EXAMINING BIAS-BASED CYBER VICTIMIZATION AMONG YOUTH: PREVALENCE, EMOTIONAL IMPACT, AND USE OF COPING STRATEGIES By Samantha Schires A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Psychology- Doctor of Philosophy 2021 ABSTRACT EXAMINING BIAS-BASED CYBER VICTIMIZATION AMONG YOUTH: PREVALENCE, EMOTIONAL IMPACT, AND USE OF COPING STRATEGIES By Samantha Schires Bias-based peer victimization focuses on social group membership of the victim, and it has been associated with more severe mental health outcomes than non-bias based forms of victimization. However, little research has examined how victims are affected by, and respond to, these experiences in an online context. The first study of this dissertation sought to fill these gaps in the literature, examining the prevalence, psychological impacts, and coping strategies of bias- based forms of cyber victimization among a sample of 808 emerging adults. Our findings indicated that gender and race impacted the risk for victimization, and that bias-based victimization was particularly distressing for racial and ethnic minority participants. Furthermore, coping strategies in response to online aggression varied in their effectiveness. Reporting strategies were associated with a higher emotional impact of victimization, while social support-seeking strategies were correlated with decreases in emotional impact, particularly among women. The current project also examined how various social identities interact to affect the risk and experience of victimization. Our second study included a sample of 397 young women of color to examine the ways in which membership in multiple marginalized social categories may place youth at an increased risk for cyber victimization. We found that women who were targeted for their gender and race simultaneously (i.e., racialized sexualized victimization) were targeted more frequently and experienced a more severe emotional impact of the experience than those targeted based on one social identity. Moreover, women who experienced racialized-sexualized victimization were unlikely to report the situation, and they were more likely than all other groups to seek social support. Our findings have critical implications for prevention and intervention efforts to combat race-based, sex-based, and racialized-sexualized forms of peer victimization. ACKNOWLEDGMENTS I would like to extend my appreciation for my mentors, colleagues, family and friends for their help and guidance throughout graduate school and in my dissertation project. First, I am so very thankful to Dr. Alex Burt for her support and mentorship over the past five years. Her investment in helping me work toward my goals throughout graduate school and her genuine passion for research and her students’ success have been crucial for my professional development and growth. I am also extremely grateful to my committee members Dr. NiCole Buchanan, Dr. Jae Puckett, and Dr. Saleem Alhabash. As members of my doctoral guidance committee, I have appreciated their thoughtful feedback on my project and vast knowledge they have provided in cyberbullying and intersectionality and for taking the time to serve on my committee. My journey through graduate school would also not have been possible without the support of my family and friends, particularly my parents Randy and Marie Schires. They have always supported my academic progress and provided me with the resources to needed to be successful, and I’m beyond grateful for their enduring support. iv TABLE OF CONTENTS LIST OF TABLES ......................................................................................................................... vi LIST OF FIGURES ...................................................................................................................... vii INTRODUCTION .......................................................................................................................... 1 STUDY 1 ...................................................................................................................................... 11 Methods............................................................................................................................. 20 Results ............................................................................................................................... 25 Discussion ......................................................................................................................... 31 STUDY 2 ...................................................................................................................................... 37 Methods............................................................................................................................. 43 Results ............................................................................................................................... 47 Discussion ......................................................................................................................... 50 GENERAL DISCUSSION ........................................................................................................... 57 APPENDICES…………………………………………………………………………………...64 APPENDIX A: TABLES .................................................................................................. 65 APPENDIX B: FIGURES ................................................................................................ 85 REFERENCES ............................................................................................................................. 87 v LIST OF TABLES Table 1. Research Questions and Hypotheses…………………………………………………...66 Table 2. Factor Loadings for Coping Items in Response to Race-Based Victimization .............. 68 Table 3. Mean Differences in Victimization and Emotional Impact of Victimization by Gender and Race ........................................................................................................................................ 70 Table 4. Mean Differences in Victimization and Emotional Impact of Victimization by Gender and Race ........................................................................................................................................ 71 Table 5. Mean Differences in Use of Coping Strategies for Race-Based Victimization of Marginalized Identities by Gender................................................................................................ 73 Table 6a. Emotional Impact of Sex and Race-Based Victimization Associated with Coping Strategies ....................................................................................................................................... 73 Table 6b. Emotional Impact of Sex-Based Victimization of Women Associated with Coping Strategies and Race ....................................................................................................................... 76 Table 7. Emotional Impact of Race-Based Victimization of Participants of Color Associated with Coping Strategies and Gender ...................................................................................................... 78 Table 8. Factor Loadings for Victimization Items by Subtype ..................................................... 79 Table 9. Correlations between Subtypes of Victimization ........................................................... 80 Table 10. Frequency of Victimization in the Past Year across Subtypes ..................................... 81 Table 11. Mean Differences in Emotional Impact of Victimization by Subtype ......................... 82 Table 12. Differences in Coping Strategies Used across Victimization Subtypes ....................... 83 Table 13. Emotional Impact of Victimization Associated with Coping Strategies ...................... 84 vi LIST OF FIGURES Figure 1. Emotional Impact of Race-Based Victimization of Participants of Color Associated with (1a) Support-Seeking Strategies and Gender and (1b) Reporting Strategies and Gender…………………………………………………………………………....……….……86 vii INTRODUCTION Cyberbullying refers to any intentional, repeated act of aggression perpetrated through an electronic medium with the intention to cause psychological harm or humiliation to a person who cannot easily defend him or herself (Kowalski, Limber, & Agatston, 2012; Patchen & Hinduja, 2012; Slonje & Smith, 2008). Cyber victimization is common among youth, with prevalence rates estimated to be between 10-40% (Kowalski et al., 2014). It has been found to be associated with several negative mental health outcomes in the literature, including depression, anxiety, suicidality, alcohol and substance use, delinquency, and physical aggression among victims (Klomek et al., 2011; Mishna, McLuckie, & Saini, 2009; Wang, Nansel, & Iannotti, 2011; Wigderson and Lynch, 2013). Like traditional bullying, cyberbullying involves an imbalance of power between the perpetrator and victim (Nocentini et al., 2010; Slonje, Smith, & Frisen, 2013). Cyberbullying can take many different forms, including harassment (i.e., offensive messages sent to or about a target), flaming (i.e., online fights or arguments that include offensive or vulgar language, and may escalate to include threats), impersonation (i.e., posing as the victim or another individual in order to communicate negative or inappropriate information to or about the victim), cyber stalking (i.e., using electronic communication to stalk another person or send repeated threatening messages), and nonconsensual sexting (i.e., distributing sexually explicit photos or videos of another individual without the person’s permission) (Kowalski, Giumetti, Schroeder, & Lattanner, 2014; Willard, 2007). In addition to taking several different forms, cyberbullying can be perpetrated through various types of technology, such as cell phones, computers, or tablets, and it can also occur across diverse platforms, including email, text messaging, social 1 networking sites, web pages, online games, or other types of internet forums (Wang, Iannotti, & Nansel, 2009). Bias-Based Bullying Cyberbullying often focuses on individual factors that differentiate the victim from his or her peers, such as appearance or perceived popularity. It may also focus on social group membership of the victim. This latter form of victimization, known as bias-based victimization, refers to victimization that focuses on a socially stigmatized identity of the victim (e.g., race, sex, sexual orientation) (Smith, 2010). Traditional (or in-person) bias-based bullying has been associated with more severe mental health outcomes than non-bias based forms of in-person victimization. To date, only a handful of studies have examined bias-based forms of victimization in an online context. However, given evidence of an overlap between cyber and in- person bullying behavior (Juvonen, 2008), it is informative to first review the literature on bias- based bullying in a face-to-face context. Traditional bullying behavior includes physical aggression (i.e., pushing, shoving, or other forms of physical intimidation), verbal aggression (i.e., name-calling, teasing, or taunting in a harmful manner), and relational or social aggression (i.e., spreading rumors, socially excluding others, or intentionally damaging someone’s reputation) (Wang, Iannotti, & Nansel, et al., 2009). For example, decades of research point to gender differences in the nature of bullying behavior. Research indicates that women are more often targeted specifically for their gender or sexual behavior than men, and they are more likely than boys to be the targets of sexualized bullying, which can include sexual comments, jokes, gestures, looks, rumors, or inappropriate physical contact or flashing (Hand & Sanchez, 2000). Nonadherence to traditional gender roles also appears to increase the risk for victimization among girls, and women or girls who stand out 2 as ‘too sexual’ or ‘too masculine,’ are more likely to be targeted by peers (Payne, 2010; Robinson, 2005). Payne (2010), through detailed qualitative interviews with adolescent and emerging adult lesbian women, found that as a result, the women frequently internalized negative perceptions of sexually expressive women, and began to restrict their own behavior as well as regulate that of other women around them. There is also considerable evidence pointing to race and ethnicity as targets for bias- based in-person bullying. Although more research is needed, racial and ethnic identity has also been found to alter the risk for peer victimization, as victimization experienced by racial minority individuals often focuses on issues of racial or ethnic identity (Card, Isaacs, & Hodges, 2007). In their study of 620 adolescents, for example, Monks, Ortega-Ruiz, and Rodriguez- Hidalgo (2008) found that among the students surveyed, those from marginalized groups were more likely to report being targeted or socially excluded due to their race or cultural background. In their study of 33 pairs of children aged 11 through 15, Moran et al. (1993) found that Asian children were significantly more likely to experience bias-based bullying, particularly racist name calling, primarily from their White peers. Bias-based Victimization in an Online Context To date, there is a relative dearth of empirical research examining cyberbullying in relation to gender and race. Emerging evidence does suggest, however, that ethnic and racial minority individuals may be at a higher risk for experiencing cyberbullying victimization, with marginalized groups reporting significantly higher levels of victimization for their race or ethnicity than those who identify as White (Pew Research Center, 2017). Gender may also heighten the risk for online victimization. In a recent qualitative study of 265 men and women, women were more likely than men to be targeted online based on topics related to their sexual 3 activity (Brody & Vangelisti, 2017). Similarly, they were more likely than men to experience more severe forms of online harassment, including sexual harassment and cyber stalking in a recent survey of 4,428 adults (Pew Research Center, 2017). Studies have also found interaction effects between race and gender in an online context, with women of color appearing to be at a particularly heightened risk for victimization (Felmlee, Rodis, and Fransisco, 2018). Felmlee et al. (2018) found that women of color were targeted for both their racial and gender identities, and stereotypes about these groups were perpetuated. Despite the obvious similarities between these findings and those for traditional bullying, there are good reasons to suspect that cyberbullying victimization may also be distinct from traditional bullying victimization in important ways. Most notably, the ability of bullies to remain anonymous online is a key characteristic that distinguishes cyberbullying from traditional bullying, and one that could lead to increased rates of bias-based forms of cyberbullying in particular. Namely, there is evidence that anonymity may be a common and preferred method of perpetration for bullies online (Dehue, 2008). Anonymity may lead individuals to feel increased levels of deindividuation, or a loss of self-awareness and a sense of diffused responsibility (Postmes, Spears, & Lea, 1998). When this awareness of the personal identity of the self and others decreases, group distinctions become more salient, leading to increases in the influence of group identities, stereotypes, and discrimination (Postmes, Spears, & Lea, 1998). Relatedly, anonymity also appears to free aggressors from norms and social constraints that may place a limit on their behavior, which may result in more aggressive and harmful behavior than could feasibly be carried out face-to-face (Patchin & Hinduja, 2006). This phenomenon of feeling less restrained online is known as online disinhibition, and it is associated with increased verbal attacks, harassment, and incitement of violence (Joinson, 2007). Typically, individuals who 4 engage in these behaviors online would not exhibit them in a real-world environment (Joinson, 2007). Anonymity may allow individuals to separate their online behavior from their “real” identity and diminish responsibility for their actions (Suler, 2004). Thus, anonymity could be an important factor leading to an increase in gendered and racialized bullying behavior online relative to traditional contexts. Given all this, we cannot assume that bias-based cyberbullying is simply a different venue for bias-based bullying in traditional contexts. In addition to influencing the frequency of bias-based bullying perpetration, studies have also demonstrated that anonymity increases the level of fear for the victims, as potentially anyone could be the perpetrator, including peers, friends, or other trusted individuals (Badiuk, 2006; Mishna et al., 2009). Anonymity is thus associated with a high level of distress for cyberbullying victims. Victims reported that being unaware of who was perpetrating against them caused them to feel increased levels of frustration and insecurity (Sticca & Perren, 2013; Vandebosch & Van Cleemput, 2008), and increased feelings of humiliation, powerlessness, and hopelessness. Furthermore, victims often worry that “hiding behind the keyboard” protects bullies from being caught or facing consequences for their actions, resulting in a reluctance to report the victimization to an adult (Mishna et al., 2009). Intersectionality Theory A major limitation of the current literature examining bullying (both traditional and cyber) is a lack of attention paid to the way various social identities interact to affect experiences. This Intersectional approach refers to the ways in which social categories (i.e., race, class, gender, sexual orientation, age, religion, ability status, etc.) exist simultaneously, interacting and leading to social inequality (Cole, 2009; Crenshaw, 1989). This term originated in Black feminist literature and was coined by Crenshaw (1989), a scholar in the field of critical 5 race theory, as a way to help explain the oppression of Black women. Specifically, Crenshaw argued that Black women were often excluded from feminist theory and antiracist policy discourse, as both are based on discrete sets of experiences that fail to reflect that complex interaction between race and gender. In order to sufficiently address the unique experiences of Black women, Crenshaw argued that feminist