Who’s Watching,Who’s Waiting? Predictors of Bystander Intervention in Adolescent Bullying

Abstract

Bullying poses ongoing challenges to student safety and well-being in schools, yet many instruments, such as the Centers for Disease Control and Prevention’s Youth Risk Behavior Survey, do not address bystander behavior. This study examined how 3,717 middle and high school students in 14 schools in the western United States respond when witnessing bullying and what factors influence their responses. Findings suggest that friendship with the target significantly increased both defending behaviors and the likelihood that bullying stopped or decreased. Gender influenced responses, with female students more likely to report and male students more likely to fight back. Racial alignment between bystander and target predicted higher reporting rates and intervention success, raising concerns about in-group favoritism. Physical bullying prompted more intervention than verbal and relational bullying, though verbal bullying was more prevalent. Findings underscore the need for culturally responsive, relationship-focused interventions to strengthen bystander engagement across contexts.

Keywords

adolescents bullying bystander race ethnicity

Introduction

Bullying remains a widespread concern in schools worldwide and has drawn the attention of educators, researchers, and policymakers (Gaffney et al., 2021). In the United States, approximately 22% of students ages 12 to 18 report being bullied each year (National Center for Education Statistics [NCES], 2021), reporting they experience physical, verbal, relational, or cyber bullying on school grounds (Basile et al., 2020; Waasdorp & Bradshaw, 2015). Nearly half of these students face multiple types of bullying, most commonly verbal and relational bullying in the form of name-calling, rumor-spreading, or exclusion (NCES, 2021). These experiences contribute to serious physical, psychological, and social consequences that include absenteeism (Baams et al., 2017; Hutzell & Payne, 2012), depression, anxiety, and suicidal ideation (Cao et al., 2021; Eyuboglu et al., 2021; Menken et al., 2022), as well as lower self-esteem (Atay et al., 2022). Students who engage in bullying also face risks such as substance abuse and an increased chance of being involved in criminal activity (Copeland et al., 2013; Farrington & Ttofi, 2011). As bullying continues to threaten school safety (National Center for Education Statistics [NCES], 2023), research has turned to the critical role of bystanders, whose reactions to this behavior can either reinforce or disrupt bullying in schools.

Thomas et al. (2016) defined the bystander effect as “The more potential helpers there are, the less likely any individual is to help” (p. 621). That is, the bystander effect applies to responding to non-emergencies (e.g., an email from the boss cc’ing everyone; Thomas et al., 2016) to life-threatening emergencies (e.g., a physical attack on someone). The presence of a greater number of people reduces the likelihood that someone will help, including individuals who have previously proven to be helpful (Plötner et al., 2015). Additionally, there are hypotheses about why the bystander effect exists, including social referencing, diffusion of responsibility, and even shyness (Plötner et al., 2015). These hypotheses have been tested, primarily in adult populations, and interventions have been developed to potentially mitigate the bystander effect (Greitemeyer & Mügge, 2015; Kistler et al., 2022; Plötner et al., 2015).

In 1970, Darley and Latané created a five-step model of intervention for emergencies. These steps include noticing the event (Step 1), recognizing or interpreting it as an emergency (Step 2), taking responsibility for helping (Step 3), and knowing how to provide help (Step 4) before providing help (Step 5). Other groups developed bystander intervention programs (e.g., “Friends don’t let friends drive drunk”), finding that these programs lead to greater awareness and possibility for intervention (Dvoskin et al., 2023). These bystander intervention programs focus on adult populations, and one may argue that adults may exhibit a bystander effect because they are socially conditioned to diffuse responsibility for several reasons (e.g., lack of self-efficacy in a task). How, then, does the bystander effect impact younger populations?

While young children may not feel like adults in their reluctance to assist someone in need, research suggests that children also diffuse responsibility when more people are present in a situation requiring assistance (Plötner et al., 2015). Previous research has identified the bystander effect in children once they reach the age of 9 (Staub, 1970), but more recent studies found the bystander effect present in children at age 5 (Plötner et al., 2015). Children were more likely to help when they felt it was their responsibility due to the lack of bystanders present. When asked why they did not help, participants stated they “had not known how to help,” even though they had been shown how to help. The researchers posited that the child’s reasons for not helping may have been to “save face” or “make themselves feel better about not having helped” (Plötner et al., 2015, p. 505). Subsequently, the researchers suggested creating interventions to address the bystander effect, which involves the diffusion of responsibility.

