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. Author manuscript; available in PMC: 2022 Jul 1.
Published in final edited form as: Cultur Divers Ethnic Minor Psychol. 2021 May 27;27(3):397–407. doi: 10.1037/cdp0000436

Victimized in Many Ways: Online and Offline Bullying/Harassment and Perceived Racial Discrimination in Diverse Racial-Ethnic Minority Adolescents

Mariani Weinstein 1, Michaeline Jensen 1, Brendesha Tynes 2
PMCID: PMC8754584  NIHMSID: NIHMS1712390  PMID: 34043397

Racial-ethnic minority youth are at risk for multiple types of victimization experiences. These victimization experiences can include bullying/harassment (e.g. insults, exclusion including repeated and power-imbalanced hostility) and also similar, racially charged experiences of racial-ethnic discrimination (e.g. insults or exclusion on the basis of one’s race or ethnicity). In a digitally connected era, these victimization experiences can occur both in traditional “offline” and increasingly ubiquitous “online” contexts. Offline and online bullying/harassment can overlap considerably in that people victimized in one context are more likely to be victimized in the other (Kowalski et al., 2014; Olweus & Limber, 2018). Similarly, many of the same youth report experiencing racial-ethnic discrimination in offline and online contexts (Tynes et al., 2014; Umaña-Taylor et al., 2015).

Negative Impacts of Victimization on Youth

These various types of victimization (offline and online bullying/harassment and perceived racial discrimination) are each consistently associated with negative developmental outcomes. Youth who experience more bullying/harassment offline (such as being verbally or physically attacked about some physical, mental, or social trait) are at heightened risk for worse mental, physical, and academic outcomes concurrently (Kowalski et al., 2014) and over time (Fahy et al., 2016) than youth who have experienced less victimization. In addition, these effects of online bullying/harassment occur over and above offline bullying/harassment both cross-sectionally (Bonanno & Hymel, 2013) and longitudinally (Cole et al., 2016; Cross et al., 2015; Holfeld & Mishna, 2019). Importantly, most youth who experience online bullying are also bullied in person, and these youth who face bullying in both contexts are more at risk for later distress (e.g. anxiety, depression) than peers who only experience offline bullying (Cross et al., 2015; Gini et al., 2018; Mitchell et al., 2007). Studies that compare the relative impact of offline and online bullying/harassment have found mixed results, with meta-analysis concluding that they are about equivalently harmful (Gini et al., 2018).

Similarly, for racial-ethnic discrimination, a large body of work has shown that youth who are victimized offline based on their race or ethnicity (e.g. treated with suspicion on the basis of race/ethnicity or called racially-charged derogatory names) are more likely to experience concurrent and later psychological, medical, and academic difficulties (Priest et al., 2013; Williams, 2018; Schmitt et al., 2014). There is also some evidence that the negative psychosocial outcomes associated with racial-ethnic discrimination may be particularly pronounced when discrimination persists or increases over time (Smith-Bynum et al., 2014; Stein et al., 2019). Online racial-ethnic discrimination has also been associated with negative mental health concurrently (Tynes et al., 2012, 2014; Umaña-Taylor et al., 2015) and with negative academic outcomes over time (Tynes et al., 2015). Some previous work has also shown negative effects of such online experiences over and above offline experiences (Tynes et al., 2008).

Multiple and Intersecting Forms of Victimization Experienced by Youth of Color

Of course, offline and online non-race related bullying also impact youth of color, and considering this association is important. Youth of color may encounter more in-person peer bullying than White youth (Peskin et al., 2006) although some studies have found lower rates (Merrill & Hanson, 2016). For instance, some research suggests that youth of color report being victims of online bullying as often (Hinduja & Patchin, 2008; Merrill & Hanson, 2016) or more often (Hong et al., 2016; Yousef & Bellamy, 2015) than White youth. Variation in rates may be related to contextual factors such as school racial composition as well as the small number of studies that examine prevalence of victimization by race (Kowalski et al., 2019). These experiences of multiple forms of victimization (along with other stressors that disproportionately impact racial-ethnic minority youth, including other culture-related stressors), likely have an aggregate detrimental impact (Steele, 2016; Vines et al., 2017; Williams, 2018). In addition, one recent study on a subsample of this same dataset examined the associations between offline and online racial-ethnic discrimination and found that offline racial-ethnic discrimination appears to precede online racial-ethnic discrimination in youth, rather than the other way around (Lozada et al., 2020). Thus, it is important to consider the co-occurrence and possible longitudinal transactions among types of victimization in youth development.

Perhaps surprisingly, given the qualitative similarities (and detrimental effects) between bullying/harassment and racial-ethnic discrimination, few studies have examined the intersections between bullying/harassment and racial-ethnic discrimination. Several studies have found cumulative harmful effects of bullying/harassment and race-based victimization (referred to in different literatures as racial-ethnic-based bullying/harassment or perceived racial-ethnic discrimination, potentially distinct but closely related constructs) on socioemotional and health outcomes (Hoglund & Hosan, 2013; Priest et al., 2019). A survey of diverse youth found that bias-based, including race-based, harassment was associated with negative mental health outcomes more than non-bias-based harassment (Jones et al., 2018). In a Latent Class Analysis of biased-based bullying, a racial discrimination class composed 33% of the sample, and additional youth faced racial discrimination in an intersectional discrimination class (7%) and sexual orientation discrimination class (7%) illustrating that many victimization experiences are shaped by race and other social identities (Garnett et al., 2014).

Given the evidence that these different types of victimization experiences (offline and online bullying/harassment and perceived racial/ethnic discrimination) have detrimental effects for youth, it is important to consider their co-occurrence over time. Most studies that consider multiple forms of victimization have been correlational (which limits our ability to understand directions of effects) and no study that we are aware of thus far has examined longitudinal relationships among bullying/harassment and perceived racial-ethnic discrimination over and above one another and over time. A better understanding of how these different types of online and offline victimization manifest over time in adolescence is necessary to inform effective preventive interventions aimed at these key risk factors in the digital age (when victimization may be occurring both in the online and offline spheres).

Present Study

Racial-ethnic discrimination has generally been examined separately from bullying/harassment, and the extensive literature on offline and online bullying/harassment does not often differentiate the content of offending messages, including those that may contain racial-ethnic-related insults. Both literatures, in parallel, have been exploring the co-occurrence of victimization in online and offline spaces, given the increasing importance of online contexts in the lives of diverse youth. This study aims to bridge the gap between these literatures by examining the concurrent and longitudinal associations among online and offline bullying/harassment and racial-ethnic discrimination in a large sample of racial-ethnic minority youth. Specifically, our study examined concurrent, longitudinal, bidirectional, and incremental associations among four types of victimization (online and offline bullying/harassment, online and offline racial-ethnic discrimination) in a single autoregressive cross-lagged panel model.