theory and antiracist discourse needed to be reevaluated under an intersectional framework since traditional approaches fail to capture the individual experiences of systems of privilege and oppression (i.e., racism, classism, sexism, etc.) (Crenshaw, 1989). The primary insight of intersectionality theory is thus that social categories interact at the individual (i.e., micro) level of experience to reflect multiple interwoven systems of privilege and oppression at the societal or structural (i.e., macro) level. Thus, the intersectional approach differs from traditional unitary approaches to research that tend to focus on a single social category of an individual. Intersectional approaches have begun to receive significant attention in psychology and related fields to better explain phenomena such as health disparities, ethnic and racial discrimination, psychological distress, and stereotyping (Galinsky, Hall, & Cuddy, 2013; Kelly, 2009; Thomas, Witherspoom, & Speight, 2008), but they remain notably underused in bullying research. Indeed, the majority of studies of bias-based bullying focus on a single identity of the victim, such as gender or race (e.g., reporting prevalence rates between different social groups within their samples, but failing to examine the intersection of these various identities). As a consequence, the experiences of individuals who may be at unique bullying risk (e.g., young women of color) are rendered invisible. What’s more, intersections of social identities may play an important role in how youth experience victimization. For instance, the literature suggests that women are more likely than 6 men to experience bullying that is gendered or sexualized in nature, and although not examined extensively in the reviewed studies, women of color may be at a particular increased risk for this type of victimization compared to other groups). The theory of double jeopardy would assert that women of color are at an increased risk for maltreatment given their subordinate status in both gender and race. In addition, sexual stereotypes of Black women as hypersexual or promiscuous were used historically to justify sexual exploitation of Black women during slavery (Collins, 2002), and they continue to exist today in many popular media representations (Wilcox, 2005). Black women’s membership in multiple marginalized categories, in combination with stereotypes depicting them in a sexualized nature, would predict an increased risk of experiencing sexualized forms of verbal bullying by peers (Buchanan, Settles, & Woods, 2008). In support of this prediction, Buchanan et al. (2008) found that among 7,714 Black and White female military personnel, Black women were more likely than White women to report experiencing more severe forms of sexual harassment, including unwanted sexual attention and sexual coercion. Responses to Victimization Although individuals who are targeted by their peers are at risk for a variety of negative outcomes, there is evidence to suggest there are individual differences in the degree of risk. The use of coping strategies may be one factor that explains why some youth appear to be more resilient to experiences of victimization than others (Kochenderfer-Ladd & Skinner, 2002). The stress and coping paradigm (Roth & Cohen, 1986; Causey & Dubow, 1992) suggest that there are two major types of coping strategies individuals engage in to respond to stressful situations: approach coping and avoidance coping. Approach strategies are direct attempts to alter the stressful situation, and they may include behaviors such as problem solving or support seeking. 7 Avoidance strategies, on the other hand, describe the ways in which individuals manage their negative reactions to stressful situations, without attempting to stop their stressors. In response to traditional bullying victimization, studies have demonstrated that approach coping strategies are generally more effective when the victimization is less frequent, as victims are likely to be more successful at changing their situation (Kochenderfer-Ladd & Skinner, 2002). There is evidence of gender and racial and ethnic differences in the use of particular coping styles. Adolescent girls, for instance, tend to use more approach strategies than boys in response to traditional bullying and peer-related stress (Hunter & Boyle, 2004; Seiffge- Krenke, 2011). Hunter and Boyle (2004), for example, surveyed 830 children aged 9 through 14, and analyses revealed that in response to bullying, girls preferred strategies including social support seeking and problem solving. Adolescents of color have also been found to use more social support in the face of general stress than White adolescents, and this particularly true for female adolescents of color (Chapman & Mullis, 2000). In their study of 361 adolescents, Chapman and Mullis (2000) found that female adolescents surveyed were more likely to turn to a friend in response to stress than White adolescents, in addition to sharing feelings and seeking spiritual support. Adolescent boys, and especially White adolescent boys, have been shown to preferentially engage in avoidant strategies, such as retaliation or denial in response to traditional and cyber victimization (Machmutow et al., 2012; Seiffge-Krenke, 2011). The literature examining coping strategies in response to cyber victimization in particular remains limited, although the studies that have been done suggest that youth use a variety of approach and avoidance strategies in response to this type of victimization. Adolescents may cope with victimization by deleting their web pages, staying offline, reporting the incident to a teacher or adult, and seeking support from a friend (Hinduja and Patchin, 2007). 8 Asking the perpetrator to stop, seeking revenge, and ignoring the situation were also commonly used strategies in a study by Smith et al. (2008). Among studies examining strategies used among adults, Schenk & Fremouw (2012) found that college students coped with victimization by telling another person, withdrawing from peers and social situations, and seeking revenge. Na, Dancy, & Park (2015) found that college students used both approach and avoidance strategies, and avoidance strategies were associated with increased rates of depressive symptoms. Students who engaged in avoidance coping were found to have experienced increased rates of cyber victimization and overall maladjustment in a recent study of undergraduates (Wick et al., 2020). In addition, avoidance coping, including increased rates of substance use, was associated with poor health outcomes among college students, including current smoking and binge drinking (Darabos et al., 2020). Of note, another important factor that may affect victims’ use of coping strategies is perceived effectiveness of the strategy. Research suggests that teachers and schools often fail to effectively respond to instances of bias-based cyberbullying, either trivializing the incident, ignoring it, or blaming the victim (Anagnostopoulos et al., 2009; Chambers et al., 2004; Mishna et al., 2018; Stein, 1995). This behavior from teachers creates and maintains a power imbalance in the classroom environment, increasing the likelihood that gendered bullying occurs. These responses may influence victims’ preferred coping strategies, as they may learn that teachers and other adult figures will not effectively intervene or will hold up existing power dynamics (Vreeman & Carroll, 2007). Intervention programs focused on the broader school environment (i.e., increased supervision and parent involvement, increased cooperation among teachers and counselors) have demonstrated greater effectiveness in decreasing victimization (Farrington, 2011; Vreeman & Carroll, 2007). 9 Current Studies Despite evidence that bias-based forms of victimization are particularly harmful for victims, and separate evidence that online anonymity increases the victim’s distress, there is limited research examining the extent to which bias-based victimization is experienced online. Similarly, little research has examined how victims may cope with experiences of online bias- based victimization. The current studies aim to address these gaps in the literature, examining the prevalence, psychological impacts, and coping strategies of race and sex-based forms of cyber victimization. In doing so, the second of the two studies will also take an explicitly intersectional approach, examining how various key social identities interact to affect the risk and experience of victimization. 10 STUDY 1 Cyber victimization refers intentional acts of aggression perpetrated through an electronic medium with the intent to cause harm or humiliation to the victim (Kowalski, Limber, & Agatston, 2012; Patchin & Hinduja, 2012; Slonje & Smith, 2008). Current estimates of the prevalence rates of cyber victimization among students are typically between 10-40% (Kowalski et al., 2014), ranging as high as 55% in one sample of university students (Dilmac, 2009). Its association with negative mental health outcomes is well-documented in the literature, with consistently observed links with depression, anxiety, suicidality, alcohol and substance use, delinquency, and physical aggression among victims (Klomek et al., 2011; Mishna, McLuckie, & Saini, 2009; Wang, Nansel, & Iannotti, 2011; Wigderson and Lynch, 2013). To date, however, the majority of cyber victimization literature focuses on school-age and adolescent youth, and there remains a relative lack of research examining victimization in emerging and young adult populations. Cyberbullying victimization often focuses on individual factors that differentiate the victim from his or her peers, including appearance, perceived popularity, and notably, the social group memberships of the victim. This latter form of victimization is known as bias-based victimization (Smith, 2011). Bias-based victimization refers to victimization that focuses on a socially stigmatized identity of the victim (e.g., race, sex, sexual orientation), and it has been associated with poorer mental health outcomes than non-bias based forms of victimization (Russell et al., 2012; Walton, 2018). To date, however, nearly all studies examining bias-based victimization have focused on in-person forms of aggression, with very few examining bias- based forms of cyber aggression. 11 Bias-based Victimization in a Traditional Context Traditional bullying behavior includes physical aggression (i.e., pushing, shoving, or other forms of physical intimidation), verbal aggression (i.e., name-calling, teasing, or taunting in a harmful manner), and relational or social aggression (i.e., spreading rumors, socially excluding others, or intentionally damaging someone’s reputation) (Wang, Iannotti, & Nansel, et al., 2009). Although findings are sometimes mixed, a vast body of literature demonstrates gender1 differences in the nature of bullying behavior. For instance, data consistently indicate that, relative to men, women are more often targeted specifically for their gender or sexual behavior. Girls are also more likely than boys to be the targets of sexualized bullying, referring to any unwanted sexual attention that makes the victim feel uncomfortable, unsafe, or humiliated (Hand & Sanchez, 2000; Mishna et al., 2018; Renold, 2002). These behaviors can include sexual comments, jokes, gestures, looks, rumors, or inappropriate physical contact or flashing (Hand & Sanchez, 2000). Girls who do not conform to traditional gender roles also appear to be at an especially high risk for victimization by peers (Payne, 2010; Robinson, 2005). Girls who stand out as ‘too sexual’ or ‘too masculine,’ for instance, are perceived as violating gender norms and 1 Although often used interchangeably in the literature, it is important to distinguish between the concepts of gender and sex. Sex refers to the biological and physiological traits that differentiate men and women, while gender refers to the socially constructed roles, behaviors, and identities associated with an individual’s biological sex (Johnson, Greaves, & Repta, 2009). The term gender is used in the current study to summarize differences in experiences of victimization between males and females in order to take social and cultural context into consideration. However, we refer to our measure of victimization as ‘sex-based victimization’ in order to remain consistent with the extant literature. Sex-based bullying or harassment broadly includes three categories of unwanted aggressive behavior: gendered bullying, which refers to behavior that maintains and asserts dominant norms of masculinity and femininity, sexualized bullying, or bullying that is sexual in nature, including unwanted sexual attention that makes the target feel uncomfortable, unsafe, or humiliated, and sexual coercion, which includes bribing or threatening a victim for sexual favors. In order not to limit our measure to only the first category of victimization, we retained the term ‘sex-based victimization’ in our study (Leskinen, Cortina, & Kabat, 2010; Mishna et al., 2018). 12 social order, and are more likely to be targeted or excluded by peers (Payne, 2010; Robinson, 2005). Racial and ethnic identities have also been found to alter the risk for peer victimization (Card, Isaacs, & Hodges, 2007), although research in this area remains quite scarce. Victimization experienced by racial minority individuals often focuses on issues of race. Mendez (2016), for example, found that 10% of youth were targeted specifically for their race, and Black students were more likely to be targeted than other racial groups. Moran et al. (1992) found that Asian children who had experienced bullying were most likely bullied through racial name- calling, and this was largely carried out by their White peers. Finally, when Monks, Ortega-Ruiz, and Rodriguez-Hidalgo (2008) distinguished between traditional bullying and bullying that was biased in nature, they found that students from marginalized groups were more likely to be targeted and socially excluded due to their race or cultural background. Bias-based Victimization in an Online Context To date, there is a paucity of empirical research examining cyber bullying in regards to gender and race. However, emerging evidence suggests that women may be more likely than men to be targeted online based on topics related to their sexual activity, and they may be more likely than men to experience more severe forms of online harassment, including sexual harassment and cyber stalking (Brody & Vangelisti, 2017; Pew Research Center, 2017). As one example, in their recent qualitative study, Brody and Vangeliststi (2017) asked 265 men and women about the cyberbullying experiences of people they know. Participants recalled significantly more experiences of women being victimized based on their sexual activity than men, with nearly 10% of the sample reported having observed this type of victimization (Brody & Vangelisti, 2017). In this same study, 21% of women ages 19 to 29 reported having been 13 harassed online due to their gender, compared to only 5% of the men (Pew Research Center, 2017). Recent studies also suggest that ethnic and racial minority individuals may be at a higher risk for experiencing cyberbullying victimization, with those identifying as Black or Hispanic reporting significantly higher levels of victimization for their race or ethnicity than those who identify as White (25%, 10%, and 3%, respectively) (Pew Research Center, 2017). Moreover, studies have found interaction effects between race and gender in an online context. Felmlee, Rodis, and Fransisco (2018) found that women of color were targeted online for both their racial and gender identities, and harmful gender and racial stereotypes about these groups were reinforced. Black adolescent females may also be at an increased risk for online sexual harassment, including receiving requests for sexual pictures, relative to their White female and Black and Hispanic male peers (Mitchell & Wolak, 2007; Tynes & Mitchell, 2013). Despite these similarities with findings for traditional bullying, there are good reasons to suspect that cyberbullying victimization may also be distinct from traditional bullying victimization in key ways. Most notably, the ability of bullies to remain anonymous online is a key characteristic that distinguishes cyberbullying from traditional bullying and could lead to increased rates of bias-based forms of cyberbullying in particular. Indeed, there is evidence that anonymity may be a common and preferred feature of bullies online (Dehue, 2008). Anonymity may lead individuals to feel increased levels of deindividuation, or a loss of self-awareness and a sense of diffused responsibility (Postmes, Spears, & Lea, 1998). When this awareness of the personal identity of the self and others decreases, group distinctions become more salient, leading to increases in the influence of group identities, stereotypes, and discrimination (Postmes, Spears, & Lea, 1998). Similarly, anonymity may free aggressors from norms and 14 social constraints that may place a limit on their behavior, which may result in more aggressive and harmful behavior than would be carried out face-to-face (Patchin & Hinduja, 2006). Thus, anonymity could be an important factor leading to an increase in gendered and racialized bullying behavior online relative to traditional contexts. Given all this, we cannot assume that bias-based cyberbullying is simply a different venue for bias-based bullying in traditional contexts. In addition to influencing the frequency of cyberbullying perpetration, studies have also demonstrated that anonymity increases the level of fear for the victims, as potentially anyone could be the perpetrator, including peers, friends, or other trusted individuals (Badiuk, 2006; Mishna et al., 2009). Anonymity is thus associated with a high level of distress for cyberbullying victims. Victims reported that being unaware of who was perpetrating against them caused them to feel increased levels of frustration and insecurity (Sticca & Perren, 2013; Vandebosch & Van Cleemput, 2008). Perpetrator anonymity may also be associated with feelings of humiliation, powerlessness, and hopelessness for victims. Sticca & Perren (2013), for instance, found that adolescents perceived anonymous cyberbullying to be more distressing (i.e., more humiliating and threatening) than traditional bullying and non-anonymous