Hate-Related Victimization in Schools: Examining Risk and Protective Factors

Abstract

Despite the growing concern over hate crimes nationwide, our understanding of such crimes within educational settings remains limited. The current study aims to address this gap by examining potential risk and protective factors associated with hate-related victimization in schools. Utilizing data from the 2022 NCVS-SCS, we analyzed how various individual and school-related characteristics are associated with the likelihood of hate-related victimization. Findings indicate that being female, having a disability, and belonging to a marginalized racial or ethnic group face a heightened risk of experiencing hate-related victimization. Regarding school-related factors, the presence of misbehaving classmates and drugs in schools increases the risk of hate-related victimization, whereas metal detectors, perceived fairness of school rules, and respectful treatment by teachers reduce the risk. Findings, implications, and future directions are discussed.

Keywords

Introduction

Hate crimes within schools have become an important societal issue, drawing growing policy and scholarly attention. Hate-related victimization in school settings has increased since 2015, where youth spend a significant portion of their daily lives (Sahin-Ilkorkor, 2025). Between 2018 and 2022, schools¹ ranked as the third most common location for reported hate crime offenses in the United States, with primary and secondary schools being the most frequently reported school settings for such incidents (U.S. Department of Justice, Federal Bureau of Investigation, 2024). The impact of hate crimes extends beyond immediate physical and psychological harm, adversely affecting students’ academic performance and overall well-being (Cuellar et al., 2021). Thus, addressing hate crimes in school settings is crucial to promoting safe and supportive learning environments, as well as supporting the mental health of students (Kurpiel, 2024; Mulvey et al., 2018).

Students who experience hate-related victimization often face multiple risk factors that increase their vulnerability. Hate crimes are motivated by bias against the victim’s group membership in a protected class (Herek et al., 1999; Iganski & Lagou, 2015). Accordingly, existing research on school-based hate crimes examined students’ demographic risk factors, mostly focusing on race and ethnicity. Other protected characteristics such as disability and gender or gender identity may also shape students’ vulnerability to hate crime (Ellonen et al., 2021; Lo Cricchio et al., 2023). Moreover, many hate crime victims do not fit into a single marginalized identity but instead belong to multiple protected classes. These multiple marginalized identities can intersect, compounding students’ risk of hate-related victimization (Macdonald et al., 2021).

In addition, characteristics of the school environment can also contribute to students’ exposure to hate crimes (Cuellar et al., 2021). Factors such as school climate, geographic location, safety practices, and peer/faculty relationships can either buffer against or exacerbate the risk of hate-related victimization (Brewer et al., 2018; Kurpiel et al., 2023). Students’ behaviors and interactions within schools often mirror the values and norms of the larger society. When certain individuals are marginalized in school settings, their risk of experiencing hate-related incidents extends beyond the school context, perpetuating broader societal patterns of bias-motivated crimes (Foster & Brooks-Gunn, 2013; Kurpiel et al., 2023). Despite growing concern, empirical research that systematically examines student and school-related characteristics and their links to school-based hate crime remains limited.

Several studies using data from the National Crime Victimization Survey–School Crime Supplement (NCVS-SCS) have examined individual- and school-level correlates of hate-related victimization among youth (Gee et al., 2024; Joo et al., 2023). However, much of this research has examined these factors separately, often focusing on either individual student characteristics or school conditions. Relatively few studies have comprehensively assessed how both individual- and school-related risk and protective factors together predict hate-related victimization in school settings. As a result, important gaps persist in our understanding of the factors that shape students’ experiences with hate-related victimization in schools.

The current study aims to address this gap by examining how a range of student- and school-related characteristics are associated with hate-related victimization within schools, using data from the 2022 NCVS-SCS, administered to youth aged 12 to 18 attending primary or secondary schools. Understanding these relationships is important for educators, school administrators, and policymakers seeking to develop informed hate crime prevention policies and practices in schools.

Hate Crime in Schools

According to the FBI’s Uniform Crime Report, a hate crime is defined as “a committed criminal offense which is motivated, in whole or in part, by the offender’s bias(es) against a race, religion, disability, sexual orientation, ethnicity, gender, or gender identity” (Federal Bureau of Investigation [FBI], n.d.). Hate-related victimization has emerged as an important concern within school environments. In 2022, approximately 10% of the 13,346 reported hate crime offenses nationwide occurred at school locations (U.S. Department of Justice, Federal Bureau of Investigation, 2024). Hate-related incidents within school settings can take various forms, such as bullying, hate speech, and other discriminatory behaviors. These incidents are distinct from other forms of crime in that they are driven by the perpetrator’s bias toward the victim’s actual or perceived group membership, often reflecting an intent to assert power over individuals based on their identity (Kurpiel, 2024).

A growing body of scholarship has examined hate-related incidents in schools, particularly in the form of bias-based or identity-based bullying (Mulvey et al., 2018; Russell et al., 2012). Prior work has highlighted conceptual links between bullying and hate crimes in school settings, noting that some forms of identity-based bullying reflect bias-motivated aggression similar to hate incidents (Englander, 2007). In addition, research suggests considerable overlap between bias-based bullying, hate speech, and other forms of hate-motivated behavior, as they all involve harm directed at individuals because of their group identity (Gee et al., 2024; Kansok-Dusche et al., 2023). Importantly, however, not all bias-based bullying or hate-related verbal harassment meets the legal threshold required to be classified as a hate crime (FBI, n.d.). To avoid conflating legally defined hate crimes with broader forms of bias-motivated victimization experienced by students, we adopt the broader term hate-related victimization. This approach is consistent with prior research using the NCVS-SCS that conceptualizes these experiences as hate-related victimization rather than legally defined hate crimes (Pyo & Maher, 2026).