Based on the existing literature, we hypothesized: That youth who report experiencing one type of victimization are more likely to also report other types of victimization (Hypothesis 1a, multiple victimization) and that each type of victimization will evidence stability over time (Hypothesis 1b, stability). We expected that we would replicate the many studies that find concurrent victimization across offline and online contexts and extend this literature to show co-occurrence across content of the victimization (bullying/harassment versus perceived racial-ethnic discrimination) as others have begun to do with related constructs (e.g. co-occurrence of ethnic, relational, and physical victimization and race-based name calling and social exclusion; Hoglund & Hosan, 2013; Thijs & Verkuyten, 2002).

We also explored whether one or more types of victimization experiences predict other types longitudinally (reflecting compounding risk over time). We predicted that some longitudinal associations among different forms of victimization would emerge (Hypothesis 2). We wondered whether risk would be conferred across offline and online contexts (Question 2a) and across content of victimization (Question 2b). These associations could elucidate which types of victimization experiences may have down-stream effects of increasing or reducing other types of victimization and thus are the most efficient to target in prevention and intervention contexts.

Finally, this study examined a potential confounding factor that is important to take into consideration if we are to parse associations between types of online and offline victimization: time spent online. Some research suggests that time spent online may expose youth to more opportunities for online victimization (Hinduja & Patchin, 2008; Kokkinos et al., 2014; Merrill & Hanson, 2016). There is also some evidence to suggest that that the link between online victimization and negative outcomes is heightened among those youth who spend the most time online (Tynes et al., 2014). Interestingly, research suggests that youth who experience victimization can find valuable supports and resources online (Bastiaensens et al., 2019), and that deeper immersion in online settings may be related to efforts to cope with negative affect (Rideout & Fox, 2018). Our study of the interplay among different types of offline and online victimization must take into account the role of time online and is also well-positioned to contribute to our understanding of whether time online may be driving victimization or, rather, if time spent online is a result of earlier victimization experiences. Based on previous literature, we hypothesized that time online would be related to all types of victimization within wave (Hypothesis 3a). However, we expected this concurrence to reflect two different longitudinal associations: We hypothesized that youth who spend more time online would be more likely to report online victimization later (due to increased exposure, Hypothesis 3b), and that youth who experience more offline or online victimization might spend more time online later (Hypothesis 3c), reflecting the use of the internet as a coping mechanism and alternative social context to negative peer experiences.

Method

Sample and Procedure

The sample for the present study was drawn from the Teen Life Online and in Schools (TLOS, Tynes et al., 2014) project, a longitudinal study of the risk and protective factors associated with online racial discrimination in diverse youth. Given the focus of the present analysis on victims of racial-ethnic discrimination, the sample analyzed here comprises all students who endorsed non-White identities in response to “What racial or ethnic group best describes your cultural background?” (N = 735; Black or African American (46% of sample); Hispanic or Latino including Mexican American, Central American, and others (32% of sample); Biracial or Multiracial (10% of sample); Asian or Asian American including Chinese, Japanese, Korean, and others (8% of sample); American Indian, including Native Americans and others (3% of sample); South Asian (<1% of sample); or Middle Eastern (<1% of sample)). Students in the subsample were in grades 6–12 and ranged in age from 10–19 (M = 14.47, SD = 1.95) at wave 1 (54.3% female). The highest level of parental education (highest of mother or father education, our indicator of SES) was on average between high school and college (2.83 on a Likert scale where 2 indicates High School and 3 indicates College).

Three waves of online survey data were collected at middle and high schools in the Midwest from 2010 to 2014; waves were 10–12 months apart. Recruitment and data collection procedures for this sample are described in depth elsewhere (Tynes et al., 2014). Parental consent forms and fliers were distributed to approximately 150 students per school with copies available in English and Spanish. During distribution, research assistants gave a brief 10-minute presentation to the selected classes to describe the purpose of the study. Each year, researchers returned to each school to administer surveys via a web link to students whose parents had returned parental consent forms. Research assistants were present during survey administration to answer questions or assist with technical difficulties. Emails and fliers were sent to students to encourage retention. Students who graduated (8th and 12th graders) either were followed to their new schools or completed the survey online in their homes. Participants received $15 Amazon.com gift certificates for their participation during the first year, $20 during the second year, and $25 during the third year at each wave, and participating schools also received a small stipend. All procedures were approved by the Institutional Review Board at the Principal Investigator’s institution.

Measures

Offline bullying/harassment.

The University of Illinois Peer Victimization Scale (revised, 3 items; Espelage & Holt, 2001) was used to measure offline bullying/harassment. These items measure overt peer victimization (not relational victimization) and were designed for 8–18year-old youth. The question stem states, “for each of the following questions, choose how many times these things happened to you in the LAST 30 days”. The items include: “Other students picked on me,” “Other students called me names,” and “I got hit and pushed by other students.” The three items were averaged to yield a mean score at each wave (MW1 =0.67, SD= 0.89, α= .82; MW2 =0.56, SD= 0.83, α= .84; MW3 =0.49, SD= 0.74, α= .82). Response options were 0 = Never, 1 = 1–2 times, 2 = 3–4 times, 3 = 5–6 times, or 4 = 7 or more times. This measure captures youth victimization that is bullying (defined as repeated, power-imbalanced, hostile actions) and harassment (more generalized peer aggression), all of which have negative psychosocial outcomes although the bullying is more severe (Ybarra et al., 2014).

Online bullying/harassment.

The six-item Online Bullying and Harassment subscale from the Online Victimization Scale (Tynes et al., 2010) was used to measure respondents’ experiences as victims of online bullying/harassment. Youth were asked to “choose how many times these things happened to you online in the last year.” The 6 items included “People have said mean or rude things about the way that I talk (write) online,” “People have posted mean or rude things about me on the Internet,” “I have been harassed or bothered online because of something that happened at school,” “I have been embarrassed or humiliated online,” “I have been bullied (repeated name calling or harassment) online,” and “People have said mean or rude things about how I look, act, or dress online.” The six items were averaged to yield a mean score at each wave (MW1 =0.23, SD= 0.53, α= .83; MW2 =0.47, SD= 0.77, α= .90; MW3 =0.61, SD= 0.88, α= .92). Response options included 0= Never, Once, A Few Times a Year, A Few Times a Month, A Few Times a Week (no data for Y1) and 5 = Every day.

Offline racial-ethnic discrimination.

Adolescents’ racial-ethnic discrimination in offline settings was assessed using the Students in School (peers) subscale. This is a subscale of the Perceived Discrimination by Adults/Peers Scale, abbreviated (Way, 1997). Students responded to five items with the stem “How often do you feel that other students in school…” and followed by “think you WON’T know the answer in class because of your race or ethnicity,” “treat you with less respect because of your race or ethnicity,” “are suspicious of you because of your race or ethnicity,” “call you names because of your race or ethnicity,” and “treat you unfairly because of your race or ethnicity.” Students responded using a 5-point Likert scale anchored 0 = Never, Rarely, Sometimes, Often, or 4 = All the Time. The six were averaged to yield a mean score at each wave (MW1 =0.55, SD= 0.83, α= .90; MW2 =0.59, SD= 0.84, α= .92; MW3 =0.68, SD= 0.86, α=.92).