forms of cyberbullying. Furthermore, victims often worry that “hiding behind the keyboard” protects bullies from being caught or facing consequences for their actions, resulting in a reluctance to report the victimization to an adult (Mishna et al., 2009). Responses to Victimization Although individuals who are targeted by their peers are at risk for a variety of negative outcomes, there is evidence to suggest that some youth appear to be more resilient to experiences of victimization than others (Kochenderfer-Ladd & Skinner, 2002). Individual differences in the 15 use of particular coping strategies may be one important factor to explain why some victims are at a greater risk for maladjustment in response to stressful peer interactions (Kochenderfer-Ladd & Skinner, 2002). The stress and coping paradigm (Roth & Cohen, 1986; Causey & Dubow, 1992) suggests that there are two major types of coping strategies individuals engage in to respond to stressful situations: approach coping and avoidance coping. Approach strategies are direct attempts to alter the stressful situation, and they may include behaviors such as problem- solving or support-seeking. Avoidance strategies, on the other hand, describe the ways in which individuals manage their negative reactions to stressful situations, either without attempting to stop their stressors or responding in an impulsive or maladaptive manner. Examples of avoidance strategies may include behaviors such as refusing to think about the stressful situation, taking their emotions out on others, seeking retaliation, or blaming themselves for the experience (Kochenderfer-Ladd & Skinner, 2002). There is evidence of gender and racial and ethnic differences in the use of particular coping styles. Adolescent girls, for instance, tend to use more approach strategies than boys in response to traditional bullying and peer-related stress, particularly social support-seeking and problem-solving strategies (Hunter & Boyle, 2004; Seiffge-Krenke, 2011). Adolescent boys, on the other hand, have been demonstrated to engage in avoidant strategies, such as retaliation or denial in response to traditional and cyber victimization (Machmutow et al., 2012; Seiffge- Krenke, 2011). Cowie (2000) found that among 1,385 middle school youth, 64% of boys reported telling no one about their experience of being bullied, compared to 36% of girls, with the majority of boys reporting having ignored the situation. In terms of ethnic and racial differences, adolescents of color have been found to use more social support in the face of general stress than White adolescents, and this particularly true for female adolescents of color, 16 although there is a lack of research examining ethnic and racial differences in coping strategies in response to bullying behavior in particular (Chapman & Mullis, 2000). In response to traditional bullying victimization, studies have demonstrated that approach coping strategies are generally more effective than avoidance strategies, although results vary. When the victimization is less frequent, approach strategies may be more useful, as victims are more likely to be successful at changing their situation (Kochenderfer-Ladd & Skinner, 2002). Support-seeking is one approach strategy that has been shown to decrease the negative effects of victimization, and this is particularly true among girls (Davidson & Demaray, 2007; Kochenderfer- Ladd & Skinner, 2002). Victims in one study of 4th-8th grade youth reported that social support helped them to brainstorm solutions to their problem, as well as to feel validated and cared for by trusted individuals (Tenebaum, Varjas, Meyers, & Parris, 2011). They also indicated that seeking support from a friend or family member was more helpful than reporting the incident to a teacher or other adult, as they did not trust that authority figures would believe them or would adequately address the situation. Mahady Wilton and Craig (2000) also found that assertiveness and problem-solving techniques were the most effective strategies enacted by children who were victimized, although these techniques were seldom used compared to avoidance strategies. Avoidance coping, on the other hand, is generally associated with higher rates of maladjustment, anxiety, and depression (Flanagan et al., 2013). Passive strategies, such as ignoring, may reinforce bullies’ motivations and lead to repeated victimization (Mahady, Wilton, & Craig, 2000). Retaliation or physical violence may lead to a reduction in physical bullying, although victims who endorsed using these strategies indicated that they may also lead to inconsistent and unpredictable consequences (Tenebaum et al., 2011). When victimization occurs frequently, however, avoidance strategies such as distancing oneself from their 17 perpetrator may have some adaptive benefits for managing the stressful impact of the experience (Kochenderfer-Ladd & Skinner, 2002). The literature examining coping strategies in response to cyber victimization remains limited, although the literature suggests that youth use a variety of approach and avoidance strategies in response to this type of victimization. Hinduja and Patchin (2007) found that adolescents coped with victimization by deleting their web pages, staying offline, reporting the incident to a teacher or adult, and seeking support from a friend. In another study of adolescents, asking the perpetrator to stop, seeking revenge, and ignoring the situation were also commonly used strategies (Smith et al., 2008). To date, there has been little research examining coping strategies used among emerging and young adults. Schenk & Fremouw (2012) found that college students coped with victimization by telling another person, withdrawing from peers and social situations, and seeking revenge. In another study, college students used both approach and avoidance strategies, and avoidance strategies were associated with increased rates of depressive symptoms (Na, Dancy, & Park, 2015). Current Study Despite evidence that bias-based forms of victimization are particularly harmful for victims, and separate evidence that online anonymity increases the victim’s distress, virtually no research has sought to establish the extent to which bias-based victimization is experienced online, particularly among emerging and young adults. Similarly, little research has examined how victims may cope with experiences of online bias-based victimization. The current study examined the prevalence of race and sex-based forms of cyber victimization, their psychological impacts, and the coping strategies used by victims. The following research questions were explored (specific hypotheses for each research question can be found in Table 1): 18 1) What percentage of emerging and young adults experience bias-based victimization online? a. Do these prevalences vary across various privileged and marginalized identities? b. Are women of color more likely to be the targets of sex-based victimization than White women? c. Do men and women of color differ in their risk for race-based victimization? 2) What are the emotional impacts of bias-based victimization? a. Does the emotional impact of victimization vary across privileged and marginalized identities? b. Does the emotional impact of sex-based victimization differ for women of color and White women? c. Does the emotional impact of race-based victimization differ for men and women of color? 3) How do victims respond to experiences of bias-based victimization? a. Does the use of coping strategies for sex-based victimization differ for women of color and White women? b. Does the use of coping strategies for race-based victimization differ for men and women of color? 4) Are commonly used coping strategies associated with the emotional impact of bias-based victimization? a. Does the association between coping strategies and emotional impact differ for White women and women of color? 19 b. Does the association between coping strategies and emotional impact differ for women and men of color? Methods Participants Participants included 808 college students from a large university in the Midwest. Participants were 62% female and 99.5% identified as cisgender. We included only the cisgender participants in the current study, as we were unable to run statistical analyses with our transgender sample due to sample size constraints. Participants had a mean age of 19.0 years. Seventy percent were freshmen or sophomores. Given our specific research questions, we enriched the sample for students of color. Among the full sample, 38% of participants identified as White (78% female), 23% Black (73% female), 24% Asian (46% female), 7% Hispanic (45% female), 2% Native American (75% female), and 6% other race/ethnicities (52% female). Sixty- four percent identified as single, and 96% did not have children. Eighty-two percent reported an income less than $10,000, and the median combined parental income fell between $100,000- $150,000. To ensure sufficient power to detect differences by race, we restricted the current analyses to Black, Asian, and White participants, the three largest racial groups in our sample. Descriptive data for the other groups can be found in Table 1A in Appendix C. Participants completed a series of questionnaires through the Psychology Department’s online subject pool (SONA). Participants signaled their consent by filling out and completing the questionnaires online. They were rewarded subject pool credit for their participation in the study. Quantitative Measures Focus of Victimization. Single items were used to assess the foci of victimization (i.e., race and sex-based victimization). Participants indicated the degree to which they had 20 experienced each form of victimization, (e.g., “Please indicate to what extent you have been victimized or targeted online based on your sex”). This item was rated for each form of victimization on a 4-point scale ranging from (0) Never, (1) Seldom, (2) Often, and (3) Very Often. Emotional Impact. A single item was used to assess the emotional severity of each form of victimization, respectively, (e.g., “If you indicated that you have been victimized based on your sex, to what extent did this experience bother you?”). This item was rated for each form of victimization on a 4-point scale ranging from (1) Mild- The experience bothered me a little, (2) Moderate- The experience bothered me quite a bit, (3) Severe- I had trouble eating, sleeping, or enjoying myself because of the experience, and (4), Very Severe- I felt unsafe or threatened because of the experience. Coping Strategies. Participants were asked to report whether or not they engaged in any of 12 distinct responses to each form of peer victimization, as well as the perceived effectiveness of each response, (e.g., “If you indicated you have been victimized online based on your sex, indicate to what extent these strategies have been helpful for you?”). These include the following approach coping strategies, adapted from the most commonly used strategies identified from previous cyberbullying research: “I told the person to stop,” “I told a friend,” “I told an adult at home,” “I told a teacher at school,” “I reported it to the authorities,” “I made a joke about it,” “I told him/her how I felt,” and “I deleted my social media account,” and the following avoidant coping strategies, in line with those identified in prior studies: “I ignored it,” “I stayed offline,” “I used alcohol or drugs,” and “I made plans to get back at him/her” (Hoff & Mitchell, 2009; Li; 2010; Machackova et al., 2013; Mishna et al., 2009; Smith et al., 2008; and Vollink et al., 2013). Responses for the coping items were rated on a 4 point scale ranging from (0) I did 21 not do this, (1) I did this and things got worse, (2) I did this and nothing changed, and (3) I did this and things got better. Coping style factor analyses. In order to determine the degree to which the coping strategies questionnaire appeared to assess the two expected domains of approach and avoidance coping, the dataset was split in half so that exploratory and confirmatory factor analyses could be run independently. Analyses were run separately for race-based and sex-based victimization. A maximum likelihood exploratory factor analysis with Direct Oblimin rotation was performed on the first subset of the data, allowing the factors to correlate with one another. Race-based victimization. Using eigenvalues greater than 1 and scree plot examination, results indicated that there were three factors underlying race-based victimization in the overall dataset. The 6 approach items loaded relatively cleanly onto the first factor, while the 4 avoidance items loaded onto the second factor. Two items loaded cleanly onto the third factor, which was labeled reporting. An exploratory factor analysis was then run separately by gender to examine whether the same factor structure emerged for men and women of color (presented in the top half of Table 2). Results indicated that a three factor structure once again emerged for men, while a four factor structure underlay the data for women. Two items loaded cleanly onto the first factor, which was labeled support-seeking. The second factor included the remaining 4 approach items, the third factor included the 4 avoidance items, and the fourth factor included the 2 reporting items. Overall, results from the exploratory factor analyses suggested that there were at least three factors underlying that data for race based victimization, and gender differences emerged relating to support-seeking strategies. A confirmatory factor analysis (CFA) was then conducted on the other half of sample to confirm the results of the exploratory factor analyses, separated by gender. The variance of the 22 factors was fixed at 1 so that each item’s factor loading could be freely estimated, and factors were allowed to correlate. Fit indices suggested acceptable model fit of the three-factor model for men (χ2(46) =100.89, TLI= .88, CFI= .91, RMSEA= .07, AIC=348.22). We also tested a two- factor model, in which the items were loaded onto the originally hypothesized approach and avoidance factors (χ2(41) =153.57, TLI= .83, CFI= .87, RMSEA= .11, AIC= 354.51). The three- factor model provided a superior fit to the data by all five fit indices, indicating that the reporting and approach factors were separable in these data. Fit indices also suggested acceptable model fit of the four -factor model for women (χ2(49) =98.72, TLI= .91, CFI= .92, RMSEA= .08, AIC=345.02). We also tested a three-factor model, in which the support seeking items were loaded onto the approach factor (χ2(46) =110.06, TLI= .87, CFI= .88, RMSEA= .09, AIC= 350.60). The four-factor model again provided a superior fit to the data, indicating that the support seeking and approach factors were distinct factors for women of color. The items for each model loaded onto their respective factors relatively well (see Table 2). Sex-based victimization. Exploratory factor analyses were also conducted for sex-based victimization. Results indicated that there were four factors in the overall dataset. As with race- based victimization, the two support-seeking items loaded relatively cleanly onto the first factor, the 4 approach items loaded onto the second factor, the 4 avoidance items loaded well onto the third factor, and the 2 reporting items loaded onto the fourth factor (see the bottom half of Table 2 for factor loadings). Analyses were then run separately by privileged and marginalized identities to examine the factor structures among White women and women of color. Results indicated that similar factor structures emerged among these groups. Overall, results from the exploratory factor analysis suggested there were four factors underlying coping styles for sex- 23 based victimization in the overall data, and these factors did not differ among privileged and marginalized identities. A confirmatory factor analysis (CFA) was then conducted on the other half of sample to confirm the results of the exploratory factor analysis. Fit indices suggested acceptable model fit of the four-factor model (χ2(49) =99.96, TLI= .92, CFI= .89, RMSEA= .08, AIC=320.52). We also tested a three-factor model, in which the support seeking items were loaded onto the approach factor (χ2(46) =174.62, TLI= .86, CFI= .85, RMSEA= .082, AIC= 332.68). The four- factor model provided a superior fit to the data by all five fit indices, indicating that the support seeking and approach factors were separable in these data. The items loaded onto their respective factors relatively well (see Table 2). Given these factor analytic results, the strategies were dichotomized according to whether or not the strategy was used and summed to create Approach, Avoidance, Support Seeking, and Reporting variables. Cronbach’s alpha was calculated to measure the internal consistency of the items. The avoidance strategy items demonstrated acceptable internal consistency reliabilities, with alphas of .64 and .69 for race and sex-based victimization, respectively. The approach strategy items also demonstrated acceptable internal consistency reliabilities, with alphas of .65 and .66 for race and sex-based victimization, respectively, as did the support seeking items, with alphas of .66 and .60. Finally, the reporting items demonstrated good internal consistency reliabilities, with alphas of .78 and .82 for race and sex-based victimization, respectively. Analyses To examine our first three research questions (i.e., What percentage of youth are targeted online based on their race or sex?; What are the emotional impacts of race-based and sex-based victimization?; How do youth respond to experiences of victimization?), we evaluated 24 differences in experiences of bias-based victimization across sex and race via t-tests. Bonferroni corrections were used to adjust for multiple comparisons, where P= .05 divided by the number of comparisons performed (Lee & Lee, 2018). Analyses specifically examined differences in sex- based victimization of women across racial group, and differences in race-based victimization of ethnic and racial minorities across gender. Moderated regression analyses were used to examine question 4 (i.e., Are commonly used coping strategies associated with the emotional impact of race-based and sex-based victimization?). We used statistical interaction terms to examine whether women of color experienced a greater emotional impact of sex-based cyber victimization than White women, and whether there were differences between men and women of color in the emotional impact resulting from race-based cyber victimization. Race was dichotomized such that participants who identified as White received a code of 0 while participants with marginalized identities received a code of 1. We were sufficiently powered to examine differences between White and Asian identities, and White and Black identities. Gender was coded similarly, with men receiving a code of 0 and women receiving a code of 1. Models were run separately to examine both race- and sex-based victimization. Results RQ1. Prevalence of Bias-Based Victimization Sex-based victimization. 