One area in which children may find themselves in a bystander situation is bullying. The Centers for Disease Control and Prevention (CDC, 2024) defines bullying as “one or more students tease, threaten, spread rumors about, hit, shove, or hurt another student over and over again” (p. 81). The Youth Risk Behavior Survey (YRBS) reported in 2023 that LGBTQ+ children were more likely to be threatened or injured with a weapon at school, not attend school because of safety concerns, and get bullied both electronically and at school than heterosexual and cisgender peers (CDC, 2024). The YRBS also found that females and children from different races are more likely to be bullied electronically and at school. Understanding the variables associated with bullying and bystander behavior may increase the ability to intervene effectively; thus, further research is needed.

Gender and age are linked to bystander behavior. Lamb et al. (2024) found that females and elementary-aged students were more likely to engage in effective bystander strategies, with males and high school students least likely to engage. Waasdorp et al. (2022) also found that high school students were least likely to engage in active bystander behavior, and females were more likely to engage in active bystander behavior, with males more likely to join in on the bullying. Peck et al. (2023) suggested that bystander training may be effective in reducing gender differences in bystander behavior, as lower self-esteem (for females) and fear of negative evaluation (for males) reduced bystander behavior in elementary-aged students.

Relationships with the victims also impact bystander behavior. Researchers examined bystander intervention for LGBTQI+ victims and found that friendship with the victim positively related to active bystander behaviors (Dessel et al., 2017; Fowler & Buckley, 2022). Other researchers found that students who felt they had a “network of peers” were more likely to engage in bystander behavior (Jenkins & Fredrick, 2017, p. 766).

Race also influences bystander behavior in bullying situations. Jones et al. (2023) found that students with racist attitudes are more likely to engage in or support racist bullying. Hong et al. (2024) reported that locker checks reduced victimization for White females, while the presence of metal detectors had the same effect for Black females. However, wearing photo IDs increased victimization among Black males, possibly due to racial profiling (Hong et al., 2024). Waasdorp et al. (2022) found that strong parent–-teacher connections increased active bystander behavior among Black and Latinx youth, who overall reported such behavior more than White youth. Dessel et al. (2017) similarly found that Chicano(a)/Latino(a)/Hispanic college students were more likely than White students to intervene when alone. Kan et al. (2022) emphasized the need for culturally sensitive, empathetic support for students targeted due to race or ethnicity. Despite these findings, the link between race and bullying outcomes remains underexplored.

The psychological profile of students who witness bullying identified factors influencing witnesses, categorizing them into impacts on the outcome of bullying. While Mauduy et al. (2021) recognized the hesitancy to defend victims of bullying, they identified five psychological types of witnesses, including: pro-defense, anti-defense, pro-bullying, conflicting beliefs, and inconsistent witnesses. Kubiszewski et al. (2018) also recognized factors influencing witnesses, placing them in three categories: alert, care, and opposition. Doumas and Midgett (2023) stressed the increased mental health risks associated with students witnessing cyberbullying and suggested that witnessing this form of bullying may be linked to higher mental health risks than witnessing bullying in the school environment.

With research on bullying increasing, Brody and Vangelisti (2016) suggested more research attention on “the presence of peers or bystanders observing the bullying episode” (p. 95). In other words, more needs to be understood about how children, as bystanders, respond when they witness bullying. Thus, the following research questions guided this exploratory study: (1) How do bystanders respond when witnessing bullying in the school setting? and (2) What factors influence student responses to bullying and the perceived outcomes?

Methods

Participants

Schools utilized a non-profit survey platform that allowed them to access the survey provided by the survey platform. As part of the survey’s platform terms and agreements, school designees who created the accounts agreed that de-identified data would be used for research purposes. Researchers obtained Institutional Review Board approval to access this data. After obtaining IRB approval, the survey platform provided de-identified data.

Participants included students from 14 schools in the western United States. Six of the schools were high schools, and nine were middle schools. A total of 3,717 students participated in this study by completing the survey. The survey platform suggested that schools survey at least 20% of the student population. Students included in the survey were determined by the schools. Due to the de-identified nature of the data, exact response rates for individual schools could not be determined. Most of the students identified as Hispanic or Latino (48.5%), while others identified as Non-Hispanic White (10.8%), Asian or Asian American (9.0%), Black or African American (8.7%), Multiple Ethnicity (7.4%), American Indian or Alaska Native (3.7%), Hawaiian or Other Pacific Islander (2.4%), Middle Eastern (0.4%), and “Other” (9.1%). Most of the participants identified as female (53.8%), while the remaining consisted of males (46.1%) and non-binary (0.1%). A total of 56.8% of students were from the 6th to 8th grade level; the remaining 43.2% were in grades 9th to 12th.