Online racial-ethnic discrimination.

Online racial-ethnic discrimination was assessed by the Individual Racial Discrimination Subscale of the Online Victimization Scale (Tynes, Rose, & Williams, 2010). This subscale assesses derogatory text, images, and symbols that directly target an individual because of his or her race and is distinct from the subscale that assesses online bullying/harassment of a general nature (described above). The individual online racial discrimination subscale consists of four-items ( “People have said mean or rude things about me because of my race or ethnic group online,” “People have shown me a racist image online,” “People have excluded me from a site because of my race or ethnic group online,” “People have threatened me online with violence because of my race or ethnic group”) that are coded on a 6-point Likert scale with response options 0= Never, Once, A Few Times a Year, A Few Times a Month, A Few Times a Week (no data for Y1) and 5 = Everyday. The items were averaged to yield a mean score at each wave (MW1 = 0.34, SD= 0.61, α=.72; MW2 =0.44, SD= 0.68, α= .73; MW3 =0.58, SD= 0.79, α= .77).

Time Online.

Time online was assessed by a single item developed for this dataset (Tynes et al., 2014): “How many hours are you online on a usual day when you use the Internet?” with response options including “0 hours”, “1 hour or less”, “1 to 2 hours”, “2 to 3 hours”, “3 to 4 hours”, “4 to 5 hours”, “5 to 6 hours”, “6 to 7 hours”, “7 to 8 hours”, and “8 or more hours” (MW1 =4.50, SD= 2.29; MW2 =4.60, SD= 2.35; MW3 =4.99, SD= 2.39). The mean of 4.50 at wave 1 can be interpreted as an average between “2 to 3 hours” and “3 to 4 hours” per day.

Covariates.

Covariates included at wave 1 were gender (54.3% female), age (M W1 = 14.47, SD = 1.95), parental education measured as the highest reported education among mother and father (as a proxy for SES; MW1= 2.83, SD= 0.85), and race/ethnicity (46% Black, 32% Latinx, 22% Other race/ethnicity). These covariates were selected because victimization has previously been shown to vary along these demographic factors (Hoglund & Hosan, 2013; Larochette et al., 2010).

Statistical Analysis

We tested study questions in a single auto-regressive cross-lagged panel model to examine concurrent and longitudinal associations between online and offline bullying/harassment, online and offline racial-ethnic discrimination, and time online across waves 1–3. We tested the cross-lagged panel model in MPlus (8.1, Muthén & Muthén, 1998–2017) using MLR estimation with standard errors robust to non-normality and controlling for relevant demographic covariates: gender, age, parental education, and dummy coded race/ethnicity. This allowed us to examine the incremental relationships of each of the variables over and above the others, controlling for salient covariates. Missing data was handled using full information maximum likelihood estimation (FIML), an efficient method for handling data in structural equation models and reducing the biases present in other missing data handling methods (Enders & Bandalos, 2001). FIML assumes that missing data are Missing at Random (MAR; Muthén et al., 1987).

Youth participants were retained at a rate of 62% from wave 1 (N= 735) to wave 2 (N= 459) and 80% from wave 2 to wave 3 (N= 367), meaning that 38% of the original sample was lost by wave 2 and 50% of the wave 1 sample was lost by wave 3. Attrition was examined by comparing wave 1 variables between those who did and did not complete wave 2 and wave 3. Participants who did not complete (vs. completed) wave 2 surveys did not differ on any primary study variables (offline bullying/harassment, online bullying/harassment, offline racial-ethnic discrimination, and online racial-ethnic discrimination) measured at wave 1, though those who completed wave 3 (compared to those who did not complete) were more likely to report higher levels of victimization at wave 1 (t(646.69) = −2.04; p = 0.04). Furthermore, those who completed (vs. did not complete) wave 2 were more likely to be Black (χ2wave2 (1, N = 735) = 18.38, p < 0.01) and those who completed wave 2 and wave 3 were less likely to be Latinx than those who did not complete waves 2 and 3 of data collection (χ2wave2 (1, N = 735) = 34.74, p < 0.01; wave 3: χ2wave3 (1, N = 735) = 10.21, p < 0.01). This over-representation of Latinx youth in missing data is likely because a predominately Latinx middle school did not agree to continue in the study after wave 1, representing 90 Latinx children who were missing at wave 2. Grade level differed among those who completed (vs. did not complete) wave 2 (t(600.31) = 1.74; p < 0.01) and wave 3 (t(729.42) = 3.95; p < 0.01) with more youth from earlier grades missing, potentially due to the transition to high school or loss of the predominantly Latinx middle school. The parents of adolescents who completed (vs. did not complete) wave 2 (t(307.25) = 2.24; p = 0.03) and wave 3 (t(517.03)= −2.28, p= 0.02) interviews had slightly more education on average. Due to these attrition differences, we include in the model relevant covariates as well as a single binary auxiliary variable for the school that was lost to follow up, in order to better satisfy the requirements for MAR data. As a sensitivity test, we also ran the model without the students from the school lost to follow up; the results had the same pattern and very similar magnitude of associations (i.e. the same significant paths emerged and standardized path coefficients were within 0.02 from the model without this exclusion).

Results

The model and significant paths are depicted in Figure 1. Table 1 includes all standardized path coefficients, confidence intervals, and p values. The model was a good fit to the data (χ2 (190) = 2234.426, p < .01; RMSEA = .058[(90% C.I.: .045 - .072]; CFI = .97; SRMR = 0.03). Consistent with our predictions, victimizations co-occurred with one another at each wave (Hypothesis 1a confirmed). Consistent with past research, youth who were bullied/harassed offline also reported more experiences of being bullied/harassed online, and youth who were discriminated against offline also reported more experiences of discrimination online, as evidenced in significant within-wave 1 covariances (depicted for wave 1 with curved arrows in Figure 1; wave 2 and 3 residual covariances can be found in Table 2). Extending previous literature, we found that youth who were bullied/harassed (online or offline) also tended to report more experiences of online and offline racial-ethnic discrimination within the same wave. Each form of victimization in this study showed considerable stability over time (Hypothesis 1b confirmed) which suggests that youth who experience a certain type of victimization are likely to continue being victimized in this way in the future.

Figure 1.

Figure 1.

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Concurrent and Longitudinal Associations among Multiple Victimizations and Time Online in Adolescents Over Three Waves. Dark lines indicate statistically significant paths (p ≤ .05). Standardized regression coefficients depicted. Full results with exact p values can be found in Table 1. Residual covariances among Wave 2 and Wave 3 variables modeled but not depicted (see Table 2 for full residual covariance results).

Table 1.