Individuals who experience hate-related victimization frequently report not only physical injury (Malcom & Lantz, 2021) but also psychological distress such as depression, anxiety, and posttraumatic stress disorder (Herek et al., 1999). For example, Eisenberg et al. (2024) found heightened emotional distress among youth who experienced hate-related words and bias-based bullying. In addition, Walton (2018) found that exposure to bias-based bullying disrupts normative development of the hypothalamic–pituitary adrenal (HPA) axis and weakens the immune system functioning in children. Beyond the direct harm to victims, hate-related incidents can also instill fear in broader school communities. The threat or presences of hate-motivated crimes can generate a ripple effect, adversely affecting not only the targeted individual but also their peers, friends, and bystanders who share the same group membership(s) (Iganski & Lagou, 2015; Kurpiel, 2024).

Understanding the factors that contribute to hate-related victimization in schools can be guided by several criminological theories. According to lifestyle-routine activities theory, the risk of victimization increases when there is a convergence of three elements: a motivated offender, a suitable target, and the absence of capable guardianship (Cohen & Felson, 1979; Ellonen et al., 2021). When applied to the school context, this theory suggests that students particularly from marginalized communities, such as racial, ethnic, or gender minorities, may face an elevated risk of hate-related crime victimization, particularly in school environments that are vulnerable to crimes. These may include schools with limited adult supervision, inadequate institutional safeguards, weak disciplinary practices, and inconsistent disciplinary practices. The lack of capable guardianship and clearly defined social norms can create opportunities for student perpetrators to engage in hate-related behavior with reduced fear of punishment.

Additionally, social disorganization theory highlights the importance of broader school structural and environmental conditions in shaping the prevalence of crime. This theory suggests that crime and victimization are most likely to occur in communities characterized by weakened social cohesion, concentrated poverty, residential instability, and ineffective institutional controls (Sampson & Groves, 1989; Shaw & McKay, 1942). In school contexts, these dynamics can translate into heightened risks for student victimization, particularly in schools with high levels of disorder, low-socioeconomic student populations, urban locations, and negative school climate with reduced student connectedness (Bradshaw et al., 2009).

These theoretical perspectives provide a complementary framework for understanding hate-related victimization in schools. Lifestyle-routine activities theory focuses on situational opportunities and individual-level vulnerability such as visibility of minority status and exposure to unsupervised areas. Further, social disorganization theory identifies broader ecological conditions such as weakened social cohesion, limited institutional control, and environmental disorder that may foster the emergence of motivated offenders and unmonitored spaces. Taken together, these theoretical perspectives suggests that hate-related victimization is not solely a product of individual vulnerabilities but also reflects how situational opportunities for victimization are shaped within broader structural and organizational conditions of school environments. This integrated perspective underscores the importance of examining both individual- and school-related characteristics to better understand the risk and protective factors associated with hate-related victimization in schools. Next, we discuss several student and school-related factors that may potentially serve as risk or protective factors influencing students’ likelihood of experiencing hate-related victimization.

Student Characteristics Related to Hate-Related Victimization

A range of student characteristics can contribute to increased vulnerability to hate-related victimization in schools. Among these, race and ethnicity have been consistently identified as key factors influencing students’ risk of being targeted. Existing evidence suggests that students from racially minoritized communities are disproportionately targeted for hate-related victimization in schools (Ellonen et al., 2021; Kurpiel et al., 2023). For example, Kurpiel et al. (2023) found that Black and Hispanic students were more likely than their White peers to experience hate-related words and bias-based bullying, which in turn contributed to heightened fear of victimization at school. Similarly, Gee et al. (2024) demonstrated that Asian American youth experienced increased incidents of hate-related verbal abuse and bias-based bullying during and after the COVID-19 pandemic. Relatedly, Walton (2018) found that positive self-concept with one’s identity or cultural background may serve as protective factors that reduce vulnerability to bias-motivated victimization. The study found that students with high levels of self-confidence in their racial, religious, or disability status were less likely to be targets of bias-based bullying.

Students with a disability are also disproportionately affected by hate-related victimization, and its impact can be more damaging compared to students without a disability. Students with mental, emotional, developmental, or behavioral disorders often face challenges in peer relationships, leading to increased social isolation. This marginalization can heighten their vulnerability to targeted peer victimization, including bullying, harassment, and hate speech (Iyanda, 2022). Brendli et al. (2022) found that children with intellectual disability are 2.84 times more likely to experience victimization compared to children without such disability. As a result, students with disabilities may avoid certain school activities or locations where they anticipate exclusion and harassment, including hate speech and bullying (Brewer et al., 2018).

Involvement in crime and violence can also influence students’ vulnerability to victimization. Peer violence often functions as both a cause and a consequence of victimization, creating a cyclical dynamic where aggression and victimization reinforce one another. Utilizing a nationally representative sample of U.S. youth aged 12 to 17, Jackson et al. (2013) found a positive relationship between delinquent activities and experiences of peer violent victimization. McCuddy and Esbensen (2021) supports this cyclical relationship, showing that both victimization and delinquency often share similar underlying risk factors, such as low self-control and association with delinquent peers.

Furthermore, students’ socioeconomic background may also shape their vulnerability to hate-related victimization in schools. Research consistently shows that students from lower socioeconomic backgrounds are at an increased risk of experiencing various forms of victimization. This heightened risk can be attributed to factors such as marginalization, social exclusion, and the perception of being different from peers, which can make these students more visible and vulnerable targets for victimization (Tippett & Wolke, 2014). For example, Jansen et al., (2012) found that children with parents of lower socioeconomic status face a higher risk of bullying. For another example, Foster and Brooks-Gunn (2013) found that students living in neighborhoods characterized by high crime rates, residential instability, and concentrated poverty were more likely to experience physical victimization at school. Overall, these findings underscore how various forms of disadvantage can make students more vulnerable to victimization. However, most existing studies have focused broadly on general bullying and victimization, leaving a critical gap in understanding the unique dynamics and drivers of hate-related incidents in schools. More research is needed to understand how student characteristics influence vulnerability to hate-related victimization to develop effective, targeted interventions for at-risk students.