46.5% of participants reported being victimized based on their gender. As we hypothesized, women were significantly more likely to experience this form of victimization than men (t(710.16)= 9.43, p <.01; see Table 3). Nearly 54.2% of women reporting having been victimized based on their gender, compared to 23.4% of men. There were no 25 significant differences between White women and women of color in rates of sex-based victimization. Race-based victimization. Approximately 41% of participants reported being victimized based on their race. As expected, participants with marginalized identities were significantly more likely than White participants to experience race-based victimization (t(214.44)= 9.04, p <.01 for Black participants; t(265.01)= 8.60, p <.01 for Asian participants; see Table 3). Approximately 60.2% of participants with marginalized identities reported having been victimized based on their race, compared to 20.1% of White participants. There were no significant differences between men and women of color in their risk for race-based victimization. RQ2. Emotional Impacts of Victimization Sex-based victimization. Participants rated the severity of the emotional impact of sex- based victimization as mild to moderate, on average (M=1.50). There were no significant differences between men and women in severity of emotional impact experienced (see Table 3). We also examined whether White and marginalized women differed in their emotional experience of sex-based victimization. Significant differences emerged between White women and Asian women (t(56.48)= -2.19, p =.03), with the latter group experiencing a higher severity of emotional impact than White women. Race-based victimization. As with sex-based victimization, participants rated the severity of the emotional impact of race-based victimization as mild-to-moderate (M=1.47). We also examined whether the emotional impact of victimization differed between privileged and marginalized groups (see Table 3). Relative to White participants, we found that Black participants reported experiencing a higher emotional impact of race-based victimization 26 (t(124.91)= 4.38, p <.01). The severity of the emotional impact did not significantly differ for men and women of color. RQ3. Responses to Victimization Sex-based victimization. We next examined differences between marginalized and White women in their use of coping strategies in response to sex-based victimization (see Table 4). Approach strategies. No significant differences emerged between White and marginalized women for “I told the person to stop,” and women appeared to use this strategy about half of the time. White women were more likely than Black (t(133.86)= 3.5, p <.01) and Asian women (t(144.27)= 3.09, p <.01) to “make a joke about it.” Privileged and marginalized women did not differ significantly in “telling him/her how I felt,” with both groups using this strategy about 25% of the time. Lastly, White women were significantly less likely to “delete my social media” than marginalized groups (t(295.98)= -5.04, p <.01), using this strategy at lower rates than Black (t(87.16)= -3.25, p <.01) and Asian (t(91.43)= -4.29 p <.01) women. Support-seeking strategies. No significant differences emerged between privileged and marginalized women in their use of “I told a friend” or “I told an adult at home.” Telling a friend was a highly preferred coping strategy among women, with most women using it more than half of the time. Reporting strategies. In general, women used low rates of reporting strategies. More specifically, however, White women were more likely than Black (t(121)= 2.50, p <.01) women to report the event to the authorities. Asian women, on the other hand, used reporting strategies at higher rates than the other groups. They were significantly more likely than White women to 27 report victimization to a teacher (t(90.60)= 4.03, p <.01) or to the authorities (t(87.80)= 3.46, p <.01). Avoidance strategies. There were no significant differences between marginalized and White women in their use of “I ignored it,” and most women used this strategy at relatively high rates. Marginalized women “made plans to get back at him/her” more often than White women (t(300.11)= 6.0, p <.01), including Black (t(91.27)= 3.07, p <.01) and Asian women (t(94.72)= 4.91, p <.01). Marginalized and White women did not differ overall in their reported use of “using alcohol or drugs,” with all groups using this strategy at moderate levels. Lastly, there were no significant differences between White and marginalized women in their use of “I stayed offline.” Race-based victimization. We also examined differences between men and women of color in their use of coping strategies in response to race-based victimization. Table 5 breaks down gender differences for each individual racial/ethnic identity. Approach strategies. Men of color were more likely than women of color to delete their social media accounts in response to race-based victimization (t(216.84)= 2.11, p =.04). No other significant gender differences emerged in the use of approach strategies, and all approach strategies were used at moderate levels. Support-seeking strategies. Women were more likely than men to “tell a friend” and “tell an adult at home” about experiences of race-based victimization (t(228.35)= 4.61, p <01; t(244.96)= 2.64, p <01, respectively). Asian women in particular were more likely to tell a friend than men (t(97.97)= 2.83, p <01), while Black women were more likely than Black men to tell an adult at home (t(58.93)= 2.83, p <01). 28 Reporting strategies. No significant differences emerged between men and women of color in the use of reporting strategies. “Telling a teacher” was used between 20-25% of the time, while “reporting to the authorities” was used a slightly lower levels. Avoidance strategies. Men and women of color used “ignoring” at relatively high levels. Both men and women “made plans to get back at him/her” nearly half of the time, although gender differences were not significant). Participants reported moderate rates of “I used alcohol or drugs” and “I stayed offline,” and no significant gender differences emerged. RQ4. Associations between Coping Strategies and Emotional Impact of Victimization Sex-based victimization. For our final objective, we examined whether the strategies that victims use to cope with bias-based aggression were associated with the severity of the emotional impact experienced. In our first regression analysis, we assessed how well the four types of coping strategies predicted the level of emotional impact (i.e., mild, moderate, severe, and very severe) of sex-based victimization (see Table 6a). As predicted, approach strategies were associated with a lower emotional impact of victimization (β= -.13, p<.05), as were support-seeking strategies (β= -.22, p<.01). Avoidance strategies were not significantly associated with emotional impact. To assess whether the associations between coping strategies and emotional impact differed between White women and women of color, we ran moderated regression analyses examining differences between White and Black identities, and White and Asian identities (the two most common marginalized identities in our sample; see Table 6b). In our model examining differences between White and Black identities, we found main effects for support-seeking strategies (β= -.21, p<.05), reporting strategies (β= .20, p<.05), and race, such that Black women reported a higher emotional impact than White women (β= .31, p<.05). However, race did not 29 moderate the relationships between coping strategies and emotional impact. Similar results emerged in our model comparing White and Asian identities, with main effects for support- seeking strategies (β= -.19, p<.05), reporting strategies (β= .13, p<.05), and race, with Asian women experiencing a higher severity of emotional impact than White women (β= .21, p<.05). As with our first model, race did not moderate the relationships between coping strategies and emotional impact as we had predicted. Race-based victimization. We also examined how well the four types of coping strategies predicted the level of emotional impact of race-based victimization (see Table 7). Contrary to our predictions, neither approach nor avoidance strategies were associated with the severity of emotional impact. As with sex-based victimization, reporting strategies were associated with a higher level of emotional impact of race-based victimization (β= .27, p<.05), while support-seeking strategies were associated with a lower emotional impact (β= -.15, p<.05). We then ran regression analyses to assess whether the association between coping strategies and emotional impact differed for men and women of color (see Table 6b). We found main effects for each of the coping strategies, such that approach and support-seeking strategies were associated with lower levels of emotional impact, as expected (β= -.18, p<.05; β= -.21, p<.05, respectively), while reporting and avoidance strategies were associated with a higher level of emotional impact (β= .33, p<.05; β= .16, p<.05, respectively). We also found a main effect of gender, such that women of color experienced a higher emotional impact of race-based victimization than men of color (β= .26, p<.05). Significant interactions were also found for support-seeking strategies and gender (β= .26, p<.05), and reporting strategies and gender (β= -.29, p<.05). Interaction terms were further analyzed by conducting simple slopes analyses to test the relationships between coping strategies 30 and emotional impact as a function of gender. Results revealed that the relationship between support-seeking strategies and emotional impact was negative and significant for women of color (β= .30, p<.01), but this relationship was not significant for men of color (see Figure 1a). The association between reporting strategies and emotional impact was positive and significant for men of color (β= .36, p<.01 but was not significant for women of color (see Figure 1b). Discussion The first goal of the present study was to examine the prevalence of race and sex-based cyber victimization in a sample of college students. Nearly half of participants endorsed experiencing sex-based victimization (46.5%), while 41% reported experiencing race-based victimization. As hypothesized, women were more likely than men to experience sex-based victimization, while participants with marginalized identities were more likely than White participants to experience race-based victimization. These findings are consistent with prior studies demonstrating that gender and race may alter the risk for traditional forms of peer victimization, and they add to the emerging work on cyber victimization reporting similar results (Brody & Vangelisti, 2017; Card, Felmlee, Rodis, & Fransisco, 2018; Isaacs, & Hodges, 2007; O’Shaughnessy et al., 2004; Payne, 2010; Robinson, 2005; Monks et al., 2008). However, we did not find differences between White women and women of color in rates of sex-based victimization, nor did we find differences between men and women of color in rates of race- based victimization. Thus, while gender and race were associated with heightened risk for experiencing victimization in the present study, the intersection between these identities did not appear to play a significant role in the experience of victimization in one domain. The second goal of the current study was to examine the emotional impacts of bias-based victimization. On average, participants rated the severity of the emotional impact of sex and 31 raced-based victimization as mild-to moderate, supporting the findings of recent studies demonstrating positive associations between cyber victimization and distress (Kowalski et al., 2014; Mishna et al., 2009; Sticca & Perren, 2013). Importantly, however, we did not examine multiple adjustment outcomes, which may have obscured potential gender differences. While gender differences did not emerge in our study, we did find significant racial and ethnic differences, with Asian women experiencing higher emotional impacts of this form of victimization than White women. Similarly, for race-based victimization, men and women reported similar levels of emotional impact, although Black participants reported a higher emotional severity than White participants. These results suggest that individuals with marginalized racial and ethnic identities may experience bias-based victimization as more distressing than victims with privileged identities, regardless of gender or the specific type of victimization experienced. As a third goal for our study, we were interested in examining the coping strategies used in response to victimization. Gender and racial/ethnic differences emerged in the use of approach, support-seeking, reporting, and avoidance strategies. White women were more likely than marginalized women to make a joke about the situation, while they were less likely to delete their social media. White women were more likely than Black women to report the situation to the authorities, which supports prior literature indicating that women of color are more likely to feel reluctant reporting instances of harassment or violence due to societal history of not being believed about these experiences (Crenshaw, 1989). Women of all racial and ethnic groups used high levels of support seeking strategies, and women of color used these strategies at higher rates than men of color, supporting findings of prior studies demonstrating that social support is a preferred coping strategy among female youth in response to social stress (Seiffge-Krenke, 32 2011). Lastly, all participants used relatively high levels of ignoring, with no significant group differences emerging. Women in marginalized groups were more likely than White women to make plans to get back at their perpetrator. Furthermore, we did not find evidence of gender differences between men and women of color in the use of avoidance strategies, in contrast to prior findings demonstrating that male youth tend to engage in higher levels of avoidance in response to victimization than female youth (Machmutow et al., 2012; Seiffge-Krenke, 2011). As a final objective, we examined the associations between coping strategies and emotional impact of victimization. Approach and support-seeking strategies were associated with a lower emotional impact of sex-based victimization, as hypothesized, although race did not moderate these associations. For race-based victimization, approach strategies were associated with a lower emotional impact for all victims, and support-seeking strategies were associated with a lower emotional impact for women of color. These findings are consistent with prior literature documenting the effectiveness of approach and support-seeking strategies for decreasing the negative effects of victimization, with support-seeking strategies being particularly helpful for women (Davidson & Demaray, 2007; Kochenderfer-Ladd & Skinner, 2002). Reporting strategies were positively associated with emotional severity for women who experienced sex-based victimization, and for men of color who experienced race-based victimization. These findings could suggest that participants found reporting strategies to be ineffective for coping with victimization, or that victims turned to these strategies following especially distressing experiences. Avoidance strategies were associated with a more severe emotional impact of race-based victimization, as expected, but results were not significant for sex-based victimization. 