Measures

The student perceptions of bullying survey (SPBS) is a 27-item skip-logic survey designed to assess the social dynamics of bullying from multiple perspectives. The survey employs an adaptive approach, presenting questions to students based on their responses to previous questions. The first question asks if students have experienced bullying in their school. Those who respond “yes” are asked about their experience from a first-hand perspective. If students select “not sure,” the survey is presented reversely, asking about verbal, physical, or cyber events first, and how these could or could not lead to a bullying experience. If a “not sure” respondent answers that they have not experienced any of these, the survey ends. If students respond that they have not been bullied, they are asked about bullying from the perspective of a bystander. For this study, the responses of the bystanders will be analyzed. The SPBS measures 15 different characteristics of bullying, including prevalence, timeline, frequency, social dynamics, location, type, perceived reason, the students’ response, and the outcome. The survey was developed through an extensive literature review and evaluated by a panel of experts in the field, comprising academic experts, school administrators, school counselors, and teachers, to establish content validity. Face validity was achieved by piloting the survey with about 30 high school students to clarify the survey items. Minor revisions to the wording were suggested to reduce ambiguity and ensure consistency, such as, indicating that the target audience had a clear understanding of the items.

Data Analysis

First, survey data were analyzed using descriptive methods to identify patterns in bullying experiences, perceptions, and responses. The analysis focused on categorical variables, including demographic characteristics, frequency of bullying, types of bullying experienced, social dynamics of bullying, perceived reasons for being bullied, and the effectiveness of different response strategies. Percentages and frequency distributions were calculated for each response category.

Second, a series of logistic regression analyses were conducted employing the SPSS statistical software version 29 to answer all research questions. For the parameters of each model, every other predictor in the series of regressions was added as the covariate in Step 1, such that the higher-order non-parametric correlations indicated unique shared variability while holding the others statistically constant. In the first regression model, the person who witnessed the bullying incident was the reporter, and the categorical predictor variable was their relationship to the victim, with the outcome of the reporting serving as the outcome. In the second model, the type of bullying behavior served as the predictor variable, and the response of the individual who witnessed the incident served as the outcome. In the third regression model, the type of bullying behavior served as the predictor variable, and the outcome served as the criterion. The reported race of the witness to the bullying and the reported race of the actual victim served as predictors, and the outcome of the reporting served as the outcome in the fourth regression model. The reporter’s race, gender, and grade served as predictors, and the response of the witness to the incident was the criterion in the fifth regression model. Finally, the frequency of reporting served as the predictor, and its impact on reporting behavior served as the outcome of the sixth regression model.

Data were screened for requisite statistical assumptions such as linearity, multicollinearity, and outliers beyond three standard deviations of the standardized residuals. Data met the necessary assumptions previously stated, and no extreme outliers were detected in the data. The covariate-adjusted odds-ratio (CAOR) served as the effect size estimate for all regression models. The leave-one-out reclassification analysis is a statistical technique that reveals the accuracy of the regression models to correctly reclassify participants in the correct category.

Results

Research Question 1 was addressed through a descriptive analysis of the data. A total of 3,717 students completed the SPBS survey. The majority of the students (63.6%) reported not being bullied. For this study, data analysis will focus on students we considered bystanders because they responded “No” to the question “I have been bullied at school,” but responded “Yes” to the question “Somebody I know has been bullied.” Approximately 19.3% (716 students) of those who took the survey identified as bystanders and were included in the analysis. Of those students, nearly half (45%) identified the target as a friend, 29.2% said the student was a classmate, and 25.8% reported not knowing the target personally, suggesting that bullying is often observed within the close peer network. These students also reported the frequency at which they observed bullying behaviors. Almost half of the students (45.8%) who reported observing a peer being victimized reported that it happened more than once, possibly indicating that students experience repeated forms of peer victimization within their peer networks. Almost a third (31.2%) reported being unsure how many times the bullying behavior occurred, which could suggest that some forms of bullying within friend networks are more covert or a reflection of bystanders not having a close relationship with the target. Most students reported that bullying was part of a group dynamic (39.0%), while 29.3% indicated that a single participant perpetrated the bullying. Notably, almost a third (31.7%) again reported being unsure of the social dynamic, indicating a lack of visibility or knowledge of the behavior or a reluctance to identify the aggressors. Within the cases of single perpetrators, boys made up the majority of aggressors, while within the group context, groups of boys and girls engaged in bullying behavior. Overall, bystanders appear to be aware of bullying within their peer networks (see Table 1).