Cross Lagged Panel Model Results

Wave 2
Bullying/Harassment
R2=.25		 Online Bullying/Harassment
R2=.20		 Racial -Ethnic Discrimination
R2=.16		 Online Racial -Ethnic Discrimination
R2=.12		 Time Online
R2=.30
95% CI
 95% CI
 95% CI
 95% CI
 95% CI
β LL UL p β LL UL p β LL UL p β LL UL p β LL UL p
Wave 1
 Bullying/Harassment .40 .29 .52 <.01 .12 .01 .23 .03 .13 .03 .24 .01 .08 −.03 .18 .15 .14 .03 .25 .01
 Online Bullying/Harassment .10 −.02 .21 .10 .30 .15 .44 <.01 −.11 −.23 .01 .09 .02 −.10 .15 .73 .07 −.02 .16 .12
 Racial Discrimination .03 −.07 .13 .59 .04 −.06 .13 .49 .25 .14 .35 <.01 .03 −.08 .14 .56 −.03 −.13 .07 .52
 Online Racial/Ethnic Discrimination .01 −.12 .13 .91 .04 −.08 .16 .48 .12 −.02 .26 .08 .23 .08 .39 <.01 −.07 −.17 .03 .17
 Time Online .02 −.07 .10 .70 .04 −.05 .13 .35 −.05 −.14 .04 .30 −.02 −.11 .07 .61 .45 .36 .53 <.01
 Black .11 .01 .20 .04 .06 −.03 .16 .18 .12 .02 .22 .01 .02 −.08 .11 .72 .05 −.04 .14 .26
 Latinx −.04 −.15 .07 .45 −.06 −.15 .03 .20 .02 −.08 .13 .65 −.06 −.17 .04 .21 .01 −.10 .11 .92
 Female −.06 −.14 .02 .16 −.01 −.10 .07 .74 −.11 −.19 −.02 .01 −.05 −.13 .04 .28 .01 −.06 .09 .73
 Age −.06 −.15 .02 .14 .04 −.06 .13 .44 .03 −.06 .13 .49 .11 .02 .21 .02 .19 .11 .27 <.01
 Parental Education −.06 −.17 .06 .32 .04 −.05 .13 .42 .06 −.05 .16 .29 −.01 −.11 .09 .89 −.02 −.12 .08 .64
Wave 3
Bullying/Harassment
R2=.18		 Online Bullying/Harassment
R2=.31		 Racial -Ethnic Discrimination
R2=.22		 Online Racial -Ethnic Discrimination
R2=.27		 Time Online
R2=.22
95% CI
 95% CI
 95% CI
 95% CI
 95% CI
Wave 2 β LL UL p β LL UL p β LL UL p β LL UL p β LL UL p
 Bullying/Harassment .31 .19 .42 <.01 .06 −.05 .17 .26 .03 −.08 .15 .60 .06 −.06 .18 .33 −.02 −.11 .07 .66
 Online Bullying/Harassment .18 .01 .35 .04 .44 .30 .58 <.01 .05 −.08 .17 .45 .12 −.02 .25 .09 −.02 −.14 .10 .72
 Racial Discrimination .05 −.07 .17 .38 .11 −.01 .23 .08 .33 .21 .45 <.01 .10 −.03 .23 .13 −.03 −.13 .08 .62
 Online Racial/Ethnic Discrimination −.10 −.25 .05 .18 <.01 −.14 .14 .99 .09 −.05 .23 .20 .27 .13 .41 <.01 .10 −.03 .23 .13
 Time Online .05 −.08 .17 .45 .02 −.09 .13 .68 .02 −.10 .13 .79 −.01 −.13 .11 .88 .42 .31 .53 <.01
 Black .01 −.11 .12 .91 −.02 −.13 .09 .72 .08 −.04 .19 .21 .06 −.04 .17 .26 .03 −.08 .14 .59
 Latinx .02 −.10 .14 .72 −.07 −.19 .05 .25 −.04 −.16 .07 .47 .01 −.10 .13 .82 −.06 −.19 .07 .37
 Female −.06 −.16 .04 .26 −.01 −.10 .08 .91 −.08 −.18 .01 .09 −.05 −.15 .04 .27 .01 −.09 .10 .89
 Age <.01 −.10 .10 .99 .11 .02 .20 .02 .06 −.05 .16 .31 .11 .01 .21 .03 .04 −.06 .14 .46
 Parental Education .06 −.05 .16 .27 −.08 −.19 .03 .18 −.02 −.14 .10 .70 −.07 −.18 .05 .27 −.06 −.19 .07 .36
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Note. β= standardized regression coefficients.

Table 2.

Covariances for Cross Lagged Panel Model

Bullying/Harassment Online Bullying/Harassment Racial -Ethnic Discrimination Online Racial-Ethnic Discrimination
95% CI
 95% CI
 95% CI
 95% CI
ψ LL UL p ψ LL UL p ψ LL UL p ψ LL UL p
Wave 2
 Bullying/Harassment
 Online Bullying/Harassment .33 .24 .42 <.01
 Racial Discrimination .30 .19 .41 <.01 .28 .18 .39 <.01
 Online Racial Discrimination .29 .18 .39 <.01 .52 .41 .64 <.01 .35 .24 .46 <.01
 Time Online .02 −.08 .11 .73 .12 <0.01 .23 .04 .15 .05 .25 <.01 .10 −.02 .21 .09
Wave 3
 Bullying/Harassment
 Online Bullying/Harassment .40 .28 .52 <.01
 Racial Discrimination .34 .22 .47 <.01 .21 .11 .32 <.01
 Online Racial Discrimination .39 .24 .54 <.01 .50 .37 .64 <.01 .23 .10 .36 <.01
 Time Online .09 −.02 .21 .10 .06 −.05 .18 .27 .01 −.11 .12 .89 .17 .06 .28 <.01
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Note. ψ = residual covariances among wave 2 and 3 endogenous variables

Few cross-lagged longitudinal associations emerged, though those that did support to some extent Hypothesis 2 that victimization types can predict one another over time. Youth who reported more offline bullying/harassment at wave 1 were more likely to report experiencing more online bullying/harassment and more offline racial discrimination at wave 2. This suggests that being a victim of offline bullying/harassment may predict risk for other forms of victimization, across offline/online contexts (affirming Question 1) and across general/racial-ethnic content of victimization (affirming Question 2). For all other possible associations, teens’ previous experiences with different types of victimization were not consistently related to later experiences of other types of victimization.

We saw that youth who reported spending more time online also tended to report more online and offline victimization experiences within-wave 1 (Hypothesis 3a). Longitudinally, time online was not predictive of any type of later victimization experience, contrary to our hypothesis (3c). However, supporting to an extent our predictions (Hypothesis 3b), there was some evidence that victimization predicts time online: those youth who reported more wave 1 offline bullying/harassment experiences reported spending more time online at wave 2.