33 Strengths and Limitations A few important limitations of the current study should be noted. First, our data was cross-sectional, meaning that causal inferences cannot be made among our variables. We used a Bonferroni correction to adjust for multiple comparisons in our t-test analyses, a rather conservative method to control the family-wise error rate. This may have decreased our power to detect true differences in the data. In addition, our data is based on a convenience sample of students, suggesting the sample may not be representative of the larger population of men and women who have experienced cyber victimization. Our results may be less generalizable to other age ranges, geographical regions, and those who do not have access to higher education. Another limitation of the present study was the use of labeling, rather than behaviorally- based items, to assess experiences of victimization, which may have affected participant responses and masked potential gender or racial/ethnic differences. Prior studies examining sexual and racial harassment have demonstrated that participants were more willing to report on experiences associated with harassment than directly label them using this terminology (Buchanan, Settles, Wu, & Hayashino, 2018; Stoll & Block, 2015). Future studies examining experiences of cyber victimization should use behavioral measures to improve reporting accuracy. We also used a single-item measure to assess the emotional impact of victimization, and a more robust measure would have improved our study. Furthermore, as stated above, we may have found gender differences related to outcomes of victimization (and in line with prior studies) if we had examined other outcomes in addition to emotional severity. Despite these limitations, the current study had several strengths. First, it is one of the first studies to our knowledge to examine how victims experience and cope with bias-based victimization online. The sample used in this study was racially and ethnically diverse, and it 34 fills important gaps in the bullying literature by studying this form of aggression using a college sample. This research provides a better understanding of the prevalence of bias-based cyber aggression, providing evidence that similar patterns of sex and race-based victimization that occur in traditional contexts also appear to occur online. Similarly, although we did not find gender differences in emotional impact in the current study, our findings revealed that race/ethnicity is an important factor that may alter the risk for experiencing negative outcomes of bias-based online victimization. Another strength of our study was that we conducted factor analyses on our coping questionnaire to confirm that the distinct coping styles hypothesized were indeed separable constructs. However, measures of coping in the bullying literature have not been tested extensively and do not have established reliability or validity. The four factors that emerged in the present study should be tested again in a separate diverse sample of young and emerging adults. Implications The results of our study provide evidence that students’ responses to cyber victimization are variable. Although we cannot make assumptions as to the direction of causation, it is possible that reporting strategies may not decrease the negative effects of victimization, although these are commonly recommended strategies in intervention programs. Consistent with findings from prior studies, victims may perceive that educators, authority figures, or other adults will not adequately address the situation, and they may turn to more maladaptive coping strategies (Mishna et al., 2018). It is important that educators are trained to recognize and respond to instances of bullying that feature bias as a motive and target this issue more directly. Approach strategies, by contrast, appeared to be effective for all victims in our study, and support-seeking strategies appeared to be particularly helpful for female victims. It is important that programs use 35 evidence-based, effective practices, and they should aim to highlight the strategies that may most benefit students in response to particular situations. Moreover, intervention programs should aim to provide an environment that decreases the responsibility placed on the victims, validates their experiences, and addresses perpetrators’ understanding of their behavior online. Furthermore, as there is evidence of bias-based bullying beginning as early as elementary school (Mishna et al., 2018), we would argue that prevention and intervention efforts should begin early, with training available in primary and secondary school settings. Finally, a major limitation of the current literature examining both traditional and cyber bullying is a lack of attention paid to the way various social identities interact to affect experiences. This intersectional approach refers to the ways in which social categories (i.e., race, class, gender, sexual orientation, age, religion, ability status, etc.) exist simultaneously, interacting and leading to social inequality (Cole, 2009; Crenshaw, 1989). Intersectional approaches have begun to receive growing attention in psychology and related fields, but they remain largely underused in bullying research. More research is needed that expands on the current work and takes an intersectional approach. As one means to achieve this, future studies should examine bias-based victimization that focuses on an individual’s multiple marginalized identities simultaneously (e.g., sexualized-racialized bullying) to better understand this unique type of risk. 36 STUDY 2 Cyberbullying refers to any intentional, repeated act of aggression perpetrated through an electronic medium with the intention to cause psychological harm or humiliation to a person who cannot easily defend him or herself (Kowalski, Limber, & Agatston, 2012; Patchin & Hinduja, 2012; Slonje & Smith, 2008). Cyberbullying often focuses on individual factors that differentiate the victim from his or her peers, such as appearance or perceived popularity. It may also focus on social group membership of the victim. This latter form of victimization, known as bias- based victimization, refers to victimization that focuses on a socially stigmatized identity of the victim (e.g., race, sex, sexual orientation) (Smith, 2011). Although bias-based victimization has been associated with more severe mental health outcomes than non-bias based forms of victimization, few studies have examined bias-based forms of victimization in an online context in particular. However, given evidence of an overlap between cyber and in-person bullying behavior (Juvonen, 2008), it is informative to jointly review the literature on bias-based bullying in a face-to-face context (Wang, Iannotti, & Nansel, et al., 2009). Victimization based on a single identity Racial and ethnic identity has been found to alter the risk for peer victimization (Card, Isaacs, & Hodges, 2007). Peskin, Tortolero, and Markham (2006) found that among a sample of 6th through 12th grade students, Black and Hispanic students reported being more likely than White students to be teased, made fun of, harassed, and physically bullied. Ethnic and racial minority individuals also appear to be at a higher risk for cyberbullying victimization, with those identifying as Black or Hispanic reporting significantly higher levels of victimization for their race or ethnicity than those who identify as White (25%, 10%, and 3%, respectively) (Pew Research Center, 2017). In their study using a sample of 10,245 urban youth, Goldweber, 37 Waasdorp, and Bradshaw (2013) similarly found that Black youth were more likely to be victims of cyberbullying than were students of other ethnicities. What’s more, the victimization experienced by racial minority individuals often tends to focus on issues of race. Mendez (2016), for example, found that 10% of youth were targeted specifically for their race, and Black students were more likely to be targeted than other racial groups. Moran et al. (1993) similarly found that Asian children who had experienced bullying were most likely bullied through racial name-calling, and this was largely carried out by their White peers. In addition, in the prior chapter, we found that among a sample of 808 college students, students of color were significantly more likely than White students to report being victimized for their race within the last year, with 60.2% reporting race-based victimization (Schires et al., 2020). By contrast, Angoff and Barnhart (2020) examined national data gathered from 13,567 youth in 144 schools and found that racial and ethnic minority youth were less likely to experience cyber victimization than their White counterparts. Although such results clearly differ from those reported above, we note that Angoff and Barnhart (2020) did not examine bias-based bullying per se. As indicated above, this distinction likely matters. Indeed, when Monks, Ortega- Ruiz, and Rodriguez-Hildago (2008) distinguished between traditional bullying and bias-based bullying, they found that students from marginalized groups were more likely to be targeted and socially excluded due to their race or cultural background. Moreover, we further note that Angoff and Barnhard (2017) also found that this protective effect did not emerge for those students who held both racial and ethnic minority and sexual minority statuses, for whom rates of cyberbullying victimization were higher. Data also consistently demonstrate gender differences in the nature of bullying victimization. A handful of studies have documented that girls are more likely than boys to be 38 the targets of sexualized bullying, which includes sexual comments, jokes, gestures, looks, rumors, or inappropriate physical contact or flashing (Hand & Sanchez, 2000). Verbal forms of sexualized bullying are the most commonly reported type, and females experience this form of bullying more frequently and more severely than do males (Lee et al.,1996; Meyer, 2008). In their qualitative study of 72 adolescents aged 14 to 15 years, girls in one focus group reported being targeted daily for their appearance or sexual reputation by male peers (Shute & Slee, 2008). Emerging evidence suggests that women are also more likely than men to be targeted online based on topics related to their sexual activity, and they may be more likely than men to experience more severe forms of online harassment, including sexual harassment and cyber stalking (Brody & Vangelisti, 2017; Pew Research Center, 2017). In their recent qualitative study, Brody and Vangeliststi (2017) asked 265 men and women about the cyberbullying experiences of people they know. Participants recalled significantly more experiences of women being victimized based on their sexual activity than men, with nearly 10% of the sample reported having observed this type of victimization (Brody & Vangelisti, 2017). In a survey of 4,428 adults, 21% of women ages 19 to 29 reported having been harassed online due to their gender, compared to only 5% of the men (Pew Research Center, 2017). In the prior chapter, we found that among 808 college students, over half reported being victimized online due to their gender, and women were significantly more likely than men to report this form of victimization (Schires et al., 2020). Finally, girls who do not conform to traditional gender roles appear to be at an especially high risk for victimization by peers (O’Shaughnessy et al., 2004). Girls who are perceived as violating gender norms and social order are more likely to be targeted or excluded by peers (Payne, 2010; Robinson, 2005). 39 Intersectionality Theory Intersectionality refers to the ways in which social categories (i.e., race, class, gender, sexual orientation, age, religion, ability status, etc.) exist simultaneously, interacting and leading to social inequality (Cole, 2009; Crenshaw, 1989). This term originated in Black feminist literature and was coined by Crenshaw (1989), a scholar in the field of critical race theory, as a way to help explain the oppression of Black women. Specifically, Crenshaw argued that Black women were often excluded from feminist theory and antiracist policy discourse, as both are based on discrete sets of experiences that fail to reflect that complex interaction between race and gender. Therefore, in order to sufficiently address the particular experiences of Black women, Crenshaw argued that feminist theory and antiracist discourse needed to be reevaluated under an intersectional framework. The primary premise of intersectionality theory is the notion that social categories interact at the individual (i.e., micro) level of experience to reflect multiple interwoven systems of privilege and oppression at the societal or structural (i.e., macro) level. Thus, the intersectional approach differs from traditional unitary approaches to research that tend to focus on a single social category of an individual, and it would argue that traditional approaches fail to capture the individual experiences of systems of privilege and oppression (i.e., racism, classism, sexism, etc.) (Crenshaw, 1989). Double jeopardy, which is frequently described in relation to intersectionality work (also referred to as multiple jeopardy), seeks to understand the experience of discrimination for people with multiple marginalized identities (Beale, 1979). The model proposes that membership in multiple marginalized groups will place individuals at an increased risk for negative experiences, such as health disparities or victimization. In addition, multiply marginalized individuals are also 40 at an increased risk of intersectional invisibility, which asserts that the experiences of non- prototypical members of stigmatized groups are rendered invisible (Purdie-Vaughns & Eibach, 2008). For instance, Black women do not fit the prototype of people of color (male) or women (White). Therefore, they are less likely to be recognized as members of their respective social groups, and their experiences are likely to be overlooked. The experiences of individuals from subordinate groups tend to be minimized or misrepresented historically, culturally, and politically, leading to further marginalization and disempowerment (Purdie-Vaughns & Eibach, 2008). The intersectional framework has become increasingly important for psychology, as it allows researchers the opportunity to understand the ways in which intersecting identities create nuanced experiences at individual and structural levels. This focus has helped shape the field’s understanding of key phenomena including health disparities, ethnic and racial discrimination, psychological distress, and stereotyping (Galinsky, Hall, & Cuddy, 2013; Thomas, Witherspoon, & Speight, 2008). For example, a recent study by Greene et al. (2020) using data from the 2015 National Survey on Drug Use and Health found that there were significant differences in rates of excessive alcohol use between Black and Hispanic sexual minority women and White heterosexual women, differences that were larger than what would be expected if differences between racial and gender identities were considered individually. Despite this increasing recognition, intersectionality remains largely unaddressed within the field of bullying research. However, a few recent studies have demonstrated that social identities may interact in ways that shape individuals’ experiences of bullying victimization as well. Stoll and Block (2015) examined 752 high school students and demonstrated that race moderated the effect of gender on cyber victimization, such that female students were more 41 likely than male students to report cyber victimization, and this association was stronger among White students than students of color. Race, however, did not moderate the relationship between students’ sexuality and experiences with cyberbullying. Despite evidence that membership in multiple marginalized social categories may place youth at an increased risk for cyberbullying victimization, limited research has examined cyber victimization that focuses specifically on the social identity of the victim (i.e., bias-based victimization). We know from the related literature examining gender and sexual harassment, however, that although the majority of the research focuses on gendered online harassment in isolation, intersectional perspectives are needed (Fox, 2015; Hackworth, 2018). Felmlee, Rodis, and Fransisco (2018) found an interaction between race and gender in their recent study of online harassment, such that among a sample of 24,000 tweets, messages containing stereotypes about women of color were easily accessible and particularly harmful, containing messages related to both the racial and gender identities of the women in their study. Moreover, individuals are likely to be targeted for their marginalized identities simultaneously (e.g., women of color are likely to experience harassment that is sexist and racist; people who identify as queer are likely to experience harassment that is additionally homophobic) (Cross, 2015). A few studies have also demonstrated that being targeted for multiple marginalized social identities increases the risk for negative outcomes. In their study of 965 adolescents using data from the 2006 Boston Youth Survey, Garnett et al. (2014) found that students who had experienced bullying based on multiple marginalized identities reported engaging in higher levels of self-harm than students who had experienced bullying based only on one identity, or than those who reported low levels of bullying. More recently, results from Mulvey et al. (2018) indicated that among the 678 adolescents sampled, those youth who experienced bias-based 42 bullying based on multiple social identities reported higher rates of school avoidance and fear than those who reported one type of bias-based bullying and those who reported non-bias-based bullying. Results such as the above suggest that by failing to capture bullying victimization through an intersectional lens, essential elements of the victimization experience may be overlooked. In addition, research questions related to risk, prevention, and intervention in multiply marginalized individuals are left unexplored (Hackworth, 2018). Current Study In short, relatively little research has examined the extent to which bias-based victimization is experienced online by those with multiple stigmatized identities, and how victims with intersectional identities may be differentially impacted. The current studies aim to fill these gaps, examining the prevalence, psychological impacts, and coping strategies of bias- based cyber victimization. In doing so, the current study will take an intersectional approach, examining how women of color respond to experiences of race-based, sex-based, and racialized- sexualized cyber victimization, respectively. We will specifically examine the extent to which membership in multiple marginalized social categories places women of color at a heightened risk for more frequent instances of each type of bias-based victimization, the respective psychological impacts of these experiences, and the coping strategies employed. Methods Participants Participants were 397 young women of color, including 295 college students from a large university in the Midwest and 102 young adults recruited through Amazon’s MTurk. Participants had a mean age of 19.4 years. All participants identified as cisgender. Thirty-nine percent of participants identified as Black, 36.4% Asian, 13.4% Hispanic, 1.3% Native American, and 9.8% 43 other race/ethnicities. Fifty-five percent of participants identified as single, and 95% did not report having children. Eighty-one percent reported earning less than $10,000, and the median combined parental income was between $75,000- $100,000. Participants completed a series of questionnaires through an online subject pool (SONA) or through Amazon’s MTurk. Participants signaled their consent by filling out and completing the questionnaires online. They were rewarded subject pool credit or small monetary reimbursement for their participation in the study. Quantitative Measures Focus of Victimization. Single items were used to assess the foci of cybervictimization (i.e., race, sex, and racialized-sexualized victimization). Participants indicated the degree to which they had experienced each form of victimization, (e.g., “Please indicate to what extent you have been victimized or targeted online based on your race”). This item was rated for each form of victimization on a 4-point