School Characteristics Related to Hate-Related Victimization

School climate can play an important role in shaping students’ vulnerability to hate-related victimization. School climate refers to a broad concept that encompasses the physical environment, shared values and norms, and interpersonal relationships within a school community (Cuellar et al., 2021). A positive school climate is essential for fostering a safe learning environment where students feel valued and supported by their peers, faculty, and staff (Brewer et al., 2018). Research shows that students who maintain positive relationships with their teachers and feel a strong sense of connectedness to their schools are less likely to experience bullying and peer aggression (Cuellar et al., 2021; Leadbeater et al., 2015).

In contrast, indicators of school disorder such as the presence of drugs and gangs can be conducive to peer victimization (Kurpiel et al., 2023; Plank et al., 2008). Students attending schools with prevalent gang activity and frequent violent incidents tend to normalize aggressive behavior, which may increase the risk of hate-related victimization, particularly for vulnerable students (Carson & Esbensen, 2019). Cuellar et al. (2021) found that students’ perceptions of school disorder, including the presence of disruptive behavior and safety concerns, were associated with higher rates of victimization. Moreover, the absence of clear disciplinary measures and effective interventions may not only increase students’ exposure to hate-related incidents but also exacerbate fear and psychological distress, further leading them to disengage from academic and social activities (Walton, 2018).

School safety practices and policies can directly impact students’ experiences of crimes, including hate crimes. Common physical security measures adopted by schools include surveillance cameras and the deployment of school resource officers (SROs). Fisher et al. (2021) found that surveillance cameras can enhance students’ perceived safety while reducing fear of hate crime (Fisher et al., 2021). However, their effectiveness in preventing crime remains uncertain. Some research suggests that surveillance measures may even create privacy concerns, making students feel monitored and uncomfortable rather than secure (Hope, 2009). Similarly, research on the effectiveness of SROs presents mixed results. For example, Theriot and Cuellar (2022) found that the presence of SROs at schools had a positive effect on students’ perceived safety and security. However, Devlin et al. (2018) found that the presence of SROs did not significantly reduce school crimes such as bullying. This suggests that the mere presence of SROs may be insufficient for creating a safer environment unless accompanied by other supportive measures.

Research suggests that proactive safety strategies such as anti-bullying programs, faculty and staff training, and increased mental health support are more effective in addressing bullying and hate-related incidents in schools (Jantzer et al., 2025). Prior research emphasizes that teachers and school staff play a critical role in promoting safe school environments (Hammar Chiriac et al., 2024). Through daily interactions with students, they can recognize early signs of biased bullying or hate-related behaviors and intervene promptly (Brewer et al., 2018). Moreover, teacher support is strongly linked to students’ sense of belonging in school. Supportive educators can help students who have experienced peer victimization feel more connected, which can buffer against the harmful effects of hate-related victimization and bullying (Allen et al., 2022; Burgess et al., 2023).

In addition, available evidence indicates that school type (public vs. private) also impacts students’ experiences of hate-related victimization (Kurpiel et al., 2023). These studies suggest that students in public schools are more likely to experience hate-related victimization than those in private schools, potentially due to differences in school resources, disciplinary policies, and student demographics.

Taken together, existing literature highlights a complex array of factors—including not only individual characteristics but also school-level dynamics—that can influence students’ experiences of hate-related victimization in schools. Several studies using NCVS-SCS data have examined the relationships between individual- and school-related characteristics and hate-related victimization among youth (Gee et al., 2024; Joo et al., 2023). However, these factors are often examined separately, and relatively few studies have comprehensively assessed how they collectively relate to hate-related victimization in school settings. For example, using the 2019 NCVS-SCS, Boehme et al. (2024) examined bullying and hate-related victimization to compare their protective and risk factors across both individual- and school-level characteristics. Their findings indicated both overlapping and distinct predictors between these two forms of victimization. However, hate-related victimization in that study was operationalized using a single verbal indicator of bias-based victimization—specifically, whether a student had been called a bad name related to their protected group membership(s). While this measure captures an important form of bias-motivated harassment, it focuses primarily on verbal expressions of bias and may not fully capture the broader range of behaviors that constitute hate-related victimization in school settings. Consequently, additional research is needed to provide a more comprehensive assessment of how student- and school-related risk and protective factors are associated with multiple forms of hate-related victimization.

Current Study

There remains a notable gap in the research on how hate-related victimization unfolds within school settings, despite growing attention to school-based hate crimes. By focusing specifically on the school context, the current study aims to enhance understanding of the factors that may contribute to student’s experiences of hate-related victimization. Extending existing research, this study adopts a more comprehensive conceptualization of hate-related victimization by examining both hate-related words (verbal harassment) and bullying, which captures a range of bias-motivated bullying behaviors. Using the most recent wave of the 2022 NCVS-SCS, this study examines how both individual and school-related characteristics, as potential risk and protective factors, can predict hate-related victimization—specifically being called hate-related words and being bullied due to group membership(s). This study addresses the following research questions: (RQ 1) Which individual-related characteristics are associated with the likelihood of being called hate-related words and experiencing bias-based bullying? (RQ 2) which school-related characteristics are associated with the likelihood of being called hate-related words and experiencing bias-based bullying?

Methods

Data and Sample

The current study utilized data from the NCVS-SCS collected in 2022. The NCVS is a nationally representative household survey regarding criminal victimization in the United States. The NCVS employs a stratified, multistage cluster sampling design. In the first stage, Primary Sampling Units (PSUs)—consisting of counties or groups of counties—are selected based on geographic and demographic stratification. In the second stage, a systematic random sample of households is selected within these PSUs. All individuals aged 12 and older living in sampled households are eligible to participate. As a supplement to the NCVS, the SCS is administered to individuals aged 12 to 18 who have attended a primary or secondary school (grades 6–12) in the past 6 months. The SCS includes questions about students’ experiences with and perceptions of crime and safety at school, as well as school-related factors such as safety measures employed by schools, the school’s geographic location, and the level of crime in the surrounding neighborhood.