Table 1.

Relationship to the Target, Frequency, and Social Structure of Bullying.

Variable	n	%
I have been bullied at school
Yes	921	24.8
I’m not sure	432	11.6
No	2363	63.6
Somebody I know has been bullied
Yes	716	19.3
No	1,647	44.3
How do you know the student?^a
He/she is a friend	322	45.0
He/she is in my class	209	29.2
I really don’t know the student	185	25.8
How often has the student been bullied?
1 time	165	23.0
2 to 3 times	199	27.8
4 to 5 times	45	6.3
6 or more	84	11.7
I don’t know	223	31.2
The student was bullied by . . .
A single student	210	29.3
A group of students	279	39.0
I don’t know	227	31.7
Bully context reported as single student
Boy	110	15.4
Girl	76	10.6
I don’t know	22	3.1
Missing	2	0.28
Bullying context reported as group
Group of boys	92	12.8
Group of girls	61	8.5
Both boys and girls	110	15.4
I don’t know	16	2.2

Percentages after this question were calculated based on the total number of students (716) who responded that they knew someone who was bullied.

Table 2 summarizes the nature of the bullying observed, the perceived motivations of the bullying, and the actions taken by the bystanders. The most common type of bullying reported was verbal bullying (77.7%), followed by physical bullying (32%), and social bullying (15.9%). When asked about the perceived reason for the bullying, students primarily reported that the target’s appearance or looks (42.7%), followed by the target’s friendships (16.5%), sexual orientation (9.9%), and race (9.6%). These results indicate a variety of social identity-based motivations for bullying. Additionally, the high percentage of “other” responses suggests that the motivation for bullying may not be easily assessed through predetermined survey categories. In terms of bystander responses to bullying, 26.3% of students reported telling the aggressor to stop, and 19.1% ignored the behavior altogether. A smaller percentage asked adults for help; 5.6% told a teacher, 4.5% reported it to the school, and only 1.1% reported it to a school resource officer. A total of 21.8% chose “other,” and another 4.7% chose not to answer. These responses indicate that while students witness bullying in their social circles, many choose informal methods of addressing it. Finally, student responses to the outcomes of their responses were positive, with 60% indicating that the bullying had stopped or decreased. However, nearly one-third indicated nothing had changed, and 3.9% said it got worse. While the overall results indicate positive responses, a significant number of students may feel their responses to a peer being bullied are ineffective.

Table 2.

Bystander Reported Bullying Type, Reason, Response, and Outcome.

Variable	n	%
Reported Type of Bullying Observed^a
Verbal	556	77.7
Physical	228	32.0
Social	114	15.9
Reported Reason for Target Being Bullied^a
Who they date	76	10.6
Their sexual orientation	71	9.9
Their friendships	118	16.5
Their looks	306	42.7
Their faith	18	2.5
Their culture	41	5.7
Their race	69	9.6
They do well in school	58	8.1
They do not do well in school	44	6.1
What they have (Socioeconomic Status [SES])	29	4.1
What they don’t have	69	9.6
Other	291	40.6
Bystander Response When Witnessing Bullying
Ignored them	137	19.1
Told the person to stop	188	26.3
Fought back	39	5.4
Told a friend or peer	58	8.1
Told parents	24	3.4
Told a teacher	40	5.6
Reported it to the school	32	4.5
Reported it to the school resource officer	8	1.1
Other	156	21.8
Did not answer	34	4.7
Bystander Response to Reported Outcome
The bullying stopped	236	33.0
The bullying happened less often	193	27.0
Nothing changed	225	31.4
The bullying got worse	28	3.9
Did not answer	34	4.7

Respondent could choose more than one response.

Research Question 2 was addressed through a more focused analysis to examine how specific variables may influence a bystander’s reaction to bullying. This analysis examined (1) overall predictors for bystanders and bullying, (2) the relationship between the bystander and the victim, and (3) demographic characteristics that may influence bystander response. A series of logistic regression models evaluated variables for their predictive power in the likelihood of bystander response and perceived outcomes. This analysis showed a clearer understanding of when and why students choose to act when witnessing peers targeted for bullying.