A cross-lag panel model offers built-in opportunity for replication. We expected that longitudinal associations from wave 1 to wave 2 would replicate in wave 2 to wave 3. However, only one longitudinal association from wave 2 to 3 emerged: youth who reported more wave 2 online bullying/harassment reported higher levels of wave 3 offline bullying/harassment. This was a unique association, not found in waves 1 to 2.

Discussion

Racial-ethnic minority youth are at risk for multiple types of victimization in the digital age. The present study sought to investigate the ways in which online and offline bullying/harassment and racial-ethnic discrimination relate to one another over the course of three years in adolescence. Overall, our results suggest that there is substantial co-occurrence of offline bullying/harassment, online bullying/harassment, offline racial-ethnic discrimination, online racial-ethnic discrimination, and time spent online. This is the first study to demonstrate this pattern of multi-victimization across settings (off and online) and content (general and racially/ethnically targeted), and is consistent with past literature showing that those youth who are victimized offline are more likely to be victimized online (Kowalski et al., 2014; Schneider et al., 2012) and that those youth who experience general bullying/harassment are also more likely to report experiencing racially-targeted discrimination (Priest et al., 2016). We also saw that those youth who spend more time online tend to report higher levels of all types of victimization experiences (not just online victimization as we hypothesized).

Despite moderate within-wave associations (with standardized coefficients ranging from 0.25 to 0.46, with stronger associations between victimization in the same context—online or offline—or of the same content—bullying/harassment or racial-ethnic discrimination), our results do not suggest a consistent pattern of mutual influence among types of victimization over time. Rather, we saw few longitudinal linkages among different types of victimization, with two exceptions. First, offline bullying/harassment at wave 1 was associated with increased risk for experiencing both online bullying/harassment and offline racial-ethnic discrimination at wave 2. Second, online bullying/harassment was associated with increased risk for experiencing offline bullying/harassment at wave 3, though neither of these associations replicated across waves. These findings suggest that offline bullying/harassment in particular may serve as a risk factor for later experiences of online bullying/harassment and offline racial-ethnic discrimination. We suspect that this longitudinal association could be due to two different processes. It could be that earlier offline bullying/harassment experiences sensitize youth to negative peer interactions in such a way that it increases their likelihood of reporting later victimization experiences (i.e. the youth is more vigilant and/or begins to make more hostile attributions), which is consistent with some findings on sensitization literatures in both bullying/victimization and racial-ethnic discrimination (see Lansford et al., 2010 for an information processing view in a younger group; Wu et al., 2015 in workplace adults). Alternatively, it could be that youth who experience early offline bullying/harassment change peer groups over time (Dishion & Patterson, 2006; Sasson & Mesch, 2017) and begin to affiliate with new online and offline social networks within which they are more likely to be victimized in the future. A direction for future research is to better elucidate the mechanisms underlying these longitudinal pathways.

Regarding time online, our results suggest that time spent online does not increase an adolescent’s risk for later victimization experiences. This may be surprising to parents who see limiting time online as a way to potentially protect their youth from negative online experiences (George & Odgers, 2015). However surprising, this finding is quite consistent with a growing body of literature which suggests that quantity of time spent online is not as important for youth social and developmental wellbeing as the quality and nature of these online experiences (Jensen et al., 2019; Odgers & Jensen, 2020). Although our results do not suggest that time spent online predisposes youth for later victimization experiences, we did find some evidence that earlier experiences with offline bullying/harassment are associated with later time spent online. This is consistent with our hypothesis and some studies which suggest that youth who suffer from negative affect and peer interactions may seek out alternate social niches and social support online (Bastiaensens et al., 2019; Rideout & Fox, 2018). While addressing time online in itself is not a good target of prevention for parents based on these results, other studies have suggested that certain types of parental engagement with youth online activities are protective against online victimization; for example, one study found a small benefit of parental monitoring, and a larger protective influence of collaborative parental strategies (Elsaesser et al., 2017).

Limitations

Our study bridges several rich literatures on online/offline bullying/harassment and racial/ethnic discrimination experiences. Our data, which were collected over the course of three years in a large, diverse sample of adolescents who are likely at increased risk for both victimization and its downstream consequences, have yielded important information about the ways that different types of victimization manifest over time and the role of time spent online as a consequence of offline victimization experiences but not as a risk factor for later victimization.

Despite its strengths, our study also has a number of limitations. First, although we specifically targeted adolescence as a sensitive period for peer victimization and time online (Rideout & Robb, 2019), our study design (which assessed participants ranging from 10–19 at wave 1) does not easily lend itself to examination of the role of age and developmental timing of discrimination experiences. That is, our findings here elucidate the role of earlier victimization experiences in impacting later victimization experiences and time online (controlling for age), but do not yield useful information about whether the age at which victimization experiences occur may be important in how victimization experiences co-occur over the course of adolescent development. We plan in future research to utilize alternate methods (i.e. cohort sequential designs, Prinzie & Onghena, 2005) which would enable us to parse these important developmental questions around timing of victimization (Zeiders et al., 2013). Indeed, future research should also consider whether a one-year lag is the appropriate time scale at which we expect these processes to emerge, or if rather, consistent with emerging research (English et al., 2020) victimization and its consequences may be best understood on a more micro (e.g. daily) level. The historical timing of our study must also be taken into account; this data speaks to the co-occurrence of online and offline victimization experiences from 2010–2014, and it is important to consider whether these associations would hold true today in our ever-evolving digital world and in light of the racial climate in this time period (and since). For instance, the Midwestern youth participants in this study were likely exposed to salient race-related events occurring during this time period (i.e. Michael Brown’s murder and the associated unrest in Fergusson, MO in 2014; the rise of the Black Lives Matter Movement). It is likely that this climate impacted the nature and frequency of victimization experiences experienced by our participants in ways we did not assess.

Second, the nature of our measurement of offline and online bullying/harassment may obscure some nuances. Although it is strengthened by using behavioral examples of bullying/harassment experiences rather than definitional questions which are harder to accurately respond to (Sawyer et al., 2008), our measure could potentially be picking up both “general” (non-racially targeted) bullying/harassment as well as racially-targeted bullying/harassment that are reported in response to both the bullying/harassment questions as well as the racial/ethnic discrimination questions. Future qualitative studies should examine the extent to which youth perceive these experiences as distinct and should use event-based measurement to better understand whether the within-wave co-occurrence is due to experiencing more distinct victimization events or to perceiving (and reporting) the same events as both types of victimization.

Third, our study focuses solely on victimization experiences (to the exclusion of perpetration experiences), despite the fact that much of the recent literature highlights that the roles of bully/perpetrator and victim are not always clear cut, including among racial/ethnic minority youth (Larochette et al., 2010; Peskin et al., 2006). Literature consistently documents overlap among youth who are both victims and perpetrators in a single (online or offline) context or across contexts, and some differences in risks and outcomes for this bully-victim class (e.g. Espelage et al., 2012; Schultze-Krumbholz et al., 2012). Future research ought to consider the ways in which these different roles may co-occur or change over time. We also do not know whether the youth in our sample were victimized by White youth or other youth of color, and future research should examine whether this would moderate impacts of victimization.