scale ranging from (0) Never, (1) Seldom, (2) Often, and (3) Very Often. Racialized-Sexualized Victimization. Participants were asked whether they have been victimized based on specific forms of race- and sex-based victimization, as well as a combination of both, (e.g., “Please indicate how often you have been called insulting names that referred to your gender and race/ethnicity through text message”). These items were rated on a 5-point scale ranging from (0) Never, (1) Once or Twice, (2) Sometimes, (3) Often, and (4) Very Often. Existing studies of cyber victimization suffer from methodological limitations in that commonly used scales do not capture multiple areas of bias occurring simultaneously. The current scale adapts these items from Buchanan’s (2005) Racialized Sexual Harassment scale, which measures experiences of racial harassment, sexual harassment, and a combination of both, 44 experienced over the past 12 months at school, as there was no previous measure examining these experiences among bullying behavior specifically. The victimization items are adapted from Smith et al. (2008) and include victimization through five online media: text, instant/direct message, chat rooms/message boards, social media sites, and other websites. To confirm that cyberaggression experiences clustered by race, sex, and racialized- sexualized aggression as expected, the dataset was split in half so that exploratory and confirmatory factor analyses could be run independently. A maximum likelihood exploratory factor analysis with Direct Oblimin rotation was performed on the first subset of the data, allowing the factors to correlate with one another. Using eigenvalues greater than 1 and scree plot examination, results indicated that there were three factors underlying the dataset. The 5 sex- based items loaded relatively cleanly onto the first factor, the 5 race-based items loaded onto the second factor, and the 5 racialized-sexualized items loaded onto the third factor. Factors loadings ranged from .42 to .77 for sex-based items, .65 to .83 for race-based items, and .55 to .82 for racialized-sexualized items. Results are presented in Table 8. A confirmatory factor analysis (CFA) was then conducted on the other half of sample to confirm the results of the exploratory factor analysis. The variance of the factors was fixed at 1 so that each item’s factor loading could be freely estimated, and factors were allowed to correlate. Fit indices suggested acceptable model fit (χ2 (36) = 111.55, TLI= .91, CFI= .92, RMSEA= .083), indicating that the sex-based, race-based, and racialized-sexualized factors were separable in these data. The items for each model loaded onto their respective factors well (see Table 8). Emotional Impact. A single item was used to assess the emotional severity of each form of victimization, respectively, (e.g., “If you indicated that you have been victimized based on 45 your race, to what extent did this experience bother you?”). This item was rated for each form of victimization on a 4-point scale ranging from (1) Mild- The experience bothered me a little, (2) Moderate- The experience bothered me quite a bit, (3) Severe- I had trouble eating, sleeping, or enjoying myself because of the experience, and (4), Very Severe- I felt unsafe or threatened because of the experience. Coping Strategies. Participants were asked to report whether or not they engaged in any of 12 distinct responses to each form of peer victimization, as well as the perceived effectiveness of each response, (e.g., “If you indicated you have been victimized online based on your race, indicate to what extent these strategies have been helpful for you?”). Responses were rated on a 4-point scale ranging from (0) I did not do this, (1) I did this and things got worse, (2) I did this and nothing changed, and (3) I did this and things got better. Strategies were grouped into approach, support-seeking, reporting, and avoidance categories. The strategies, “I told the person to stop,” “I made a joke about it,” “I told him/her how I felt,” and “I deleted my social media account” were classified as approach strategies; “I told a friend” and “I told an adult at home” were labeled support-seeking strategies; “I told a teacher at school” and “I reported it to the authorities” were categorized as reporting strategies; avoidance strategies included “I ignored it,” “I stayed offline,” “I used alcohol or drugs,” and “I made plans to get back at him/her.” Cronbach’s alphas were calculated to measure the internal consistencies of the items. The items demonstrated acceptable internal consistency reliabilities, with alphas ranging from .60 to .63 for approach strategies, from .64 to .68 for support-seeking strategies, from .67 to .78 for reporting strategies, and from .61 to .65 for avoidance strategies across subtypes of victimization. 46 Analyses To examine our first research question (i.e., What percentage of youth are targeted online based on their race, sex, or both?), we calculated the percentages of participants experiencing each victimization subtype. To address our next two research aims (i.e., What are the emotional impacts of bias-based victimization?; How do youth respond to these experiences?), we evaluated differences in experiences of victimization across victimization subtype using a series of ANOVAS. Regression analyses were used to examine whether commonly used coping strategies were associated with the emotional impact of bias-based victimization. As in prior work (Study 1 in this dissertation), strategies were grouped into approach, support-seeking, reporting, and avoidance categories. Models were run separately to examine race-based, sex- based, and racialized-sexualized victimization. Results Correlations Correlations among the victimization subtypes were computed. Results are presented in Table 9. Self-reported experiences of sex- and race-based victimization were positively correlated (r=.42, p<.01). Racialized-sexualized victimization was moderately associated with sex-based victimization (r=.51, p<.01) and strongly positively associated with experiences of race-based victimization (r=.80, p<.01). Frequencies of Victimization Experiences The proportion of participants experiencing each victimization subtype are presented in Table 10, along with the frequency of those experiences. Results indicated that more than one- third (38.3%) of participants experienced bias-based cyber victimization in the past year. Of these participants, half (50%) reported experiencing racialized-sexualized victimization, nearly 47 one-third (31.6%) reported primarily race-based victimization, and 18.4% experienced victimization primarily based on sex. Participants experiencing victimization based on sex or race alone reported these experiences occurring relatively less frequently, with the majority of participants of sex- (75%) and race-based (85.1%) victimization reporting a frequency of 1 to 2 times in the past year. Racialized-sexualized victimization occurred more frequently, with more than one-third (38.2%) of participants reporting a frequency of 3 to 5 times, and nearly one- quarter (21.1%) reporting 6-9 times in the past year. Emotional Impact of Victimization A one-way ANOVA was run to examine rates of emotional impact by subtype of victimization. Results indicated that participants who faced racialized-sexualized victimization experienced a significantly higher emotional impact of the event than those victimized based solely on their sex (F(2,148 =2.32, p=.05); see Table 11). Those who experienced race-based victimization alone also reported experiencing a higher emotional impact than those who experienced sex-based victimization alone. Responses to Victimization Approach strategies. A series of one-way ANOVAS were used to examine differences in the use of various coping strategies in response to victimization across the three victimization subtypes. Results are presented in Table 12. Within the category of approach strategies, participants who reporting having experienced racialized-sexualized victimization were significantly more likely than those victimized based on sex alone to tell the perpetrator how they felt (F(2,148)=2.01), p<.05), and to delete their social media account (F(2, 148), 3.33, p=.01). Those victimized on race alone were also more likely than those targeted for sex alone to tell the perpetrator how they felt (F(2,148)=2.01), p<.05). 48 Support-seeking strategies. Within the category of support-seeking strategies, participants endorsing racialized-sexualized victimization reported telling a friend at higher rates than those victimized for race or sex alone (F(2,148)=2.04, p<0.5). No differences emerged in rates of telling an adult at home. Reporting strategies. In terms of reporting strategies, participants reporting racialized- sexualized victimization were more likely than those targeted for race or sex alone to tell a teacher at school (F(2, 148)= 2.12, p<.05). There were no significant differences in reporting the event to authorities. Avoidance strategies. Among the avoidance strategies, those reporting racialized- sexualized victimization were more likely than those reporting sex-based victimization alone to ignore the event (F(2,148)=2.98, p<.05), use alcohol or drugs (F(2,148) =4.44, p<.01), try to get back at the person (F(2,148)=2.75, p<.05), and stay offline (F(2,148)=2.10, p<.05). Victims of racialized-sexualized victimization were also more likely than those of race-based victimization alone to stay offline. Associations between Coping Strategies and Emotional Impact of Victimization Regression analyses were used to determine whether the above coping strategies were associated with the severity of the emotional impact of the experience. In our first regression analysis, we assessed the extent to which the four types of coping strategies predicted the level of emotional impact (i.e., mild, moderate, severe, and very severe) of race-based victimization (see Table 13). As predicted, approach and support-seeking strategies were associated with a lower emotional impact of victimization (β= -.19, p<.05; β= -.21, p<.05, respectively). Reporting strategies, on the other hand, were positively associated with emotional impact (β= .18, p<.05). 49 Next, we examined the extent to which various coping strategies predicted the level of emotional impact of sex-based victimization. Again, consistent with our hypotheses, support seeking strategies were associated with a lower severity of emotional impact (β= -.18, p<.05). However, contrary to our predictions, approach, reporting, and avoidance strategies were not associated with the severity of emotional impact. Finally, we examined the extent to which the various coping strategies predicted the level of emotional impact of racialized-sexualized victimization. In line with our predictions, approach strategies were associated with a lower emotional impact of victimization (β= -.19, p<.05), as were support-seeking strategies (β= -.23, p<.05). Reporting strategies were associated with a higher severity of emotional impact (β= .20, p<.05). In contrast to our predictions, avoidance strategies were again not associated with the level of emotional impact experienced. Discussion Rather than examining who is at most risk for cybervictimization by a single social status, the current study examined who is at most risk across multiple social statuses, including gender and race. The first goal of the study was to examine prevalence rates of race- and sex- based victimization across victimization subtypes (i.e., race-based, sex-based, racialized- sexualized victimization). Over one-third of participants in our study were involved in bias-based victimization in the past year, a finding that is higher than previous studies examining bias-based victimization among women of color (Pew Research Center, 2017). Of the participants involved in bias-based victimization, one-half had been targeted for both their gender and racial identities simultaneously, and members of this group were more likely than those targeted for their gender or race alone to experience victimization frequently, with nearly 60% reporting the experience having occurred at least 3 to 5 times in the past year. In contrast, the vast majority of participants 50 targeted for their gender or race alone reported victimization that occurred 1 to 2 times in the past year. These findings support the results of recent studies suggesting that holding intersecting marginalized identities increases the risk for victimization online. Indeed, in their study of online harassment, Felmlee, Rodis, and Fransisco (2018) found distinctive ways in which women of color were targeted online, such that harmful gender and racial stereotypes about these groups were weaponized against them. Similarly, other studies have demonstrated that Black adolescent females may be at a heightened risk for online sexual harassment compared to their White female and Black and Hispanic male peers (Mitchell & Wolak, 2007; Tynes & Mitchell, 2013). The second goal of the current study was to examine the emotional impacts of bias-based victimization. Participants who were simultaneously targeted for both their gender and racial identities reported experiencing a higher emotional impact of the experience than those who were targeted for their gender or race alone, while those targeted for their race reported a higher emotional impact than those targeted for their gender alone. These findings support extant literature examining peer victimization and intersectionality demonstrating the complex relationships between identity and outcomes of victimization. Byrd and Carter Andrews (2016) examined discrimination and school related outcomes in a sample of 1468 participants, and found that students who experienced discrimination related to their marginalized identities evidenced worse academic performance and engagement, poorer teacher-student relationships, and negative perceptions of their school climate (Byrd & Carter Andrews, 2016). Garnett et al. (2014) examined a large and diverse sample of youth (N=965), and similarly found that those who experienced discrimination related to multiple social identities were more likely to engage in self-harm and had higher rates of suicidal ideation compared to those who experienced racial discrimination alone. 51 As a third goal for our study, we examined the coping strategies used in response to victimization. Differences emerged in the use of approach strategies, with participants who reported being targeted for both their gender and race demonstrating significantly higher rates of telling the perpetrator to stop and deleting their social media accounts than those reporting being targeted for their gender or race alone. There were also differences in rates of support-seeking and reporting strategies, with those in the racialized-sexualized group telling a friend and telling a teacher about their experience at higher rates than the other groups. Among the avoidance strategies, those in the racialized-sexualized group were also more likely to ignore the event, use alcohol or drugs, try to get back at the perpetrator, and stay offline than those in the other groups. These results support findings of prior research demonstrating that young women of color may be more likely to use social support in the face of general stress than their White counterparts (Chapman & Mullis, 2000). What’s more, our results suggest that young women of color use a variety of approach and avoidance strategies in response to bias-based victimization, and may use these strategies at higher rates when targeted for multiple social identities simultaneously. It is also possible that these individuals engaged in higher rates of strategy use because they struggled to effectively moderate their distress. In a study by Black et al. (2011) of nearly 68,000 college women, those with multiple marginalized identities were more likely to report higher rates of sexual violence than other groups, and to report increased levels of distress. They engaged in more frequent and more maladaptive coping strategies than their counterparts. Future studies should examine effectiveness rates of individual strategies in addition to frequency of use, to shed light on specific strategies that may help lower distress. We lastly examined the associations between coping strategies and emotional impact of victimization. Approach strategies were associated with a lower emotional impact of race-based 52 and racialized-sexualized victimization, as hypothesized, while support-seeking strategies were associated with a lower emotional impact of both race-and sex-based victimization alone as well as racialized-sexualized victimization. Reporting strategies were associated with a higher emotional impact for race-based and racialized-sexualized, but not sex-based victimization alone, while avoidance strategies were not associated with emotional impact for any type of victimization. These findings are consistent with prior literature documenting the effectiveness of approach and support-seeking strategies for decreasing the negative effects of victimization, with support-seeking strategies being particularly helpful for women of color (Davidson & Demaray, 2007; Kochenderfer-Ladd & Skinner, 2002). These findings could suggest that participants found reporting strategies to be ineffective for coping with victimization, or that victims turned to these strategies following especially distressing experiences, in line with prior work demonstrating that women of color are more likely to feel reluctant reporting instances of harassment or violence due to societal history of not being believed about these experiences (Crenshaw, 1989). Strengths and Limitations Consistent with Intersectionality theory, current findings of this study shed light on the complexity between intersecting social identities and experiences of victimization. Past research consistently indicates that youth with multiple marginalized identities are at the highest risk for discrimination (Byrd & Carter Andrews, 2016) and mental health problems (Garnett et al., 2014; LeVasseur, Kelvin, & Grosskopf, 2013). Our findings support these prior studies, as the women of color in our study experienced victimization based on both race and gender as more pervasive and distressing than victimization based on one identity alone. Another strength of the present study