The analysis first explored overall predictors for bullying and bystander behavior. Specifically, the study assessed the effects of witnessing bullying, the type of bullying observed, and the frequency of exposure on bullying outcomes. Individuals who witnessed a bullying incident were 2.81 times more likely to report that the bullying stopped completely compared to all other outcomes. Regarding the type of bullying observed, results showed a significant relationship between the type of bullying and the bystander’s response. Bystanders who witnessed physical bullying were 5.12 times more likely to report it to the school, and 2.66 times more likely to fight back compared to any other type of bullying behavior. In contrast, the frequency of witnessing bullying did not significantly predict bystander intervention. Specifically, findings indicated nil predictive effect of frequency of bullying behavior on the reporting of bullying, p = .18.

Next, the relationship between the bystander and the target of bullying was examined in relation to predicted responses and outcomes. Results indicated a significant association between the bystander’s relationship with the victim and their response. Bystanders who identified the target as a friend were 3.57 times more likely to report the incident to the school and 4.84 times more likely to fight back than if the victim was someone they did not know. The analysis then examined the relationship between the bystander, the target, and the outcome. A significant association emerged between the nature of the relationship and the effectiveness of the response in reducing bullying. When the victim was a friend, the bullying was 1.93 times more likely to stop completely and 2.88 times more likely to decrease.

Finally, demographic characteristics were analyzed to assess their influence on bystander response to bullying. Key variables included race and gender. The analysis identified significant associations between the racial identities of the bystander and the target in predicting whether the bullying incident was reported. When both the bystander and target were White, the likelihood of the incident being reported to the school was 3.52 times higher compared to all other racial pairings. Racial alignment between the bystander and the target also significantly influenced the effectiveness of the reporting. When both the bystander and target were White, the bullying behavior was 7.01 times more likely to stop completely compared to other outcomes.

In addition, the influence of bystander gender, race, and grade level on response type was examined. However, gender was the only significant predictor of response behavior, while all other predictors were non-significant; all p-values ≥ .293. Female bystanders were 2.60 times more likely to report the incident to the school, and males were 2.46 times more likely to fight back compared to all other responses. Table 3 displays the omnibus results for each model, the results of the leave-one-out reclassification results, and the CAORs along with their 96% confidence intervals.

Table 3.

Statistical Results and 95% Confidence Interval of Effect Sizes for the Logistic Regressions.

Predictor	χ² (df)	p	[LoOC%] CAOR (CI_95%)
BWB	66.19 (4)	<.001	[74.75]
Bullying stopped			2.81 (2.19, 3.61)
BWPB	84.57 (4)	<.001	[75.66]
Report to school			5.12 (3.61, 7.25)
Fight back			2.66 (2.16, 3.27)
ITF	92.26 (6)	<.001	[83.5]
Report to school			3.57 (2.75, 4.63)
Fight back			4.84 (3.51, 6.68)
VF	109.12 (6)	<.001	[79.93]
Bullying stopped			1.93 (1.71, 2.18)
Bullying decreased			2.88 (2.36, 3.50)
BTW	81.94 (9)	<.001	[79.06]
Report to school			3.52 (2.68, 4.62)
BTW	126.33 (9)	<.001	[80.02]
Bullying stopped			7.01 (4.99, 9.82)
Gender	65.17 (19)	<.001	[86.22]
Female, report to school			2.60 (2.06, 3.28)
Male, fight back			2.46 (1.98, 3.06)

Note. N = 3,717. df = degrees of freedom; [LoOC%] = leave-one-out reclassification percentile results; CAOR + (CI_95%) = covariate-adjusted odds ratio and its 95% confidence interval; BWB = bystanders who witnessed a bullying incident; BWPB = bystanders who witnessed physical bullying; ITF = bystanders who identified target as a friend; VF = when target of bullying was identified as a friend; BTW = when bystander and target identified as White.

