects of the Positive Action Program on problem behaviors in elementary school students: a matched-pair randomized control trial in Chicago. Psychol. Health 26, 187–204. doi: 10.1080/08870446.2011.531574

PubMed Abstract | CrossRef Full Text | Google Scholar

Low, S., Frey, K. S., and Brockman, C. J. (2010). Gossip on the playground: changes associated with universal intervention, retaliation beliefs, and supportive friends. Sch. Psychol. Rev. 39, 536–551. doi: 10.1080/02796015.2010.12087740

CrossRef Full Text | Google Scholar

May, D. C. (2014). School Safety in the United States: A Reasoned Look at the Rhetoric. Durham, NC: Carolina Academic Press.

McDougall, P., and Vaillancourt, T. (2015). Long-term adult outcomes of peer victimization in childhood and adolescence: pathways to adjustment and maladjustment. Am. Psychol. 70:300. doi: 10.1037/a0039174

PubMed Abstract | CrossRef Full Text | Google Scholar

Modin, B., Östberg, V., and Almquist, Y. (2011). Childhood peer status and adult susceptibility to anxiety and depression. A 30-year hospital follow-up. J. Abnorm. Child Psychol. 39, 187–199. doi: 10.1007/s10802-010-9462-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Musu-Gillette, L., Zhang, A., Wang, K., Zhang, J., Kemp, J., Diliberti, M., et al. (2018). Indicators of School Crime and Safety: 2017. Washington, DC.

Google Scholar

Oberle, E., Domitrovich, E. E., Meyers, D. C., and Weissberg, R. P. (2016). Establishing systemic social and emotional learning approaches in schools: a framework for schoolwide implementation. Cambrid. J. Educ. 46, 277–297. doi: 10.1080/0305764X.2015.1125450

CrossRef Full Text | Google Scholar

Palmer, S. B., and Abbott, N. (2018). Bystander responses to bias-based bullying in schools: a developmental intergroup approach. Child Dev. Perspect. 12, 39–44. doi: 10.1111/cdep.12253

CrossRef Full Text | Google Scholar

Perkins, H. W., Craig, D. W., and Perkins, J. M. (2011). Using social norms to reduce bullying: a research intervention among adolescents in five middle schools. Group Process. Intergroup Relat. 14, 703–722. doi: 10.1177/1368430210398004

CrossRef Full Text | Google Scholar

Peskin, M. F., Tortolero, S. R., and Markham, C. M. (2006). Bullying and victimization among black and hispanic adolescents. Adolescence 41, 467–484. doi: 10.1016/j.jadohealth.2006.10.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Peters, E., Riksen-Walraven, J. M., Cillessen, A. H., and de Weerth, C. (2011). Peer rejection and HPA activity in middle childhood: friendship makes a difference. Child Dev. 82, 1906–1920. doi: 10.1111/j.1467-8624.2011.01647.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Prinstein, M. J., and La Greca, A. M. (2004). Childhood peer rejection and aggression as predictors of adolescent girls' externalizing and health risk behaviors: a 6-year longitudinal study. J. Consult. Clin. Psychol. 72, 103–112. doi: 10.1037/0022-006X.72.1.103

PubMed Abstract | CrossRef Full Text | Google Scholar

Reijntjes, A., Kamphuis, J. H., Thomaes, S., Bushman, B. J., and Telch, M. J. (2013). Too calloused to care: an experimental examination of factors influencing youths' displaced aggression against their peers. J. Exp. Psychol. Gen. 142, 28–33. doi: 10.1037/a0028619

PubMed Abstract | CrossRef Full Text | Google Scholar

Reijntjes, A., Thomaes, S., Bushman, B. J., Boelen, P. A., de Castro, B. O., and Telch, M. J. (2010). The outcast-lash-out effect in youth: alienation increases aggression following peer rejection. Psychol. Sci. 21, 1394–1398. doi: 10.1177/0956797610381509

PubMed Abstract | CrossRef Full Text | Google Scholar

Reijntjes, A., Thomaes, S., Kamphuis, J. H., Bushman, B. J., De Castro, B. O., and Telch, M. J. (2011). Explaining the paradoxical rejection-aggression link: the mediating effects of hostile intent attributions, anger, and decreases in state self-esteem on peer rejection-induced aggression in youth. Pers. Soc. Psychol. Bull. 37, 955–963. doi: 10.1177/0146167211410247

PubMed Abstract | CrossRef Full Text | Google Scholar

Richman, L. S., and Leary, M. R. (2009). Reactions to discrimination, stigmatization, ostracism, and other forms of interpersonal rejection: a multimotive model. Psychol. Rev. 116, 365–383. doi: 10.1037/a0015250

PubMed Abstract | CrossRef Full Text | Google Scholar

Schaafsma, J., and Williams, K. D. (2012). Exclusion, intergroup hostility, and religious fundamentalism. J. Exp. Soc. Psychol. 48, 829–837. doi: 10.1016/j.jesp.2012.02.015

CrossRef Full Text | Google Scholar

Schoch, S., Nikitin, J., and Freund, A. M. (2015). Why do (n't) you like me? The role of social approach and avoidance motives in attributions following social acceptance and rejection. Motivat. Emot. 39, 680–692. doi: 10.1007/s11031-015-9482-1