was the addition of the use of behaviorally-based items, rather than the use of labeling 53 only, to assess experiences of victimization. Prior studies examining sexual and racial harassment have demonstrated that participants were more willing to report on experiences associated with harassment than directly label them using this terminology (Buchanan, Settles, Wu, & Hayashino, 2018; Stoll & Block, 2015). Future studies examining experiences of cyber victimization should also make use of behavioral measures to improve reporting accuracy. That said, there are several limitations to this study. Although the current study examined commonly used coping strategies and their association with the emotional impact of the experience, causal inferences could not be drawn from our study design. Thus, more research is needed to test and identify the factors that will protect youth from the pain of being victimized online, as this will be vital to the continuing development and improvement of intervention programs to help victims of bullying, especially those youth from marginalized backgrounds who may be at heightened risk for experiencing negative outcomes (Card et al., 2007; Mendez (2016; Peskin et al., 2006). The present study was strengthened by its assessment of a large and diverse young adult sample with respect to racial and ethnic diversity. However, generalizations that can be made from the current findings remain limited. Future research would benefit from examining multiple universities across geographically diverse regions. Such research would help determine whether the current study findings are replicable in a more generalizable sample, and thus have important implications for the policy and practice. The current study was also limited to survey data collected from participants, which while useful, provides only a surface-level understanding of their lived experiences. Future studies should consider incorporating qualitative methods to more deeply examine individuals’ experiences of victimization and explore their ways of coping, 54 which could provide meaningful information to help researchers, providers, and policymakers better understand how to help youth facing bias-based victimization. Clinical Implications The current study has important potential implications for bias-based cyberbullying prevention and intervention efforts. Reporting strategies are the most are commonly recommended strategies in intervention programs (deLara, 2012; Olweus, 1993). Although we cannot make assumptions as to the direction of causation (as noted above), we found no evidence that the use of reporting strategies decreases the negative emotional effects of victimization. Rather, they were related to higher levels of emotional impact. Consistent with findings from prior studies, these results may reflect the fact that educators, authority figures, or other adults may not adequately address the situation (Mishna et al., 2018). Approach strategies, by contrast, appeared to be effective for all victims in our study, and support-seeking strategies appeared to be particularly helpful. It is important that programs use evidence-based, effective practices, and they should aim to highlight the strategies that may most benefit students in response to particular situations. Thus, while reporting strategies may be important for perpetrator accountability, they appear to be less useful for emotionally supporting the victim. We would thus recommend both that programs 1) let students know what strategies will actually be most helpful to them emotionally, and 2) train educators and authority figures to believe students, thereby decreasing the possibility of further emotional harm to those students when they do report instances of bias-based bullying. Building on the latter point, it is also critical that efforts to address cyberbullying incorporate an awareness of the broader structures in which students, parents, teachers and schools operate, and which in turn are influenced by dominant discourses that reinforce dominant 55 social norms (Kousholt & Fisker, 2015). Common approaches to anti-bullying intervention tend to focus on individual-level factors; however, when individual factors are blamed for bullying victimization, and when parents, teachers and students are given the responsibility for addressing bullying, systemic and structural responsibility tends to be erased or minimized (Kousholt & Fisker, 2015). An intersectional perspective encourages shifting responsibility from individuals toward broader social structures that perpetuate inequality (Kousholt and Fisker, 2015). 56 GENERAL DISCUSSION Emerging evidence suggests that bias-based victimization is a particularly harmful form of bullying. To date, however, virtually no research has examined whether and how these harms might extend to the line context, and moreover, how they might vary across the intersection of multiple marginalized identities. The current study aimed to address this limitation in the literature by examining the prevalence, psychological impacts, and coping strategies of race and sex-based forms of cyber victimization (Study 1). Study 2 re-examined these findings through an explicitly intersectional lens, focusing specifically on the prevalence, psychological impacts, and coping strategies of the racialized-sexualized forms of victimization experienced by women of color. Prevalence of victimization. The results of our first study were consistent with prior studies demonstrating that gender and race may individually alter the risk for peer victimization, with the women in our study reporting they were more likely to experience sex-based victimization than men, and the participants of color reporting they were more likely than White participants to experience race-based victimization. The results of our second study, which focused directly on women of color, further found that approximately half had been targeted for their gender and racial identity simultaneously within the past year. This group was also significantly more likely than those who had been targeted based on one social identity to report victimization that occurred frequently (3-5 times within the past year). Our results add to the growing body of literature suggesting that Black female youth may have an especially heightened risk for online sexual harassment compared to their peers (Mitchell, Finkelhor, & Wolak, 2007; Tynes & Mitchell, 2014), and that this sexual harassment is often racialized as well. 57 Emotional impact. Consistent with prior research demonstrating positive associations between cyber victimization and distress, our participants rated the overall emotional impact of bias-based victimization as mild-to-moderate (Kowalski et al., 2014; Mishna et al., 2009). Study 1 indicated that participants with marginalized identities were more likely than White participants to suffer a higher emotional impact of race- or sex-based victimization, regardless of gender. Study 2 further indicated that women of color targeted for their gender and race simultaneously experienced a higher emotional impact than those targeted for gender or race alone. Our results thus clearly support the findings of recent work demonstrating negative outcomes associated with victimization based on multiple marginalized social identities, including higher rates of distress, poorer academic performance, and higher rates of self-harm (Byrd & Carter Andrews, 2016; Garnett et al., 2014). Reponses to victimization. Our results suggest young women of color may use a variety of approach and avoidance strategies to cope with bias-based victimization, and they may use these strategies at higher rates when targeted for multiple social identities simultaneously. We also found that White women were more likely than Black women to report the situation to the authorities. Women of color who experienced racialized-sexualized victimization were not likely to report the situation to authorities, although they were more likely than other groups to report the situation to a teacher. However, across all groups, reporting strategies were associated with a higher emotional impact. Given the cross-sectional nature of these data, such findings suggest that either participants may have found these strategies to be ineffective for coping with victimization, that they are more likely to report very upsetting experiences, or both. That said, we note that the first interpretation is most consistent with prior literature indicating that women 58 of color are more likely to feel reluctant reporting instances of harassment or violence due to societal history of not being believed about these experiences (Crenshaw, 1989). We also found that women of all racial and ethnic groups used high levels of support seeking strategies in response to either gender or racial victimization, and that women of color used these strategies at higher rates than men of color in response to racialized victimization. Such findings collectively support the findings of prior studies demonstrating that social support is a preferred coping strategy among female youth in response to social stress (Seiffge-Krenke, 2011). Moreover, women of color who experienced racialized-sexualized victimization used support-seeking strategies at higher rates than those who were targeted for their race or gender alone. findings are consistent with prior literature documenting the effectiveness of support- seeking strategies for decreasing the negative effects of victimization, with support-seeking strategies being particularly helpful for women of color (Davidson & Demaray, 2007; Kochenderfer-Ladd & Skinner, 2002). Notably, the coping strategy factors that emerged in the present studies differed from the hypothesized two-factor model of approach and avoidance coping. Rather, a four-factor model consisting of approach, avoidance, reporting, and support-seeking strategies was generated. While research examining coping styles among cyber victimization remains scarce, Alipan et al. (2018) posited that young adults differ from children in how they cope with cyber bullying, responding either through a problem-solving, emotion or avoidance, or a technological solution (e.g., deleting social media accounts). Individuals select strategies according to level of perceived control over their cyber bullying experience (Alipan et al. 2018). It is possible that the reporting items did not load onto the approach factor because although they would typically be considered problem solving items, many victims are reluctant to report cyber bullying incidents 59 to schools and authority figures due to perceptions that their concerns will not be adequately addressed (i.e., lack of perceived control) (Mishna, 2018; Tenebaum, Varjas, Meyers, & Parris, 2011). In place, students have turned to alternative strategies, including more technological ones, despite not knowing how they will impact the situation (Alipan et al., 2018). Limitations and future research. A few important limitations of the current studies should be noted. First, our data were cross-sectional, examining commonly used coping strategies and their association with the emotional impact of the experience. As such, causal inferences cannot be made among our variables. Future research is needed to test and identify the factors that will protect youth from the pain of being victimized online. The current study was also limited to survey data, which while useful, provides a limited understanding of their lived experiences. Future studies should consider using qualitative methods to more thoroughly assess individuals’ experiences of victimization and coping, which could provide meaningful information in understanding how to better help youth facing bias-based victimization. Another limitation of Study 1 was the use of labeled, rather than behaviorally-based items, to assess experiences of victimization, which may have affected participant responses and masked potential gender or racial/ethnic differences. Prior studies examining sexual and racial harassment have demonstrated that participants were more willing to report on experiences associated with harassment than directly label them using this terminology (Buchanan, Settles, Wu, & Hayashino, 2018; Stoll & Block, 2015). This limitation was addressed in Study 2. Future studies examining experiences of cyber victimization should use behavioral measures to improve reporting accuracy. Finally, measures of coping we used have not been tested extensively and do not have established reliability or validity. The factors that emerged in the present study should be tested again in a separate diverse sample of young and emerging adults. 60 Limitations should be noted regarding our samples as well. First, we did not allow participants the option to select multiple racial or ethnic identities, but allowed for written response. We were insufficiently powered to test for group differences, which may have obscured potential findings among these groups. It will be vital for future studies to examine these groups more closely, as biracial and multiracial groups report elevated rates of sexual victimization compared to other groups (Black et al., 2011). Second, while we collected data for rates and impact of victimization focused on sexual orientation, these data were not included in our final analyses. However, sexual minority youth are more likely than their straight counterparts to experience bullying, sexual violence, societal stigma, discrimination, psychological maladjustment, and suicidality (Espinoza and Wright, 2018; Smith et al., 2020). There is a need for future work to better understand the impact of cyber victimization on the LGBTQ+ community. Implications. Although we cannot make assumptions as to the direction of causation, the current study has important potential implications for bias-based cyberbullying prevention and intervention efforts. The results of our study provide evidence that participants’ responses to cyber victimization vary, and that the most effective coping strategies are not those typically recommended by administrators (i.e., reporting strategies may not decrease the negative effects of victimization). Approach strategies, by contrast, appeared to be effective for nearly all victims in our studies, and support-seeking strategies appeared to be particularly helpful for female victims who had experienced any type of bias-based victimization. It is important that programs use evidence-based, effective practices, and they should aim to highlight the strategies that may most benefit students in response to particular situations. 61 Relatedly, it would be important that educators are trained to recognize and specifically respond to instances of bullying that feature bias as a motive and target this issue directly. Intervention programs should aim to provide an environment that decreases the responsibility placed on the victims while validating their experience. Reisner et al. (2020) found that among adolescents and young adults who had experienced bullying, school staff were often non- responsive, lacked empathy, or did not convey trust or confidentiality. Similarly, in their study of 107 students who had experienced bias-based bullying, students reported teachers lacked the interpersonal skills to be able to effectively intervene (Hillard et al., 2014). In line with the tenets of intersectionality theory, it is also critical that efforts to address cyberbullying incorporate an awareness of the broader social structures in which these interactions operate (Kousholt & Fisker, 2015). Common approaches to anti-bullying tend to emphasize intervention at the individual level and to do so without regard to marginalized identities. An intersectional perspective encourages a focus on the broader social structures that perpetuate social inequalities, such as the important role of the school climate. School curricula is one key area where students can learn important dynamics about social behavior (Wernick et al., 2021). Critical multicultural programs focused on issues of race and racism have documented increased engagement in student anti-racist behavior, willingness to address inequality and oppression, and higher rates of engagement, including political participation and activism (Wernick, et al., 2021). This work suggests that implementation of a multicultural curricula may play an important role in teaching youth to recognize systems of oppression, and to encourage behaviors that promote acceptance and inclusion. (Kousholt and Fisker, 2015). Buy-in from school administration is a key factor for addressing bias-based bullying. Studies have demonstrated that bias-based bullying and 62 harassment frequently occur in the presence of teachers and administrative members who do not take action to stop it, leading to a culture of minimization or acceptance of the behavior (Chambers, van Loon, & Tincknell, 2004; Mishna et al., 2018; Stein, 1995). It is critical for schools to take action to recognize and respond to bias-based bullying, which could include implicit bias training for staff, the inclusion of diverse staff members, and the implementation of clear and consistent anti-bullying protocols (Reisner et al., 2020). One such protocol is the Olweus Bullying Prevention Program (OBBP) (Olweus et al., 2020). Developed in the 1980s, the program contains community, school, classroom, and individual-level components demonstrated to be effective in reducing bullying and antisocial behavior among school-aged youth. Educators’ efforts to respond to bias-based bullying must also recognize that students have multiple social identities and that those with multiple marginalized identities are at the greatest risk for peer victimization. In a recent qualitative study of 28 LGBTQ adolescents and young adults and 19 school staff, youth spoke of the importance of addressing bullying from an intersectional framework, highlighting a need to educate school staff about the issues unique to LGBTQ youth of color (Reisner et al., 2020). Staff, in turn, did not recognize this group may need extra support, nor did they report a need to learn more about the challenges these students may face. This study also pointed to the need for more representation of identities among school staff, as comfort with staff may affect levels of trust and willingness among students to confide in them (Lesesne et al., 2015, Mishna et al., 2018). Consideration of intersectionality and striving toward more inclusive and supportive school environments is an important step toward better equipping educators to support victims and address instances of bias-based bullying. 