Discussion

Decades of research have shown that the presence and actions of bystanders can influence the outcomes of bullying incidents (Brody & Vangelisti, 2016; Darley & Latane, 1970). The bystander effect has been observed across contexts and age groups (Plötner et al., 2015; Staub, 1970) and is shaped by factors such as diffusion of responsibility, perceived competence, and potential social cost (Thomas et al., 2016). In schools, bystander intervention can be influenced by individual characteristics, the relationship to the target, demographic similarities, and school climate (Barhight et al., 2017; Jenkins et al., 2023; Konishi et al., 2021; Thornberg & Jungert, 2013; Waasdorp et al., 2022). For example, friendships with the targets increase the likelihood of intervention (Dessel et al., 2017; Fowler & Buckley, 2022; Jenkins & Fredrick, 2017), while racial identity dynamics can influence the probability and effectiveness of reporting (Dessel et al., 2017; Jones et al., 2023; Waasdorp et al., 2022) In addition, gender plays a part in bystander behavior, with female students more likely to engage in defending behaviors, while male students are prone to confront or assist the aggressors (Evans & Smokowski, 2015; Jenkins & Nickerson, 2017; Lamb et al., 2024). While national efforts, such as the CDC’s YRBS, measure a range of youth health behaviors and bullying victimization trends, it does not include measures of bystander involvement (CDC, 2024), reflecting a broader gap in understanding how and why students choose to intervene. The present study examined bystander behavior in school-based bullying incidents, highlighting how children respond, what influences those responses, and how effective they believe their actions to be.

A total of 19.3% of students indicated that they had not been bullied but had witnessed someone else being the target of a form of bullying, reinforcing that indirect exposure to bullying is common in the school setting (Jenkins & Fredrick, 2017; Waasdorp et al., 2022). Several key insights into bystander behavior emerged from these findings. Almost half of the bystanders who witnessed bullying indicated that they considered the target of the bullying a friend. Further analysis revealed that when a bystander considered a target a friend, they were 3.57 times more likely to engage in defending behaviors such as reporting the incident to the school. Friendship also had an impact on the perceived effectiveness of bystander intervention, as bystander and target friendship resulted in 1.93 times more likely for the bullying to stop and 2.88 times more likely for it to decrease. Research suggests that close personal bonds of friendships increase empathy and perceived responsibility, which in turn increase defending behaviors (Dessel et al., 2017; Forsberg et al., 2018; Jenkins & Fredrick, 2017). In fact, research has consistently shown that higher levels of empathy predict defending behaviors (Malamut et al., 2022), and the presence of closer peer relationships may prompt increased intervention (Forsberg et al., 2018; Longobardi et al, 2020). These findings highlight the importance of helping targets of bullying develop supportive social networks that could mitigate aggressive bullying behaviors. While relational closeness may be a predictor of defending behaviors, other factors such as race and gender can also shape bystander decisions to intervene.

Demographic characteristics also seemed to function as predictors of bystander behaviors in bullying situations. Despite only making up approximately 10% of the survey sample, bystanders who identified as White were 3.52 times more likely to report the bullying incident to the school when the target was also White. This demographic connection also appeared to influence the effectiveness of reporting, resulting in bullying being 7.01 times more likely to stop when both the bystander and target were white. The finding that racial similarities between the bystander and the target increased the likelihood of reporting and perceived success of the intervention is consistent with previous research on in-group bystander behaviors; in that, individuals are more likely to defend those that they perceive as belonging to their own social group (Gonultas et al., 2019; Palmer et al., 2015, 2022; Waasdorp et al., 2022). However, this in-group defense is not consistent across all demographic groups, and bystander defending behaviors appear to be less likely within minority groups (Evans & Smokowski, 2015; Mulvey et al., 2020; Waasdorp et al., 2022). Gonultas et al. (2019) suggested that a likely reason the majority groups are more likely to engage in defending than minority groups is that minority groups might feel disenfranchised and are concerned about the potential of being targeted for victimization if they intervene. The perceptions of particular demographic groups could have a profound impact on the school environment; in that, it highlights the fact that bystander intervention may not be about just helping others in general, but who gets helped based on identity. If bystanders are more likely to be from the majority group and only intervene for in-group members, then out-groups may receive less support, underscoring the need for interventions that explicitly address in-group favoritism.

The type of bullying witnessed by a bystander also determines the response. In the current study, approximately 77% of students who reported witnessing bullying as a bystander reported that it occurred verbally. Only 32% reported witnessing physical bullying, and 15.9% reported relational bullying. This is consistent with previous research, as verbal bullying has been the most visible and reported type of bullying in schools (Bradshaw et al., 2013; McBrayer et al., 2025; Rigby, 2020; Woolley, 2019). However, in the current study, physical bullying was a stronger indicator for intervention. Results indicated that bystanders who witnessed physical bullying were 2.66 times more likely to fight back compared to any other type of bullying behavior. Perhaps this is because physical aggression is perceived as a more serious or severe offense; therefore, it is more likely to elicit a response (Forsberg et al., 2018; Thornberg et al., 2018), while verbal and relational bullying can be downplayed or ignored as joking or just having fun (Wang et al., 2009). This may explain why, even though verbal bullying is more common, it potentially does not elicit the same response as physical aggression. However, it should be noted that Wu et al. (2024) found that students were more likely to defend in situations of verbal and relational bullying because social norms supported intervention, but intervening in physical bullying carried a higher personal risk.