A total of 4,573 respondents participated in the 2022 NCVS SCS. Our analytic sample includes only respondents who answered hate-related victimization questions on their own, either in person or over the phone, excluding individuals who were interviewed through a proxy. Following existing approaches (Boehme et al., 2024; Wynne & Joo, 2011), we excluded respondents who reported receiving homeschooling during the school year instead of being enrolled in a public or private school. Since the study focuses on school-based victimization, homeschooled students may not have had the comparable exposure to school environments where such victimization may occur. Excluding these respondents helps reduce potential bias due to differences in exposure to school-based victimization. After excluding cases that did not meet the inclusion criteria and applying listwise deletion for missing data,² our final analytical sample comprised 2,573 individuals (56.3%). The majority of the final sample (78.6%) identified as White (9.1% Black; 5.6% Asian; 1.0% American Indian/Alaska Native; 0.5% Hawaiian/Pacific Islander; and 5.2% multiracial). Approximately 23.9% of respondents were of Hispanic origin. The sample consisted of 54.4% males and 45.6% females, with a mean age of 14.90 years (SD = 1.85; range = 12–18).

Measures

Dependent Variables: Hate-Related Victimization

Descriptive statistics for all measures are presented in Table 1. The two dependent variables are (a) being called hate-related words and (b) experiencing bias-based bullying. Being called hate-related words indicates whether a respondent has been insulted or called a bad name at school based on their race, religion, ethnic background or national origin, disability, gender, or sexual orientation (1 = Yes, 0 = No). While hate-related words capture bias-based verbal harassment, bias-based bullying encompasses not only verbal harassment but also broader forms of bullying targeting protected identities. Consistent with existing approaches (Joo et al., 2023), we constructed a binary measure indicating whether a respondent experienced any of the bullying behaviors defined by the NCVS SCS³ during the current school year due to their membership in any of these groups (1 = Yes, 0 = No).

Table 1.

Measure Descriptive Statistics (N = 2,573).

Variables	M or proportion	SD	Min−Max
Hate-related words	0.07		0−1
Bias-based bullying	0.06		0−1
Gender (female)	0.46		0−1
Race (White)	0.79		0−1
Hispanic origin	0.24
Disability	0.07		0−1
Fought at school	0.03		0−1
School instability (Attended two or more schools)	0.04		0−1
Perceived neighborhood crime	1.55	0.68	1−4
Age (years)	14.90	1.85	12−18
School type (Public)	0.93		0−1
School location
City	0.24
Suburb	0.39
Town	0.14
Rural	0.23
Perceived neighborhood crime near school	1.65	0.67	1−4
Misbehaving classmates	0.43		0−1
Gun presence	0.05		0−1
Gang presence	0.06		0−1
Drug presence	0.24		0−1
School safety measures
Security guards or police officers	0.80		0−1
Adult supervision in hallways	0.91		0−1
Metal detectors	0.12		0−1
Security cameras	0.97		0−1
Code of student conduct	0.96		0−1
Perceptions of school authorities
Fairness of school rules	3.17	0.57	1−4
Fairness of punishment	3.16	0.68	1−4
Strict enforcement of school rules	3.06	0.65	1−4
Student awareness of punishment	3.11	0.63	1−4
Teacher respect for students	3.26	0.59	1−4
Presence of supportive adults (3 item; α = .86)	3.39	0.49	1−4
Presence of supportive peers (3 item; α = .91)	3.40	0.52	1−4

Although the two measures capture distinct forms of bias-based victimization, both reflect victimization related to protected identities. This type of victimization is conceptually distinct from general bullying that is not motivated by bias toward a protected group and may involve different mechanisms and correlates (Boehme et al., 2024).

Independent Variables

Individual Characteristics

We included measures of several student characteristics guided by theoretical and empirical evidence regarding factors that may contribute to student victimization in schools, including hate crimes (Brendli et al., 2022; Cuellar et al., 2021; Kurpiel et al., 2023). The following variables were measured: (a) gender at birth (1 = female, 0 = male); (b) race/ethnicity (1 = person of color, 0 = White), (c) Hispanic origin (1 = yes, 0 = no), (d) disability⁴ (1 = yes, 0 = no); (e) involvement in physical fights at school during the current school year (1 = yes, 0 = no); (f) school instability during the current school year (1 = attended two or more schools, 0 = attended one school); (g) perceived neighborhood crime (1 = strongly disagree; 4 = strongly agree); and (h) age (years). Perceived neighborhood crime was measured by respondents’ level of agreement with the statement, “There is a lot of crime in the neighborhood where you live,” with higher scores indicating higher perceived crime rate.

School Characteristics

We included measures of school-related factors based on existing research (Bradshaw et al., 2009; Brewer et al., 2018; Fisher et al., 2021; Walton 2018). These include (a) school type, indicating whether the respondent attends a public school (coded as 1) or private school (coded as 0) and (b) school location (1 = city, 2 = suburb, 3 = town, 4 = rural). We also examined (c) perceived neighborhood crime near school, measured by the level of agreement with the statement “There is a lot of crime in the neighborhood where you go to school” (1 = strongly disagree; 4 = strongly agree), with higher scores indicating a higher perceived crime rate.

To measure the school disciplinary environment, we included an item assessing how often respondents are distracted from doing schoolwork because of (a) misbehaving classmates using a 4-point scale (1 = never, 2 = almost never, 3 = sometimes, 4 = most of the time). For multivariate analyses, responses of “sometimes” and “most of the time” were combined into one category of “Yes, misbehaving classmates” (coded as 1), while “never” and “almost never” were combined into the category of “No, misbehaving classmates” (coded as 0; Boehme et al., 2024).

We included dichotomous measures of student exposure to crime and violence (1 = yes, 0 = no), we examined (a) gun presence, which asked whether respondents saw another student who brought a gun to school, (b) gang presence, which assessed whether there are any gangs at school, and (c) drug presence, which asked whether respondents saw another student under the influence of illegal drugs or alcohol while at school (Kurpiel et al., 2023).

Our analyses also included several dichotomous school safety measures (1 = yes, 0 = no) including (a) the presence of security guards or police officers; (b) adult supervision in hallways; (c) the use of metal detectors; (d) the presence of security cameras; and (e) the enforcement of code of student conduct.