63 APPENDICES 64 APPENDIX A: TABLES 65 Table 1. Research Questions and Hypotheses Research Question Hypothesis RQ1: What % of youth are targeted online based on their race or sex? RQ1a: Do these prevalences vary across H1a: Sex-based cyberbullying victimization various privileged and marginalized will be higher in women than in men. Race- identities? based cyberbullying victimization will be higher in marginalized participants than in White participants. RQ1b: Are women of color more likely to H1b: Sex-based victimization will be higher be the targets of sex-based victimization in marginalized women than White women. than White women? RQ1c: Do men and women of color differ H1c: We predict that race-based victimization in their risk for race-based victimization? will be higher among marginalized women than men. RQ2: What are the emotional impacts of race and sex-based victimization? RQ2a: Does the emotional impact of H2a: Women will experience a greater victimization vary across privileged and emotional impact of sex- based victimization marginalized identities? than men. Participants of color will experience a higher emotional impact of race- based victimization than White participants. RQ2b: Does the emotional impact of sex- H2b: Women of color targeted for their sex based victimization differ for women of will experience a higher emotional impact than color and White women? White women. RQ2c: Does the emotional impact of H2c: Women of color will experience a higher race-based victimization differ for men emotional impact than men of color. and women of color? RQ3: How do youth respond to experiences of bias-based victimization? RQ3a: Does the use of coping strategies H3a: Women of color will use more support- for sex-based victimization differ for seeking strategies than White women. women of color and White women? RQ3b: Does the use of coping strategies H3b: Men of color will use more avoidance for race-based victimization differ for coping strategies than women of color. men and women of color? RQ4: Are commonly used coping H4: Approach coping strategies will be strategies associated with the emotional associated with lower emotional impacts of impact of race and sex-based race and sex-based victimization than victimization? avoidance coping strategies. 66 Table 1 (cont’d) RQ4a: Does the association between H4a: Given prior research pointing to a coping strategies and emotional impact stronger reliance on social support networks in differ for White women and women of POC, we predict that approach and support color? seeking strategies will be associated with a lower emotional impact of sex-based victimization for women of color than White women. RQ4b: Does the association between H4b: We predict that approach coping coping strategies and emotional impact strategies will be associated with a lower differ for women and men of color? emotional impact of race-based victimization for women than men of color. 67 Table 2. Factor Loadings for Coping Items in Response to Race-Based Victimization Factor Item EFA Factor CFA Factor Loadings Loadings Race-based Victimization Men of Color F1 (Approach Strategies) 1. I told the person to stop .33 .30 2. I told a friend .41 .48 3. I told an adult at home .43 .52 4. I made a joke about it .47 .54 5. I told him/her how I felt .64 .61 6. I deleted my social media account .50 .63 F2 (Avoidance Strategies) 1. I Ignored it .53 .66 2. I stayed offline .34 .40 3. I made plans to get back at him/her .71 .67 4. I used alcohol or drugs .61 .68 F3 (Reporting Strategies) 1. I told a teacher at school .58 .61 2. I reported it to the authorities .72 .82 Women of Color EFA Factor CFA Factor Loadings Loadings F1 (Support Seeking Strategies) 1. I told a friend .58 .61 2. I told an adult at home .81 .79 F2 (Approach Strategies) 1. I told the person to stop .35 .41 2. I made a joke about it .45 .42 3. I told him/her how I felt .70 .69 4. I deleted my social media account .56 .65 F3 (Avoidance Strategies) 1. I Ignored it .52 .69 2. I stayed offline .37 .45 3. I made plans to get back at him/her .69 .68 4. I used alcohol or drugs .60 .62 F4 (Reporting Strategies) 1. I told a teacher at school .49 .69 2. I reported it to the authorities .61 .66 Sex-based Victimization F1 (Support Seeking Strategies) I told a friend .51 .54 I told an adult at home .87 .77 F2 (Approach Strategies) I told the person to stop .29 .32 68 Table 2 (cont’d) I made a joke about it .43 .36 I told him/her how I felt .65 .59 I deleted my social media account .61 .57 F3 (Avoidance Strategies) I Ignored it .49 .58 I stayed offline .37 .44 I made plans to get back at him/her .53 .46 I used alcohol or drugs .51 .48 F4 (Reporting Strategies) I told a teacher at school .56 .64 I reported it to the authorities .79 .77 Note. For race-based victimization in men of color, the first factor had an eigenvalue of 3.58 and explained 31.22% of the variance, the second factor had an eigenvalue of 1.75 and explained 12.01% of the variance, and the third factor had an eigenvalue of 1.42 and explained 10.81% of the variance. For women of color, the first factor had an eigenvalue of 4.21 and explained 33.25% of the variance, the second factor had an eigenvalue of 2.01 and explained 15.12% of the variance, the third factor had an eigenvalue of 1.79 and explained 11.07% of the variance, while the fourth factor had an eigenvalue of 1.04 and explained For sex-based victimization, the first factor had an eigenvalue of 4.07 and explained 35.61% of the variance, the second factor had an eigenvalue of 2.77 and explained 13.92% of the variance, the third factor had an eigenvalue of 1.75 and explained 10.10% of the variance, while the fourth factor had an eigenvalue of 1.01 and explained 8.12% of the variance. 69 Table 3. Mean Differences in Victimization and Emotional Impact of Victimization by Gender and Race n M SD t df p d Prevalence of Victimization Sex-based victimization by Gender Men/Women 203/425 .25/.70 .50/.79 -8.72 579.77 <.01 .68 Race-based victimization by Race White/ Black 291/167 .18/.78 .41/.82 -9.04 214.44 <.01a .93 White/ Asian 291/181 .18/.65 .41/.66 -8.60 265.01 <.01a .86 Emotional Impact of Victimization Sex-based victimization by Gender Male/ Female 44/222 1.41/1.48 .54/.63 -.72 264 .24 .12 Race-based victimization by Race White/ Black 48/99 1.25/1.64 .44/.61 -4.38 124.91 <.01a .73 White/ Asian 48/101 1.25/1.45 .44/.61 -2.24 123.80 .03a .37 a p values set at .025 due to Bonferroni correction to control for multiple comparisons 70 Table 4. Mean Differences in Use of Coping Strategies for Sex-Based Victimization of Women across White and Marginalized Identities n M SD t df p d APPROACH STRATEGIES I told the person to stop White/Black 122/54 .47/.35 .50/.51 -.72 174 .47 .24 White/Asian 122/44 .47/.56 .50/.50 -1.13 164 .26 .18 White /Marginalized 122/128 .48/.54 .50/.50 .06 218 .95 .12 I made a joke about it White/Black 122/54 .51/.27 .50/.44 3.50 136.86 <.01a .51 White/Asian 122/44 .51/.29 .50/.46 3.09 144.27 <.01a .46 White / Marginalized 122/98 .51/.27 .50/.45 4.57 217.65 <.01a .50 I told him/her how I felt White/Black 122/3054 .26/.39 .44/.49 -1.62 92.40 .10 .30 White/Asian 122/44 .25/.25 .44/.44 .03 164 .98 0 White / Marginalized 122/98 .26/.28 .44/.45 -.22 206.41 .83 .04 I deleted my social media White/Black 122/54 .09/.30 .29/.46 -3.25 87.16 <.01a .55 White/Asian 122/44 .09/.37 .29/.49 -4.29 91.43 <.01a .69 White / Marginalized 122/98 .09/.29 .28/.45 -3.71 156.56 <.01a .53 SUPPORT-SEEKING STRATEGIES I told a friend White/Black 122/54 .68/.58 .47/.50 1.33 116.30 .19 .21 White/Asian 122/44 .68/.66 .47/.48 .21 164 .84 .04 White / Marginalized 122/98 .70/.65 .46/.48 .82 218 .41 .11 I told an adult at home White/Black 122/54 .31/.30 .46/.46 .18 174 .86 .02 White/Asian 122/44 .32/.34 .46/.48 -.26 164 .80 .04 White / Marginalized 122/98 .32/.32 .47/.47 .05 218 .96 .0 REPORTING STRATEGIES I told a teacher at school White/Black 122/54 .09/.09 .28/.29 -.17 174 .86 0 White/Asian 122/44 .09/.34 .28/.48 -4.03 90.60 <.01a .64 White / Marginalized 122/98 .07/.17 .26/.36 -2.12 171.72 .04a .32 71 Table 4 (cont’d) I reported it to the authorities White/Black 122/54 .05/0 .21/0 2.50 121 .01a .34 White/Asian 122/44 .06/.26 .25/.44 -3.46 87.80 <.01a .56 White / Marginalized 122/98 .05/.06 .22/.24 -.39 218 .70 .04 AVOIDANCE STRATEGIES I ignored it White/Black 122/54 .73/.70 .45/.46 .34 174 .73 .07 White/Asian 122/44 .73/.79 .45/.41 -1.08 144.88 .28 .14 White / Marginalized 122/98 .76/.74 .43/.44 .47 218 .64 .05 I made plans to get back at him/her White/Black 122/54 .12/.31 .32/.47 -3.07 91.27 <.01a .47 White/Asian 122/44 .12/.44 .32/.50 -4.91 94.72 <.01a .76 White / Marginalized 122/98 .11/.32 .32/.47 -3.98 160.51 <.01a .52 I used alcohol or drugs White/Black 12/38 .35/.28 .49/.46 .53 48 .60 .15 White/Asian 12/41 .35/.22 .49/.44 .07 51 .94 .28 White / Marginalized 12/79 .33/.27 .49/.44 .48 89 .63 .13 I stayed offline White/Black 12/38 .29/.30 .47/.47 -.08 48 .94 .02 White/Asian 12/41 .29/.34 .47/.50 -.38 25.56 .70 . White / Marginalized 12/79 .33/.27 .49/.44 .48 89 .63 .13 *Strategies dichotomized, 1= used strategy, 0= did not use strategy ap values set at .016 due to Bonferroni correction to control for multiple comparisons 72 Table 5. Mean Differences in Use of Coping Strategies for Race-Based Victimization of Marginalized Identities by Gender n M SD t df p d APPROACH STRATEGIES I told the person to stop Black Men/Women 27/72 .67/.64 .48/.48 .26 97 .80 .06 Asian Men/Women 53/47 .51/.66 .50/.48 -1.53 97.53 .13 .31 Men/Women 80/119 .56/.65 .50/.48 -1.19 165.01 .24 .18 I made a joke about it Black Men/Women 27/72 .26/.36 .45/.48 -.99 50.34 .32 .21 Asian Men/Women 53/47 .36/.21 .48/.41 1.62 98 .11 .34 Men/Women 80/119 .33/.30 .47/.46 .33 197 .74 .06 I told him/her how I felt Black Men/Women 27/72 .30/.38 .47/.49 -.72 97 .47 .17 Asian Men/Women 53/47 .26/.19 .45/.40 .86 98 .39 .16 Men/Women 80/119 .28/.30 .45/.46 -.42 172.60 .67 .04 I deleted my social media Black Men/Women 27/72 .22/.22 .42/.42 .00 97 1.0 0 Asian Men/Women 53/47 .42/.30 .50/.46 1.22 98 .23 .25 Men/Women 80/119 .36/.25 .48/.44 1.96 158.35 .04a .24 SUPPORT-SEEKING STRATEGIES I told a friend Black Men/Women 27/72 .44/.63 .51/.49 -1.62 97 .10 .38 Asian Men/Women 53/47 .45/.72 .50/.45 -2.83 97.97 .01a .57 Men/Women 80/119 .45/.66 .50/.47 -3.02 163.32 <.01a .43 73 Table 5 (cont’d) I told an adult at home Black Men/Women 27/72 .19/.46 .40/.50 -2.83 58.93 .01a .60 Asian Men/Women 53/47 .38/.53 .49/.50 -1.55 98 .12 .30 Men/Women 80/119 .31/.49 .47/.50 -2.52 177.76 .01a .37 REPORTING STRATEGIES I told a teacher at school Black Men/Women 27/72 .15/.24 .36/.43 1.02 54.84 .31 .23 Asian Men/Women 53/47 .26/.34 .45/.48 -.83 98 .41 17 Men/Women 80/119 .23/.28 .42/.45 -.83 197 .41 .11 I reported it to the authorities Black Men/Women 27/72 .19/.13 .40/.35 .57 97 .57 .16 Asian Men/Women 53/47 .19/.19 .40/.40 -.04 98 .97 0 Men/Women 80/119 .19/.16 .39/.37 .51 197 .41 .08 AVOIDANCE STRATEGIES I ignored it Black Men/Women 27/72 .93/.76 .27/.43 2.25 74.79 .03a .47 Asian Men/Women 53/47 .79/.72 .41/.45 .80 98 .43 .16 Men/Women 80/119 .84/.75 .37/.44 1.56 186.24 .12 .22 I made plans to get back at him/her Black Men/Women 27/72 .41/.37 .50/.49 .29 97 .77 .08 Asian Men/Women 53/47 .62/.47 .49/.50 1.55 98 .12 .30 Men/Women 80/119 .55/.41 .50/49 1.93 197 .05a .28 74 Table 5 (cont’d) I used alcohol or drugs Black Men/Women 23/47 .22/.32 .42/.47 -.88 68 .38 .22 Asian Men/Women 51/41 .37/.37 .49/.49 .07 90 .95 0 Men/Women 74/88 .32/.34 .47/.48 -.22 160 .83 .04 I stayed offline Black Men/Women 23/47 .17/.32 .44/.47 -1.37 52.26 .18 .33 Asian Men/Women 51/41 .25/.32 .49/.35 -.65 90 .52 .16 Men/Women 74/88 .23/.32 .42/.47 -1.26 159.14 .21 .20 ap values set at .016 due to Bonferroni correction to control for multiple comparisons 75 Table 6a. Emotional Impact of Sex and Race-Based Victimization Associated with Coping Strategies B SE β ƒ2 Sex-based victimization .06 Constant 1.43 .07 Approach strategies -.06 .02 -.12* Support-seeking strategies -.16 .06 -.22** Reporting strategies .20 .04 .28** Avoidance strategies -.01 .02 -.03 Race-based victimization .04 Constant 1.45 .07 Approach strategies .02 .02 .06 Support-seeking strategies -.12 .03 -.11* Reporting strategies .17 .08 .24* Avoidance strategies .01 .04 .01 Table 6b. Emotional Impact of Sex-Based Victimization of Women Associated with Coping Strategies and Race B SE β ƒ2 Emotional impact of sex-based victimization in Black as compared to White women .03 Constant 1.26 .10 Approach strategies -.02 .08 -.05 Support seeking -.16 .18 -.23* strategies Reporting strategies .16 .12 .22* Avoidance .00 .07 -.00 strategies Race .25 .19 .31* Approach x Race -.08 .09 -.13 Support x Race -.02 .14 -.02 Reporting x Race -.52 .38 -.12 Avoidance x Race -.02 .10 -.02 76 Table 6b (cont’d) Emotional impact of sex-based victimization in Asian as compared to White women .03 Constant 1.25 .12 Approach strategies -.02 .06 -.04 Support seeking -.16 .09 -.19* strategies Reporting strategies .16 .14 .13* Avoidance .00 .08 -.00 strategies Race .31 .25 .21* Approach x Race -.10 .13 -.12 Support x Race .06 .18 .05 Reporting x Race .26 .26 .16 Avoidance x Race -.08 .13 -.10 77 Table 7. Emotional Impact of Race-Based Victimization of Participants of Color Associated with Coping Strategies and Gender B SE β ƒ2 .05 Constant 1.45 .07 Approach strategies -.08 .06 -.19* Support seeking -.13 .11 -.19 strategies Reporting strategies .29 .13 .27* Avoidance .06 .06 .17* strategies Gender .32 .16 .26* Approach x gender -.03 .08 -.06 Support x gender .21 .11 .24* Reporting x gender -.30 .15 -.26* Avoidance x gender .09 .06 .13 *Men coded as 0, Women coded as 1 78 Table 8. Factor Loadings for Victimization Items by Subtype Factor Item EFA CFA Factor Factor Loadings Loadings Sex-Based Items 1. Been called insulting names that referred to your gender .51 .60 through text message 2. Received an upsetting instant message or direct message that .77 .75 negatively referred to your gender 3. Been made fun of for your gender in a chat room or message .61 .69 board 4. Had something posted on your social media site (e.g., .42 .50 Facebook, Twitter, etc.) that referred to your gender negatively 5. Had something posted about you on another web page that .54 .61 referred to your gender negatively Race-Based Items 1. Been called insulting names that referred to your race/ethnicity .68 .65 through text message 2. Received an upsetting instant message or direct message that .65 .72 negatively referred to your race/ ethnicity 3. Been made fun of for your race/ ethnicity in a chat room or .66 .59 message board 4. Had something posted on your social media site (e.g., .82 .73 Facebook, Twitter, etc.) that referred to your race/ ethnicity negatively 5. Had something posted about you on another web page that .83 .79 referred to your race/ ethnicity negatively Racialized-Sexualized Items 1. Been called insulting names that referred to your gender and .72 .65 race/ ethnicity through text message 2. Received an upsetting instant message or direct message that .73 .72 negatively referred to your gender and race/ethnicity 3. Been made fun of for your gender and race/ethnicity in a chat .55 .58 room or message board 4. Had something posted on your social media site (e.g., .81 .75 Facebook, Twitter, etc.) that referred to your gender and race/ ethnicity negatively 5. Had something posted about you on another web page that .82 .81 referred to your gender and race/ ethnicity negatively 79 Table 9. Correlations between Subtypes of Victimization 1. 2. 3. 1. Sex-Based 1.0 Victimization 2. Race-Based .42** 1.0 Victimization 3. Racialized- Sexualized .51** .80** 1.0 Victimization 80 Table 10. Frequency of Victimization in the Past Year across Subtypes Non-Involved Sex Race Racialized-Sexualized Overall N (%) 245 (61.7) 28 (7.1) 48 (12.1) 76 (19.1) 1-2 Times 21 (75.0) 41 (85.1) 31 (40.7) 3-5 Times 4 (14.3) 2 (4.2) 29 (38.2) 6-9Times 3 (10.7) 5 (10.4) 16 (21.1) 81 Table 11. Mean Differences in Emotional Impact of Victimization by Subtype Sex-Based Race-Based Racialized- Post-Hoc Victimization Victimization Sexualized Victimization M (SD) M (SD) M (SD) np Emotional 1.54 (.60) 1.92 (.74) 1.95 (59) R-S> S; .06 Impact of R>S Victimization 82 Table 12. Differences in Coping Strategies Used across Victimization Subtypes Sex-Based Race- Racialized- Post-Hoc Victimizati Based Sexualized on Victimizati Victimization on M (SD) M (SD) M (SD) np APPROACH STRATEGIES 1. I asked the person to .36 (.50) .57 (.50) .64 (.49) N.S .05 stop 2. I told them how I .36 (.50) .71 (46) .85 (.36) R-S>S; .08 felt R>S 3. I deleted my social .10 (.30) .33 (.48) .41 (.49) R-S >S .07 media 4. I made a joke about it .18 (.38) .30 (.10) .30 (1.0) N.S .04 SUPPORT-SEEKING STRATEGIES 5. I told a friend .10 (.30) .14 (.36) .28(.45) R-S >S,R .03 6. I told an adult at .54 (.52) .71 (.47) .67 (.48) N.S. .02 home REPORTING STRATEGIES 7. I told a teacher at .09 (.30) .05 (.22) .30 (.46) R-S>S,R .05 school 8. I reported it to the .27 (.46) .52 (.51) .51 (.50) N.S. .06 authorities AVOIDANCE STRATEGIES 9. I ignored it .10 (.30) .14 (36) .30 (.46) R-S>S .04 10. I used alcohol or .18 (.40) .33 (.48) .48 (50) R-S>S .05 drugs 11. I tried to get back at .14 (.39) .27 (.28) .44 (.50) R-S>S .06 them 12. I stayed offline .10 (.30) .05 (.22) .33 (.48) R-S>S,R .04 83 Table 13. Emotional Impact of Victimization Associated with Coping Strategies B SE β ƒ2 Race-Based Victimization .08 Constant 1.22 .07 Approach strategies -.03 .01 -.19* Support seeking -.05 .02 -.21* strategies Reporting strategies .04 .03 .18* Avoidance .00 .01 -.03 strategies Sex-Based Victimization .03 Constant 1.19 .06 Approach strategies .00 .02 -.12 Support seeking -.02 .03 -.18* strategies Reporting strategies .01 .05 .08 Avoidance .02 .04 .05 strategies Racialized-Sexualized Victimization .08 Constant 1.56 Approach strategies -.04 .03 -.19* Support seeking -.04 .02 -.23* strategies Reporting strategies .02 .05 .20* Avoidance .03 .02 .14 strategies 84 APPENDIX B: FIGURES 85 1.8 1.6 1.6 1.4 1.4 1.2 Emotional Impact Emotional Impact 1.2 1 1 Men 0.8 0.8 Men Women 0.6 0.6 Women 0.4 0.4 0.2 0.2 0 0 Support-Seeking Support-Seeking Reporting Low Reporting High Low High Figure 1. 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