Implications and Conclusions

The current study highlights several implications for practice. First, findings suggest that friendship is a key factor in predicting bystander defending behavior. Friendship support is associated with prosocial bystander behavior (Evans & Smokowski, 2015) and has been shown to serve as a buffer for those at risk of being targeted (Kochel et al., 2015).In fact, research shows that bystanders were more likely to help a target if that person was a friend (Forsberg et al., 2018; Jenkins et al., 2023). One way for schools to increase defending behaviors is to focus on school-based interventions that promote strong peer relationships that encourage student intervention during bullying incidents (Evans & Smokowski, 2015). Peer-led initiatives, such as ambassador or mentoring programs, can further model and reinforce prosocial intervention.

Second, the findings suggest that bystanders were more likely to engage in direct defending when witnessing physical bullying, likely due to its severity, whereas verbal bullying, though twice as prevalent, did not elicit the same response. Students may not defend targets of verbal bullying because of the perception that it could be joking or teasing (Wang et al., 2009). However, verbal bullying over time can have a profound impact on a student’s mental health and confidence (Kapitanoff & Pandey, 2024). Friendship is a strong motivator for intervention (Jenkins et al., 2023). Therefore, to increase bystander intervention, schools can promote programs focused on teaching students to identify when types of behaviors transition from everyday conflict to bullying, as well as teaching strong prosocial norms to foster bystander interventions (Yun & Graham, 2018). This could include teaching students about the potential negative impacts of ignoring bullying and the positive impacts when students stand up and support one another (Salmivalli et al., 2011). Role-play scenarios and guided practice can further equip students with the language and strategies they need to intervene safely and effectively. Teacher training that explicitly recognizes and reinforces positive defending behaviors when they occur can also encourage students to repeat these actions.

Finally, the current findings highlight how in-group identification can influence a bystander’s willingness to intervene. This can be concerning in a school because higher rates of identity-based bullying are reported among minority youth (Galán et al., 2021), and this population of students is not only significantly less likely to report bullying (Webb et al., 2021) but they are also more likely to engage in inactive responses to bullying (Gonultas et al., 2019). Additionally, minority youth are especially vulnerable to factors that influence bullying victimization and perpetration, such as adverse home or school environments (Cook et al., 2010; Xu et al., 2020). Schools should explicitly address this bias in bystander training to ensure support for all students, regardless of identity. This may include portions of bullying prevention and intervention training that are culturally sensitive to the unique needs and experiences of minority groups (Lutrick et al., 2020), specific experiences of discrimination and bullying (Lorenzo-Blanco et al., 2016), or differences in cultural values between specific groups (Landers et al., 2024; Lozano et al., 2021). Embedding these practices within broader school climate frameworks, such as social and emotional learning (SEL) or positive behavior interventions and supports (PBIS), and strengthening anonymous reporting systems with visible follow-up can help ensure support for all students.

Limitations and Future Research

There are some limitations to the current study. First, the study relied on self-reported online surveys, which introduce potential errors. Responses to sensitive topics may be shaped by social desirability. Students may overreport pro-social action, underreport inaction, or simply inaccurately recall events or frequencies (Althubaiti, 2016). The use of online surveys could limit the response rate and introduce bias into the results as well. Online surveys are not only completed by those who are literate and have access to an online device, but also who are biased enough to take the time to complete the survey (Andrade, 2020). Finally, the high number of “I don’t know” responses in several parts of the survey suggests that bullying is a complex behavior that is not easily captured through survey questions. Similarly, the frequent use of “other” when naming reasons for being targeted points to the possibility that motives behind bullying may be more nuanced and harder to pinpoint. In some cases, a student’s experience may be driven by personal factors that do not fit in a predefined survey category. These could include subtle differences such as personality or relationship dynamics that do not have a clear cause. Students may choose “other” when explaining that they were bullied, underscoring the fact that the cause of bullying is not always obvious or easily identifiable.