Implications

This study highlights the importance of considering multiple forms of victimization among racial-ethnic minority youth, both in research (which has considered bullying/harassment and racial-ethnic discrimination in largely siloed ways) and in intervention/prevention contexts, where it may be particularly important to consider the fact that racial-ethnic minority youth may be experiencing and being impacted by multiple types of victimization simultaneously. Further research should elucidate ways to minimize risks and increase protective factors for youth of color against the many concurrent and long-term harms caused by racial discrimination (Stewart et al., 2019) and bullying/harassment that youth of color also face (Kowalski et al., 2019). Systemic solutions are necessary to minimize the cumulative and compounding strains faced by youth (Williams, 2018). Everyday factors that contribute to many forms of resilience—social support from family and peers, guidance from parents and other adult mentors— also may help with these multiple stressors and threats to youth development (Masten, 2001). This study suggests that an intervention that targets general bullying/harassment may protect racial-ethnic minority youth from future experiences of victimization, consistent with the effectiveness of interventions that target skills applicable to multiple types of victimization (Durlak et al., 2011). When youth in the school environment learn principles of human dignity and respect that apply to all and effective, prosocial strategies for relating with peers and resolving conflict, this may prevent not just racial-ethnic discrimination but other forms of injustice, maltreatment, and hatred, and these benefits may persist over time.

Public Significance:

We examined multiple types of victimization and time spent online at three yearly surveys in racial/ethnic minority adolescents. We found that youth simultaneously experienced bullying/harassment and racial/ethnic discrimination both offline and online, they continued to be victimized in the same way over multiple years, those who reported experiencing more offline bullying/harassment at the first survey reported more offline discrimination and more online bullying/harassment the next wave, and time online was related concurrently, but not over time, to victimization. Our results suggest that interventions for offline bullying/harassment might prevent other types of victimization later but simply reducing time online does not appear to be an effective strategy for protecting adolescents from victimization in a digital age.

Funding:

Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under Award R01HD061584 (PI: Brendesha M. Tynes). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. While working on this article, Mariani Weinstein was supported by the Center for Empowered Learning and Development with Technology (CELDTech) Paper Camp at University of Southern California, and by the UNC Greensboro Clinical Psychology Running Start program.