CrossRef Full Text | Google Scholar

Sinclair, H. C., Ladny, R. T., and Lyndon, A. E. (2011). Adding insult to injury: effects of interpersonal rejection types, rejection sensitivity, and self-regulation on obsessive relational intrusion. Aggress. Behav. 37, 503–520. doi: 10.1002/ab.20412

PubMed Abstract | CrossRef Full Text | Google Scholar

Sjöström, A., and Gollwitzer, M. (2015). Displaced revenge: can revenge taste “sweet” if it aims at a different target? J. Exp. Soc. Psychol. 56, 191–202. doi: 10.1016/j.jesp.2014.09.016

CrossRef Full Text | Google Scholar

Sommer, K. L., and Bernieri, F. (2015). Minimizing the pain and probability of rejection: evidence for relational distancing and proximity seeking within face-to-face interactions. Soc. Psychol. Pers. Sci. 6, 131–139. doi: 10.1177/1948550614549384

CrossRef Full Text | Google Scholar

Stalker, K. C., Wu, Q., Evans, C. B., and Smokowski, P. R. (2018). The impact of the positive action program on substance use, aggression, and psychological functioning: is school climate a mechanism of change? Childr. Youth Services Rev. 84, 143–151. doi: 10.1016/j.childyouth.2017.11.020

CrossRef Full Text | Google Scholar

Stubbs-Richardson, M., and May, D. C. (2020). Social contagion in bullying: an examination of strains and types of bullying victimization in peer networks. Am. J. Crim. Just. 20:9572. doi: 10.1007/s12103-020-09572-y

CrossRef Full Text | Google Scholar

Tauriac, J. J., Kim, G. S., Sariñana, S. L., Tawa, J., and Kahn, V. D. (2013). Utilitizing affinity groups to enhance intergroup dialogue workshops for racially and ethnically diverse students. J. Special. Group Work 38, 241–260. doi: 10.1080/01933922.2013.800176

CrossRef Full Text | Google Scholar

Twenge, J. M., and Campbell, W. K. (2003). “Isn't it fun to get the respect that we're going to deserve?” Narcissism, social rejection, and aggression. Pers. Soc. Psychol. Bull. 29, 261–272. doi: 10.1177/0146167202239051

PubMed Abstract | CrossRef Full Text | Google Scholar

Utley, J., Sinclair, H. C., Nelson, S., Ellithorpe, C., and Stubbs-Richardson, M. (2021). Behavioral and psychological consequences of social identity-based aggressive victimization in high school youth. Self Identity 1–25. doi: 10.1080/15298868.2021.1920049

CrossRef Full Text | Google Scholar

van Beest, I., Carter-Sowell, A. R., van Dijk, E., and Williams, K. D. (2012). Groups being ostracized by groups: Is the pain shared, is recovery quicker, and are groups more likely to be aggressive? Group Dynam. Theory Res. Pract. 16, 241–254. doi: 10.1037/a0030104

CrossRef Full Text | Google Scholar

Vossekuil, B. (2004). The Final Report and Findings of the Safe School Initiative: Implications for the Prevention of School Attacks in the United States. Washington, DC: Diane Publishing.

Google Scholar

Wang, J., Iannotti, R. J., and Nansel, T. R. (2009). School bullying among adolescents in the United States: Physical, verbal, relational, and cyber. J. Adolesc. Health 45, 368–375. doi: 10.1016/j.jadohealth.2009.03.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Wernick, L. J., Kulick, A., Dessel, A. B., and Graham, L. F. (2017). Theater and dialogue to increase youth's intentions to advocate for LGBTQQ people. Res. Soc. Work Pract. 26, 189–202. doi: 10.1177/1049731514539417

CrossRef Full Text | Google Scholar

Wesselmann, E. D., Butler, F. A., Williams, K. D., and Pickett, C. L. (2010). Adding injury to insult: Unexpected rejection leads to more aggressive responses. Aggress. Behav. 36, 232–237. doi: 10.1002/ab.20347

PubMed Abstract | CrossRef Full Text | Google Scholar

Wittig, M. A. (2010). “A mutual acculturation model for understanding and undermining prejudice among adolescents,” in Intergroup Attitudes and Relations in Childhood Through Adulthood, eds S. R. Levy, and M. Killen (New York, NY: Oxford University Press), 220–235.

Google Scholar

Zimmer-Gembeck, M. J., Pronk, R. E., Goodwin, B., Mastro, S., and Crick, N. R. (2013). Connected and isolated victims of relational aggression: associations with peer group status and differences between girls and boys. Sex Roles 68, 363–377. doi: 10.1007/s11199-012-0239-y

CrossRef Full Text | Google Scholar

Keywords: bullying, rejection, aggression, prosocial behavior, antisocial behavior, asocial behavior, self-harm, perceived groupness

Citation: Stubbs-Richardson M, Sinclair HC, Porter B and Utley JW (2021) When Does Rejection Trigger Aggression? A Test of the Multimotive Model. Front. Psychol. 12:660973. doi: 10.3389/fpsyg.2021.660973

Received: 30 January 2021; Accepted: 31 May 2021;
Published: 25 June 2021.

Edited by:

Mark Hallahan, College of the Holy Cross, United States

Reviewed by:

Haijiang Li, Shanghai Normal University, China
Yangu Pan, Southwestern University of Finance and Economics, China

Copyright © 2021 Stubbs-Richardson, Sinclair, Porter and Utley. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Megan Stubbs-Richardson, megan@ssrc.msstate.edu; H. Colleen Sinclair, csinclair@psychology.msstate.edu