In addition, the cross-sectional design of the study limits causal interpretations; while associations between friendship and defending behaviors were identified, causal direction cannot be determined. The reliance on a single method of data collection, which is student self-report surveys, also restricts the findings, as no teacher, peer, or observational data were available to triangulate responses. Finally, generalizability is limited by the characteristics of the sample, as cultural, socioeconomic, or contextual differences across schools may influence bystander behavior in ways not captured here.

Waasdorp et al. (2022) noted that programs to improve bystander intervention are still emerging, and more research is needed. Future research should explore culturally tailored interventions, particularly for minority students who face greater risks when intervening. For example, more research is needed to understand how racial/ethnic/cultural identity shapes bystander decision-making in the school setting, particularly for minority students who may perceive higher risks or fewer benefits for intervening. Examining in-group favoritism and disparities in support for bullying could provide deeper insight. In addition, while physical bullying drew more responses in this study, verbal bullying, while more prevalent, often goes unchallenged. Future research should focus on training that improves recognition of the harm caused by less visible or unrecognized bullying types and teaches lower-risk interventions.

Footnotes

ORCID iDs

Eric Landers

Meredith A. Rausch

Juliann Sergi McBrayer

Antonio Gutierrez De Blume

Monika Krah

Funding

The authors received no financial support for the research and/or authorship of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interests with respect to the authorship and/or publication of this article.

Author Biographies

Eric Landers is an associate professor at Georgia Southern University with a PhD in Special Education from the University of Florida. He specializes in behavior management, bullying prevention, and inclusive education. As Chair of the Southeast Conference on School Climate, he leads research on school safety and positive behavior support. He is a recognized expert in school behavior interventions, has authored peer-reviewed publications, secured research grants, and presents nationally and internationally.

Meredith A. Rausch, PhD, NCC, is an associate professor at the University of Alabama. A former Midwesterner, she earned her undergraduate degree in Public Speaking and master’s in Community Counseling from the University of Wisconsin–Whitewater, followed by a PhD in Counselor Education and Supervision from the University of Iowa. She also completed a certificate in improvisational comedy at the Second City in Chicago. Her diverse background includes serving as a personal trainer, figure competitor, and co-founder/vice president of a 501(c)3 supporting survivors of intimate partner violence. She has extensive experience with Veterans, including neuropsychological assessment, program development for military families, and crisis counseling. She has published over 30 peer-reviewed articles, three books, eight book chapters, and a textbook, and has spoken nationally. She co-created the podcast Hello Again, Friend and has received numerous honors, including the NBCC Servant Leadership Award and multiple research and teaching awards.

Kymberly Harris earned her PhD in interdisciplinary teacher education from the University of Alabama. With a focus on K-12 education, her professional journey has predominantly centered around engaging with incarcerated adolescents. Presently, she holds a position as a faculty member at Georgia Southern University. She brings a wealth of expertise to her academic pursuits, with a research agenda encompassing undergraduate research, positive behavior and supports (PBS), preservice teacher education, and school climate.

Juliann Sergi McBrayer, EdD, is an associate professor and co-director of the National Youth Advocacy and Resilience Research Center at Georgia Southern University. She directs the Educational Leadership master’s and specialist programs. She holds degrees from Georgia Southern University, Ohio University, and SUNY College at Buffalo. With over 20 years of experience, she has served as a professor, program coordinator, instructional leader, and classroom teacher. Her research focuses on educational leadership preparation, emphasizing professional learning that is purposeful, collaborative, and sustainable. She prioritizes school and community safety, mental health, well-being, and restorative justice in her scholarly work.

Antonio Gutierrez De Blume, PhD, is currently a professor at Georgia Southern University, where he teaches quantitative research methods and statistics. His research expertise is in examining metacognition under the theory of self-regulated learning. More specifically, he is interested in how learners monitor their comprehension during learning episodes. His program of research includes examining the effects of dispositional characteristics (e.g., various aspects of motivation) and learning strategy training on learners’ calibration (accuracy and bias), confidence in performance judgments, and performance as well as investigating the latent dimensions of calibration to improve its measurement.

Monika Krah, BS, is a doctoral student in Clinical Psychology at Georgia Southern University. She earned two Bachelor’s degrees in Psychology and German Studies, as well as a minor in European Studies, from the University of Connecticut. Before starting graduate school, she worked as a research assistant at the Yale Stress Center, managing and conducting research concerning binge drinking and obesity. Her research interests lie in whole health, or the interconnectedness between mental and physical health.

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