References

  1. Baldry AC, Farrington DP, & Sorrentino A (2017). School Bullying and Cyberbullying Among Boys and Girls: Roles and Overlap. Journal of Aggression, Maltreatment and Trauma, 26(9), 937–951. 10.1080/10926771.2017.1330793 [DOI] [Google Scholar]
  2. Bastiaensens S, Van Cleemput K, Vandebosch H, Poels K, DeSmet A, & De Bourdeaudhuij I (2019). “Were You Cyberbullied? Let Me Help You.” Studying.Adolescents’ Online Peer Support of Cyberbullying Victims Using Thematic Analysis of Online Support Group Fora. In Narratives in Research and Interventions on Cyberbullying among Young People (pp. 95–112). Springer International Publishing. 10.1007/978-3-030-04960-7_7 [DOI] [Google Scholar]
  3. Bonanno RA, & Hymel S (2013). Cyber Bullying and Internalizing Difficulties: Above and Beyond the Impact of Traditional Forms of Bullying. Journal of Youth and Adolescence, 42(5), 685–697. 10.1007/s10964-013-9937-1 [DOI] [PubMed] [Google Scholar]
  4. Cole DA, Zelkowitz RL, Nick E, Martin NC, Roeder KM, Sinclair-McBride K, & Spinelli T (2016). Longitudinal and Incremental Relation of Cybervictimization to Negative Self-Cognitions and Depressive Symptoms in Young Adolescents. Journal of Abnormal Child Psychology, 44(7), 1321–1332. 10.1007/s10802-015-0123-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cross D, Lester L, & Barnes A (2015). A longitudinal study of the social and emotional predictors and consequences of cyber and traditional bullying victimisation. International Journal of Public Health, 60(2), 207–217. 10.1007/s00038-015-0655-1 [DOI] [PubMed] [Google Scholar]
  6. Dishion T, & Patterson G (2006). The Development and Ecology of Antisocial Behavior in Children and Adolescents.
  7. Durlak JA, Weissberg RP, Dymnicki AB, Taylor RD, & Schellinger KB (2011). The Impact of Enhancing Students’ Social and Emotional Learning: A Meta-Analysis of School-Based Universal Interventions. Child Development, 82(1), 405–432. 10.1111/j.1467-8624.2010.01564.x [DOI] [PubMed] [Google Scholar]
  8. Elsaesser C, Russell B, Ohannessian CMC, & Patton D (2017). Parenting in a digital age: A review of parents’ role in preventing adolescent cyberbullying. In Aggression and Violent Behavior (Vol. 35, pp. 62–72). Elsevier Ltd. 10.1016/j.avb.2017.06.004 [DOI] [Google Scholar]
  9. Enders CK, & Bandalos DL (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8(3), 430–457. 10.1207/S15328007SEM0803_5 [DOI] [Google Scholar]
  10. English D, Lambert SF, Tynes BM, Bowleg L, Zea MC, & Howard LC (2020). Daily multidimensional racial discrimination among Black U.S. American adolescents. Journal of Applied Developmental Psychology, 66(December 2019), 101068. 10.1016/j.appdev.2019.101068 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Espelage DL, & Holt MK (2001). Bullying and victimization during early adolescence: Peer influences and psychosocial correlates. Bullying Behavior: Current Issues, Research, and Interventions, 2(2), 123–142. [Google Scholar]
  12. Espelage DL, Rao MA, & Craven RG (2012). Theories of cyberbullying. Principles of Cyberbullying Research: Definitions, Measures, and Methodology, 49–67. 10.4324/9780203084601 [DOI] [Google Scholar]
  13. Fahy AE, Stansfeld SA, Smuk M, Smith NR, Cummins S, & Clark C (2016). Longitudinal Associations Between Cyberbullying Involvement and Adolescent Mental Health. Journal of Adolescent Health, 59(5), 502–509. 10.1016/j.jadohealth.2016.06.006 [DOI] [PubMed] [Google Scholar]
  14. Garnett BR, Masyn KE, Austin SB, Miller M, Williams DR, & Viswanath K (2014). The Intersectionality of Discrimination Attributes and Bullying Among Youth: An Applied Latent Class Analysis. Journal of Youth and Adolescence. 10.1007/s10964-013-0073-8 [DOI] [PubMed] [Google Scholar]
  15. George MJ, & Odgers CL (2015). Seven Fears and the Science of How Mobile Technologies May Be Influencing Adolescents in the Digital Age. Perspectives on Psychological Science, 10(6), 832–851. 10.1177/1745691615596788 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gini G, Card NA, & Pozzoli T (2018). A meta-analysis of the differential relations of traditional and cyber-victimization with internalizing problems. Aggressive Behavior, 44(2), 185–198. 10.1002/ab.21742 [DOI] [PubMed] [Google Scholar]
  17. Hinduja S, & Patchin JW (2008). Cyberbullying: An Exploratory Analysis of Factors Related to Offending and Victimization. Deviant Behavior, 29(2), 129–156. 10.1080/01639620701457816 [DOI] [Google Scholar]
  18. Hoglund WLG, & Hosan NE (2013). The Context of Ethnicity: Peer Victimization and Adjustment Problems in Early Adolescence. Journal of Early Adolescence, 33(5), 585–609. 10.1177/0272431612451925 [DOI] [Google Scholar]
  19. Holfeld B, & Mishna F (2019). Internalizing Symptoms and Externalizing Problems: Risk Factors for or Consequences of Cyber Victimization? Journal of Youth and Adolescence, 48(3), 567–580. 10.1007/s10964-018-0974-7 [DOI] [PubMed] [Google Scholar]
  20. Hong JS, Lee J, Espelage DL, Hunter SC, Patton DU, & Rivers T (2016). Understanding the correlates of face-to-face and cyberbullying victimization among u.s. Adolescents: A social-ecological analysis. Violence and Victims, 31(4), 638–663. 10.1891/0886-6708.VV-D-15-00014 [DOI] [PubMed] [Google Scholar]
  21. Jensen M, George MJ, Russell MR, & Odgers CL (2019). Young Adolescents’ Digital Technology Use and Mental Health Symptoms: Little Evidence of Longitudinal or Daily Linkages. Clinical Psychological Science, 7(6), 1416–1433. 10.1177/2167702619859336 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Jones LM, Mitchell KJ, Turner HA, & Ybarra ML (2018). Characteristics of bias-based harassment incidents reported by a national sample of U.S. adolescents. Journal of Adolescence, 65(August 2017), 50–60. 10.1016/j.adolescence.2018.02.013 [DOI] [PubMed] [Google Scholar]
  23. Kokkinos CM, Antoniadou N, & Markos A (2014). Cyber-bullying: An investigation of the psychological profile of university student participants. Journal of Applied Developmental Psychology, 35(3), 204–214. 10.1016/j.appdev.2014.04.001 [DOI] [Google Scholar]
  24. Kowalski RM, Giumetti GW, Schroeder AN, & Lattanner MR (2014). Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 140(4), 1073–1137. 10.1037/a0035618 [DOI] [PubMed] [Google Scholar]
  25. Kowalski RM, Limber SP, & McCord A (2019). A developmental approach to cyberbullying: Prevalence and protective factors. Aggression and Violent Behavior, 45(November 2017), 20–32. 10.1016/j.avb.2018.02.009 [DOI] [Google Scholar]
  26. Lansford JE, Malone PS, Dodge KA, Pettit GS, & Bates JE (2010). Developmental cascades of peer rejection, social information processing biases, and aggression during middle childhood. Development and Psychopathology, 22(3), 593–602. 10.1017/S0954579410000301 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Larochette AC, Murphy AN, & Craig WM (2010). Racial bullying and victimization in Canadian school-aged children: Individual and school level effects. School Psychology International, 31(4), 389–408. 10.1177/0143034310377150 [DOI] [Google Scholar]
  28. Lozada FT, Seaton EK, Williams CD, & Tynes BM (2020). Exploration of bidirectionality in African American and Latinx adolescents’ offline and online ethnic-racial discrimination. Cultural Diversity and Ethnic Minority Psychology. 10.1037/cdp0000355 [DOI] [PubMed] [Google Scholar]
  29. Masten AS (2001). Ordinary magic: Resilience processes in development. American Psychologist, 56(3), 227–238. 10.1037/0003-066X.56.3.227 [DOI] [PubMed] [Google Scholar]
  30. Merrill RM, & Hanson CL (2016). Risk and protective factors associated with being bullied on school property compared with cyberbullied. BMC Public Health. 10.1186/s12889-016-2833-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Mitchell KJ, Ybarra M, & Finkelhor D (2007). The relative importance of online victimization in understanding depression, delinquency, and substance use. Child Maltreatment, 12(4), 314–324. 10.1177/1077559507305996 [DOI] [PubMed] [Google Scholar]
  32. Muthén B, Kaplan D, & Hollis M (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52(3), 431–462. 10.1007/BF02294365 [DOI] [Google Scholar]
  33. Muthén L, & Muthén B (1998– 2017). MPlus user’ guide.