We considered students’ perceptions of school authorities. Respondents indicated their level of agreement or disagreement on a 4-point Likert scale (1 = strongly disagree to 4 = strongly agree) with statements assessing various aspects of school authority. These included (a) fairness of school rules (“The school rules are fair”), (b) fairness of punishment (“The punishment for breaking school rules is the same no matter who you are”), (c) student awareness of punishment (“If a school rule is broken, students know what kind of punishment will follow”), (d) strict enforcement of school rules (“The school rules are strictly enforced”), and (e) teacher respect for students (“Teachers treat students with respect”). Higher scores indicated more positive perceptions of the respective aspect being measured.

Finally, we considered the presence of supportive adults and peers (Joo et al., 2023; Tian, 2021). We used a three-item measure of supportive adults (Cronbach’s α = .86). Respondents were asked whether there is a teacher or other adult at school who (a) “really cares about you,” (b) “listens to you when you have something to say,” and (c) “tells you when you do a good job.” We used a three-item measure of supportive peer students (Cronbach’s α = .91). Respondents were asked whether there is a student at school who (a) “really cares about you,” (b) “listens to you when you have something to say,” and (c) “believes that you will be a success.” All items were measured on a 4-point Likert scale (1 = strongly disagree to 4 = strongly agree), with higher scores indicating stronger support from adults and peers at school. Responses for supportive adults and supportive peers were averaged across their respective items to create two mean indices.

Analytic Strategy

Given that both of our dependent variables—hate-related words and bias-based bullying—are binary measures, we employed a logistic regression framework to estimate the effects of student and school characteristics. Due to the skewed distribution of these variables (with 7.23% of students reporting being called hate-related words and 6.30% experiencing bias-based bullying), we explored two extensions of the standard binary logistic model to test the robustness of our results. First, we estimated a skewed logistic regression model to address the bias that can arise when the outcome variable is highly imbalanced. This issue is particularly relevant when one outcome category represents fewer than 30% of observations, which can distort coefficient estimates in a standard logit model (Nagler, 1994). Second, given the rare-event nature of our dependent variables, we employed Firth’s logistic regression, a method originally proposed by Firth (1993) and developed by Coveney (2021) in Stata. This approach applies penalized maximum likelihood estimation to mitigate the bias and inflated standard errors that can occur when modeling rare events. Further, variance inflation factors (VIF) were acceptable (mean VIF = 1.31 for both outcomes), indicating that multicollinearity was not an issue in the current analyses. All analyses were conducted using Stata version 19.0. The syntax used for the analyses is available from the corresponding author upon request.

Results

Hate-Related Words

Table 2 presents the results of logistic regression models examining the likelihood of being called hate-related words. Overall, findings across the three models were consistent, with minimal differences in the significance, direction, and magnitude of the effects of individual and school characteristics. Regarding individual characteristics, gender, race, and disability status were significant predictors in all models. Respondents who were female (1.39 < OR < 1.69; p < .05), a person of color (0.44 < OR < 0.62; p < .05), and had a disability (3.61 < OR < 10.38; p < .01) were more likely than their counterparts to be called hate-related words because of their race, religion, ethnic background or national origin, disability, gender, or sexual orientation.

Table 2.

Logistic Regression Models: Hate-Related Words (N = 2,573).

Independent variables	Standard logistic			Skewed logistic			Firth’s logistic
Independent variables	b	SE	Odds ratio	b	SE	Odds ratio	b	SE	Odds ratio
Individual characteristics
Gender (female)	0.34*	0.17	1.40	0.52*	0.28	1.68	0.33*	0.17	1.39
Race (Person of color)	0.49*	0.20	1.63	0.82*	0.38	2.27	0.48*	0.19	1.62
Hispanic origin	0.10	0.20	1.11	0.24	0.32	1.28	0.11	0.20	1.11
Disability	1.31***	0.22	3.72	2.34**	0.85	10.38	1.29***	0.22	3.61
Fought at school	0.23	0.35	1.25	0.59	0.63	1.80	0.23	0.34	1.26
School instability	0.38	0.36	1.46	0.35	0.60	1.42	0.40	0.35	1.50
Perceived neighborhood crime	0.15	0.13	1.16	0.15	0.21	1.17	0.14	0.13	1.15
Age (years)	−0.08	0.05	0.92	−0.13	0.07	0.88	−0.08	0.05	0.92
School characteristics
School type (Public)	0.39	0.50	1.48	0.39	0.62	1.47	0.30	0.48	1.36
School location (rf: City)
Suburb	−0.20	0.21	0.82	−0.30	0.32	0.74	−0.19	0.21	0.82
Town	−0.60	0.30	0.55	−0.88	0.49	0.42	−0.57	0.30	0.57
Rural	−0.05	0.25	0.95	0.02	0.37	1.02	−0.04	0.24	0.96
Perceived neighborhood crime near school	0.16	0.24	1.18	0.27	0.23	1.31	0.16	0.13	1.18
Misbehaving classmates	1.15***	0.20	3.17	1.55***	0.41	4.71	1.13***	0.20	3.09
Student exposure to crime and violence
Gun presence	0.42	0.28	1.52	0.46	0.49	1.58	0.42	0.25	1.52
Gang presence	0.50	0.25	1.60	0.85	0.52	2.34	0.46	0.25	1.58
Drug presence	0.89***	0.19	2.44	1.35**	0.44	3.87	0.88***	0.18	2.40
School safety measures
Security guards or police officers	0.51	0.27	1.66	0.58	0.37	1.78	0.48	0.26	1.61
Adult supervision in hallways	−0.21	0.30	0.81	−0.40	0.45	0.67	−0.23	0.29	0.80
Metal detectors	−0.80*	0.32	0.45	−0.86	0.44	0.42	−0.75*	0.31	0.47
Security cameras	1.64	1.05	5.17	2.20	1.40	9.02	1.24	0.88	3.45
Code of student conduct	−0.02	0.42	0.98	0.24	0.66	1.27	−0.06	0.40	0.94
Perceptions of school authorities
Fairness of school rules	−0.27	0.16	0.76	−0.42	0.25	0.66	−0.27	0.15	0.77
Fairness of punishment	−0.11	0.14	0.89	−0.21	0.22	0.81	−0.12	0.13	0.89
Strict enforcement of school rules	−0.08	0.13	0.92	−0.16	0.21	0.85	−0.08	0.13	0.92
Student awareness of punishment	−0.09	0.14	0.91	−0.10	0.21	0.90	−0.09	0.14	0.92
Teacher respect for students	−0.10	0.15	0.90	−0.27	0.25	0.76	−0.10	0.15	0.91
Presence of supportive adults	−0.03	0.21	0.99	0.14	0.33	1.15	−0.01	0.21	0.99
Presence of supportive peers	0.61**	0.19	1.84	0.89*	0.35	2.43	0.59**	0.19	1.81
Constant	−4.99**	1.69	0.01	−1.95	0.67	0.04	−4.38**	1.57	0.01
LL	−535.23			−532.69			−487.02

p < .05. **p < .01. ***p < .001.