  34. Odgers CL, & Jensen MR (2020). Annual Research Review: Adolescent mental health in the digital age: facts, fears, and future directions. Journal of Child Psychology and Psychiatry, jcpp.13190. 10.1111/jcpp.13190 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Olweus D, & Limber SP (2018). Some problems with cyberbullying research. Current Opinion in Psychology, 19, 139–143. 10.1016/j.copsyc.2017.04.012 [DOI] [PubMed] [Google Scholar]
  36. Peskin MF, Tortolero SR, & Markham CM (2006). Bullying and victimization among black and hispanic adolescents. Adolescence. [PubMed]
  37. Priest N, Kavanagh A, Bécares L, & King T (2019). Cumulative Effects of Bullying and Racial Discrimination on Adolescent Health in Australia. Journal of Health and Social Behavior, 60(3), 344–361. 10.1177/0022146519868847 [DOI] [PubMed] [Google Scholar]
  38. Priest N, King T, Bécares L, & Kavanagh AM (2016). Bullying Victimization and Racial Discrimination Among Australian Children. American Journal of Public Health, 106(10), 1882–1884. 10.2105/AJPH.2016.303328 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Priest N, Paradies Y, Trenerry B, Truong M, Karlsen S, & Kelly Y (2013). A systematic review of studies examining the relationship between reported racism and health and wellbeing for children and young people. Social Science and Medicine. 10.1016/j.socscimed.2012.11.031 [DOI] [PubMed] [Google Scholar]
  40. Prinzie P, & Onghena P (2005). Cohort Sequential Design. In Encyclopedia of Statistics in Behavioral Science. John Wiley & Sons, Ltd. 10.1002/0470013192.bsa110 [DOI] [Google Scholar]
  41. Rideout VJ, & Fox S (2018). Digital Health Practices, Social Media Use, and Mental Well-Being Among Teens and Young Adults in the U.S. 1–95. https://www.hopelab.org/reports/pdf/a-national-survey-by-hopelab-and-well-being-trust-2018.pdf
  42. Rideout VJ, & Robb M (2019). THE COMMON SENSE CENSUS: MEDIA USE BY TWEENS AND TEENS. In Intergovernmental Panel on Climate Change (Ed.), Climate Change 2013 - The Physical Science Basis (pp. 1–30). Cambridge University Press. 10.1017/CBO9781107415324.004 [DOI] [Google Scholar]
  43. Sasson H, & Mesch G (2017). The Role of Parental Mediation and Peer Norms on the Likelihood of Cyberbullying. Journal of Genetic Psychology, 178(1), 15–27. 10.1080/00221325.2016.1195330 [DOI] [PubMed] [Google Scholar]
  44. Sawyer AL, Bradshaw CP, & O’Brennan LM (2008). Examining Ethnic, Gender, and Developmental Differences in the Way Children Report Being a Victim of “Bullying” on Self-Report Measures. Journal of Adolescent Health. 10.1016/j.jadohealth.2007.12.011 [DOI] [PubMed] [Google Scholar]
  45. Schmitt MT, Postmes T, Branscombe NR, & Garcia A (2014). The consequences of perceived discrimination for psychological well-being: A meta-analytic review. Psychological Bulletin. 10.1037/a0035754 [DOI] [PubMed] [Google Scholar]
  46. Schneider SK, O’Donnell L, Stueve A, & Coulter RWS (2012). Cyberbullying, School Bullying, and Psychological Distress: A Regional Census of High School Students. American Journal of Public Health, 102(1), 171–177. 10.2105/AJPH.2011.300308 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Schultze-Krumbholz A, Jäkel A, Schultze M, & Scheithauer H (2012). Emotional and behavioural problems in the context of cyberbullying: A longitudinal study among German adolescents. Emotional and Behavioural Difficulties, 17(3–4), 329–345. 10.1080/13632752.2012.704317 [DOI] [Google Scholar]
  48. Smith-Bynum MA, Lambert SF, English D, & Ialongo NS (2014). Associations between trajectories of perceived racial discrimination and psychological symptoms among African American adolescents. Development and Psychopathology, 26(4), 1049–1065. 10.1017/S0954579414000571 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Steele JL (2016). Race and General Strain Theory: Examining the Impact of Racial Discrimination and Fear on Adolescent Marijuana and Alcohol Use. Substance Use and Misuse, 51(12), 1637–1648. 10.1080/10826084.2016.1191513 [DOI] [PubMed] [Google Scholar]
  50. Stein GL, Castro-Schilo L, Cavanaugh AM, Mejia Y, Christophe NK, & Robins R (2019). When Discrimination Hurts: The Longitudinal Impact of Increases in Peer Discrimination on Anxiety and Depressive Symptoms in Mexican-origin Youth. Journal of Youth and Adolescence 10.1007/s10964-019-01012-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Stewart A, Schuschke J, & Tynes B (2019). Online Racism: Adjustment and Protective Factors Among Adolescents of Color. In Handbook of Children and Prejudice (pp. 501–513). Springer International Publishing. 10.1007/978-3-030-12228-7_28 [DOI] [Google Scholar]
  52. Thijs J, & Verkuyten M (2002). Racist victimization among children in The Netherlands: the effect of ethnic group and school. Ethnic and Racial Studies, 25(2), 310–331. 10.1080/0141987012010950 [DOI] [Google Scholar]
  53. Tynes BM, Giang MT, Williams DR, & Thompson GN (2008). Online Racial Discrimination and Psychological Adjustment Among Adolescents. Journal of Adolescent Health, 43(6), 565–569. 10.1016/j.jadohealth.2008.08.021 [DOI] [PubMed] [Google Scholar]
  54. Tynes BM, & Mitchell KJ (2014). Black Youth Beyond the Digital Divide: Age and Gender Differences in Internet Use, Communication Patterns, and Victimization Experiences. Journal of Black Psychology, 40(3), 291–307. 10.1177/0095798413487555 [DOI] [Google Scholar]
  55. Tynes BM, Rose, Chad A, & Williams DR (2010). The development and validation of the online victimization scale for adolescents. Cyberpsychology: Journal of Psychosocial Research on Cyberspace. http://web.b.ebscohost.com.libproxy.uncg.edu/ehost/pdfviewer/pdfviewer?vid=1&sid=9f8672a2-72d3-40fa-81e6-64cb0beaa9c7%40pdc-v-sessmgr06 [Google Scholar]
  56. Tynes BM, Rose CA, Hiss S, Umaña-Taylor AJ, Mitchell K, & Williams D (2014). Virtual Environments, Online Racial Discrimination, and Adjustment among a Diverse, School-Based Sample of Adolescents. International Journal of Gaming and Computer-Mediated Simulations, 6(3), 1–16. 10.4018/ijgcms.2014070101 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Tynes BM, Toro J. Del, & Lozada FT (2015). An unwelcomed digital visitor in the classroom: The longitudinal impact of online racial discrimination on academic motivation. School Psychology Review, 44(4), 407–424. 10.17105/SPR-15-0095.1 [DOI] [Google Scholar]
  58. Tynes BM, Umaña-Taylor AJ, Rose CA, Lin J, & Anderson CJ (2012). Online racial discrimination and the protective function of ethnic identity and self-esteem for African American adolescents. Developmental Psychology, 48(2), 343–355. 10.1037/a0027032 [DOI] [PubMed] [Google Scholar]
  59. Umaña-Taylor AJ, Tynes BM, Toomey RB, Williams DR, & Mitchell KJ (2015). Latino adolescents’ perceived discrimination in online and offline settings: An examination of cultural risk and protective factors. Developmental Psychology, 51(1), 87–100. 10.1037/a0038432 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Vines AI, Ward JB, Cordoba E, & Black KZ (2017). Perceived Racial/Ethnic Discrimination and Mental Health: a Review and Future Directions for Social Epidemiology. Current Epidemiology Reports, 4(2), 156–165. 10.1007/s40471-017-0106-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Vitoroulis I, & Georgiades K (2017). Bullying among immigrant and non-immigrant early adolescents: School- and student-level effects. Journal of Adolescence. 10.1016/j.adolescence.2017.10.008 [DOI] [PubMed] [Google Scholar]
  62. Way N (1997). Adult and peer discrimination measure. Unpublished Document.
  63. Williams DR (2018). Stress and the Mental Health of Populations of Color: Advancing Our Understanding of Race-related Stressors. Journal of Health and Social Behavior, 59(4), 466–485. 10.1177/0022146518814251 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Wu IHC, Lyons B, & Leong FTL (2015). How racial/ethnic bullying affects rejection sensitivity the role of social dominance orientation. Cultural Diversity and Ethnic Minority Psychology. 10.1037/a0037930 [DOI] [PubMed] [Google Scholar]
  65. Yousef WSM, & Bellamy A (2015). The impact of cyberbullying on the self-esteem and academic functioning of Arab American middle and high school students. Electronic Journal of Research in Educational Psychology, 13(3), 463–482. 10.14204/ejrep.37.15011 [DOI] [Google Scholar]
  66. Zeiders KH, Umaña-Taylor AJ, & Derlan CL (2013). Trajectories of depressive symptoms and self-esteem in latino youths: Examining the role of gender and perceived discrimination. Developmental Psychology, 49(5), 951–963. 10.1037/a0028866 [DOI] [PubMed] [Google Scholar]

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