Turning to school-related characteristics, the presence of misbehaving classmates (3.09 < OR < 4.71; p < .001) and drugs at school (p < .01) significantly increased the likelihood of being called hate-related words. Gang activity also appeared to elevate this risk, though its effect did not reach statistical significance at the level of .05. In contrast, the presence of metal detectors reduced the likelihood of being called hate-related words (1.62 < OR < 2.27; p < .05). Unexpectedly, the presence of supportive peers was associated with a higher likelihood of being called hate-related words (1.81 < OR < 1.84; p < .05) across all models. However, this finding may be underpowered due to the relatively small proportion of participants who disagreed or strongly disagreed with individual items (ranging from 2.9% to 4.7%).

Bias-Based Bullying

Table 3 presents the results of logistic regression models examining the likelihood of experiencing bias-based bullying. Overall, findings across the three models were consistent, in terms of the significance, direction, and magnitude of the effects of individual and school characteristics. Regarding individual characteristics, gender, disability status, and age were significant predictors in all models. Respondents who were female (1.73 < OR < 1.31; p < .01), younger (0.80 < OR < 0.86; p < .01), and had a disability (3.68 < OR < 10.32; p < .001) were more likely to experience bias-based bullying because of their race, religion, ethnic background or national origin, disability, gender, or sexual orientation.

Table 3.

Logistic Regression Models: Bias-Based Bullying (N = 2,573).

Independent variables	Standard logistic			Skewed logistic			Firth’s logistic
Independent variables	b	SE	Odds ratio	b	SE	Odds ratio	b	SE	Odds ratio
Individual characteristics
Gender (female)	0.57**	0.18	1.76	0.84**	0.29	2.31	0.55**	0.18	1.73
Race (Person of color)	0.10	0.22	1.10	0.31	0.34	1.37	0.10	0.22	1.11
Hispanic origin	−0.04	0.22	0.96	−0.07	0.33	0.93	−0.03	0.22	0.97
Disability	1.33***	0.23	3.79	2.33***	0.61	10.32	1.30***	0.23	3.68
Fought at school	0.20	0.38	1.22	0.87	0.70	2.39	0.21	0.37	1.24
School instability	0.57	0.37	1.77	0.70	0.63	2.01	0.58	0.36	1.80
Perceived neighborhood crime	0.07	0.15	1.08	0.00	0.24	1.00	0.07	0.15	1.07
Age (years)	−0.15**	0.05	0.86	−0.22**	0.08	0.80	−0.14**	0.05	0.86
School characteristics
School type (Public)	0.64	0.56	1.90	0.70	0.73	2.02	0.53	0.53	1.71
School location (rf: City)
Suburb	0.04	0.24	1.04	−0.16	0.37	0.85	0.03	0.24	1.03
Town	−0.29	0.32	0.75	−0.55	0.49	0.57	−0.27	0.31	0.76
Rural	0.18	0.27	1.20	0.08	0.40	1.09	0.18	0.26	1.20
Perceived neighborhood crime near school	0.03	0.15	1.03	0.19	0.25	1.21	0.03	0.15	1.03
Misbehaving classmates	0.92***	0.21	2.51	1.25***	0.33	3.50	0.90***	0.21	2.46
Gun presence	0.53	0.29	1.70	0.66	0.51	1.93	0.53	0.28	1.70
Gang presence	0.16	0.29	1.18	0.51	0.50	1.66	0.16	0.29	1.18
Drug presence	1.09***	0.20	2.96	1.63***	0.38	5.09	1.06***	0.20	2.89
School safety measures
Security guards or police officers	0.19	0.26	1.21	0.42	40	1.52	0.17	0.26	1.19
Adult supervision in hallways	−0.37	0.30	0.69	−0.39	0.48	0.68	−0.38	0.30	0.68
Metal detectors	−0.83*	0.36	0.43	−1.04*	0.51	0.35	−0.77*	0.35	0.46
Security cameras	1.55	1.05	4.73	2.44	1.56	11.48	1.15	0.87	2.92
Code of student conduct	0.68	0.56	1.98	1.31	0.89	3.69	0.57	0.53	3.16
Perceptions of school authorities
Fairness of school rules	−0.22	0.17	0.80	−0.39	0.29	0.68	−0.22	0.16	0.80
Fairness of punishment	−0.32*	0.13	0.73	−0.60*	0.25	0.55	−0.31*	0.13	0.73
Strict enforcement of school rules	−0.04	0.14	0.96	−0.08	0.223	0.92	−0.04	0.14	0.96
Student awareness of punishment	−0.11	0.15	0.90	−0.20	0.23	0.82	−0.11	0.14	0.90
Teacher respect for students	−0.35*	0.16	0.70	−0.58*	0.27	0.56	−0.34*	0.15	0.71
Presence of supportive adults	0.51*	0.23	1.68	0.99*	0.39	2.70	0.50*	0.22	1.65
Presence of supportive peers	0.19	0.20	1.20	0.13	0.31	1.14	0.19	0.20	1.20
Constant	−3.83*	1.80	0.02	−1.71	2.63	0.18	−3.18	1.67	0.04
LL	−476.40			−471.39			−430.26

p < .05. **p < .01. ***p < .001.

Regarding school-related characteristics, the presence of misbehaving classmates (2.46 < OR < 3.50; p < .001) and drugs at school (2.89 < OR < 5.09; p < .001) significantly increased the likelihood of experiencing bias-based bullying. In addition, the presence of metal detectors (0.35 < OR < 0.46; p < .05), perceived fairness of school punishment (0.25 < OR < 0.73; p < .05), and teachers’ treatment of students with respect (0.27 < OR < 0.71; p < .05) reduced the likelihood of experiencing bias-based bullying. Unexpectedly, the presence of supportive adults was associated with a higher likelihood of experiencing bias-based bullying (1.65 < OR < 2.70; p < .05). However, this finding may be underpowered due to the relatively small proportion of participants who disagreed or strongly disagreed with individual items (ranging from 2.3% to 4.0%).

Additional Analyses

The NCVS-SCS includes person-level sampling weights that allow for nationally representative estimation. Because the study compared three logistic estimators, including Firth logistic regression, and the Firth estimator available in Stata does not support probability weights, we estimated the three primary models without weights to ensure comparability across modeling approaches. To assess the generalizability of the findings to the population eligible for the SCS supplement, we tested supplementary traditional and skewed logit models using the person-level weight provided by the NCVS. Findings were largely consistent across the weighted and unweighted analyses. One exception was observed for the models predicting hate-related words. Although White respondents were less likely than respondents of color to report being called hate-related words, this association did not reach statistical significance at the .05 level (traditional logit: b = −0.42, SE = 0.22, OR = 0.66, p = .052; skewed logit: b = −0.52, SE = 0.28, OR = 0.59, p = .068).

Discussion

The aim of this study was to examine how both individual and school characteristics are related to hate-related victimization in schools. The analysis revealed several potential risk and protective factors that are worthy of further discussion.

Regarding individual characteristics, we found that being female, a person of color, and having a disability significantly increased students’ likelihood of being called hate-related words. Similarly, female, disability status, and younger age elevated the vulnerability to bias-based bullying. These findings align with prior research suggesting the heightened vulnerability of marginalized students to bias-based victimization. Racial and ethnic minority students have been found to experience disproportionately high levels of victimization compared to their white counterparts, often rooted in systemic racism (Kurpiel et al., 2023). Similarly, gender-based vulnerabilities have been consistently observed in school settings with girls more likely to be targeted for social and relational aggression (Faris & Felmlee, 2014), which may increase their vulnerability to hate-related victimization. The increased risk of hate-related victimization among students with disabilities observed in our study aligns with research showing that youth with intellectual, emotional, or developmental disabilities face greater social marginalization and peer rejection, which in turn can increase their vulnerability to victimization (Brendli et al., 2022; Dion et al., 2018; McDonnell et al., 2019).

It is important to note, however, that differences emerged between bias-based bullying and hate-related words with respect to individual characteristics. Being a person of color was associated with an increased likelihood of being called hate-related words but was not significantly associated with experiencing bias-based bullying. Consistent with existing findings (Morales et al., 2019), this finding suggests that verbal harassment may be more closely tied to racial identity than broader forms of bias-based bullying. In contrast, age was associated with an increased likelihood of bias-based bullying but not with being called hate-related words. Younger age may be particularly vulnerable to bullying behaviors motivated by bias. Overall, findings suggest the potential role of developmental differences in shaping the prevalence and forms of hate-related victimization among students (Astor et al., 2001).

Beyond individual vulnerabilities, our findings highlight the important role of school-related characteristics in shaping students’ risk of hate-related victimization. Notably, the presence of misbehaving classmates and drugs significantly increased the likelihood of experiencing both types of hate-related victimization. These findings are consistent with prior research indicating that disordered school environments increase students’ risk for a range of peer victimization experiences (Vidourek & King, 2019). Our findings suggest that poorly managed school climates may foster permissive attitudes toward aggressive behavior, including hate-related crime.

Moreover, our findings indicate that certain protective factors within schools decreased students’ vulnerability to experiencing hate-related victimization. The presence of metal detectors was associated with reduced exposure to both hate-related words and bias-based bullying. Perceived fairness of punishment and teacher respect for students decreased instances of bias-based bullying but not hate-related words. These findings suggest that positive school climates—characterized by fairness, respect, and support from school authorities—can mitigate risk for bias-based bullying in schools.

These findings are well aligned with the U.S. Department of Education’s (n.d.) school climate framework, which outlines three main domains that contribute to a positive school climate: safety, student engagement, and the overall school environment. These dimensions encompass not only students’ feelings of physical and emotional safety but also the quality of relationships within the school and how school rules and policies are enforced. Our findings provide empirical support for this model, particularly in demonstrating how students’ perceptions of procedural justice and teacher–student respect contribute to a climate less conducive to hate-related victimization. Future research may benefit from using these school climate domains as a systematic framework for grouping and examining school-related factors, which could help clarify how multiple contextual features collectively shape students’ risk of hate-related victimization. In addition, future studies may consider integrating other theoretical perspectives such as social ecological framework (Espelage et al., 2013) to further understand how broader school structures shape hate-related victimization.

Unexpectedly, we found that the presence of supportive peers and adults at school was associated with an increased likelihood of hate-related victimization. This finding contrasts with prior research, which highlights the protective role of strong social support in buffering against bullying and hate-related experiences (Allen et al., 2022; Burgess et al., 2023). One possible explanation is that students who experience victimization may be more likely to seek out support from peers and adults in response to their victimization. Another possibility is that the proportion of students reporting low levels of support was small, possibly limiting the variability needed to detect the protective effects. However, this relationship should be interpreted as correlational rather than causal, as the cross-sectional nature of the data does not allow us to determine the direction of the relationship.

Overall, this study extends prior research on hate-related victimization in school settings by examining how student- and school-related characteristics are associated with different forms of hate-related victimization in schools. The present study contributes to the literature on risk and protective factors related to related to such victimization. Building on Boehme et al.’s (2024) study that examined general bullying and hate-related words and their associated risk and protective factors, our study distinguishes between hate-related words (verbal harassment) and bias-based bullying, which captures a broader range of hate-motivated bullying behaviors, providing a more comprehensive assessment of hate-related victimization. This distinction allows us to assess how different forms of hate-related victimization share common risk and protective factors. Consistent with Boehme et al. (2024), our findings highlight the importance of both individual and school-related characteristics in shaping students’ risk of hate-related victimization. However, some predictors operate differently across forms of victimization, suggesting that distinct mechanisms may underlie verbal bias-based harassment and broader hate-motivated bullying behaviors. These findings indicate that interventions addressing hate-related victimization may need to account for differences between verbal harassment and broader bias-based bullying behaviors. Future research should further examine the mechanisms linking individual and school-related characteristics to these distinct forms of bias-motivated victimization.

Practical Implications

This study offers several important practical implications by highlighting the role of particular student and school characteristics in shaping the risk of hate-related victimization. These insights offer actionable guidance for educators, administrators, and policymakers in allocating resources and school climate initiatives aimed at preventing hate-related incidents. Our findings suggest that schools should provide targeted support for students at greater risk, particularly those from ethnically minority communities, female students, and individuals with disabilities. Such support may include mentoring programs, counseling services, and peer support groups that create spaces for students to share experiences of hate-related victimization and receive emotional support from peers who have faced similar challenges. Schools may also implement culturally responsive counseling and staff training programs to better recognize and respond to bias-motivated victimization.

Promoting a positive school climate is also essential in mitigating hate crimes. Our findings suggest that schools should adopt comprehensive strategies to address indicators of disorder, such as misbehaving classmates and exposure to drugs, which may create conditions that facilitate peer aggression and bias-motivated behavior. Strengthening supervision and clearly communicating behavioral expectations may help reduce such risks. At the same time, strengthening schoolwide protective measures—such as installing metal detectors where necessary, ensuring fair enforcement of school policies, and fostering respectful interactions between students and teachers—can contribute to a safer learning environment.

Limitations and Future Research Directions

Several limitations of this study should be acknowledged. First, the reliance on self-reported data introduces the potential for recall bias, social desirability, and underreporting, particularly given the sensitive nature of hate-related victimization (Cuellar et al., 2021). Second, the cross-sectional design limits our ability to make causal relationships between individual and school characteristics and hate-related victimization. Future research would benefit from employing longitudinal studies to examine how these relationships unfold over time. Incorporating mixed-method approaches that combine quantitative data with qualitative interviews or focus groups would help enrich our understanding of students’ experiences of hate-related victimization. Third, this study may not have accounted for other factors that could influence hate-related victimizations such as student sexual identity, socio-economic status, and family support (Parodi et al., 2025). Importantly, this study does not account for structural factors outside of the school context such as community socioeconomic disadvantage and neighborhood disorder that may shape students’ exposure to criminal victimization (Espelage et al., 2013). Future research should incorporate these broader contextual factors to better understand how structural conditions influence students’ vulnerability to hate-related victimization in school settings. Fourth, due to the rising use of digital technology and social media among youth, future scholars should also consider the growing impact of desensitization and exposure to hate online and its effect on creating violent behaviors in person (Madriaza et al., 2025).

Limitations related to measurement should be acknowledged. In the NCVS-SCS, a question about current gender identity is asked only of respondents aged 16 or older. Since our sample includes students aged 12 to 18, a consistent measure of gender identity was not available for all respondents. Therefore, the analyses rely on gender assigned at birth rather and do not capture potential variation in victimization experiences related to respondents’ current gender identity. In addition, following existing approaches (Joo et al., 2023), we used a binary indicator of bias-based bullying reflecting whether respondents reported any of the bullying behaviors defined by the NCVS SCS due to their group membership(s). However, this operationalization does not explicitly capture the repetition of bullying behaviors—an important feature of bullying (Olweus, 2013)—and may therefore include relatively infrequent incidents, potentially overestimating the prevalence of bullying. Future research should incorporate measures that capture the frequency or persistence of bullying behaviors to better understand how individual- and school-related characteristics shape bias-based bullying in school settings. Finally, given that several racial groups had relatively small numbers of respondents, we constructed a binary measure of race/ethnicity comparing White students with students of color to ensure sufficient statistical power for multivariate analyses. However, this approach may mask heterogeneity across racial and ethnic groups and limits the ability to examine intersectional differences in hate-related victimization. Future research with larger or more diverse samples should examine variation across racial and ethnic groups and intersectional identities to better understand how bias-motivated victimization differs across marginalized student populations.

Conclusion

Despite these limitations, this study provides important insights into the factors associated with hate-related victimization in school settings. The findings highlight the importance of addressing both individual- and school-related characteristics to reduce students’ risk of hate-related victimization. Importantly, the results reveal meaningful differences across forms of victimization, suggesting that hate-related words and bias-based bullying may be shaped by distinct mechanisms. These findings emphasize the need for systemic and targeted interventions aimed at improving school climate and protecting vulnerable student populations. Overall, this study contributes to a better understanding of hate-related victimization in educational environments and informs ongoing efforts to create safer and more inclusive schools.

Footnotes

Acknowledgements

We thank the anonymous reviewers for their helpful comments.

ORCID iD

Jimin Pyo

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.

Notes

Author Biographies

Michelle Penagos, BA, is a graduate student in the School of Public Affairs at San Diego State University. Her research interests center on examining the sociological and psychological factors related to crime and victimization. She is particularly interested in understanding the psychological motivations behind criminal behavior and delinquency in low-income neighborhoods. Additionally, she is interested in exploring the victims and potential victims’ perspectives on crime and vulnerability to crime.

Jimin Pyo, PhD, is an associate professor in the Department of Criminology and Justice Studies at California State University, Northridge. Her research interests center on individuals’ perceptions in the criminal justice context such as confidence in criminal justice institutions, legitimacy, perceived crime risk, and fear of crime. Her work has been published in such journals as Crime and Delinquency, Criminal Justice and Behavior, Journal of Experimental Criminology, and Journal of Interpersonal Violence, among others.

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