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Examination of the Change in Latent Statuses in BullyingBehaviors Across Time

Ji Hoon RyooUniversity of Virginia

Cixin WangUniversity of California, Riverside

Susan M. SwearerUniversity of Nebraska-Lincoln

Involvement in bullying and victimization has been mostly studied using cross-sectional data from 1 time point. As such, much of our understanding of bullying andvictimization has not captured the dynamic experiences of youth over time. To examinethe change of latent statuses in bullying and victimization, we applied latent transition

analysis examining self-reported bullying involvement from 1,180 students in 5ththrough 9th grades across 3 time points. We identified unobserved heterogeneoussubgroups (i.e., latent statuses) and investigated how students transition between theunobserved subgroups over time. For victimization, 4 latent statuses were identified:frequent victim (11.23%), occasional traditional victim (28.86%), occasional cyber andtraditional victim (10.34%), and infrequent victim (49.57%). For bullying behavior, 3latent statuses were identified: frequent perpetrator (5.12%), occasional verbal/relational perpetrator (26.04%), and infrequent perpetrator (68.84%). The characteris-tics of the transitions were examined. The multiple-group effects of gender, grade, andfirst language learned on transitions across statuses were also investigated. The infre-quent victim and infrequent perpetrator groups were the most stable, and the frequentvictim and frequent perpetrator groups were the least stable. These findings suggestinstability in perpetration and victimization over time, as well as significant changes,especially during school transition years. Findings suggest that school-based interven-tions need to address the heterogeneity in perpetrator and victim experiences inadolescence.

Keywords: latent transition analysis, bullying, victimization, middle and high schools

Supplemental materials:http://dx.doi.org/10.1037/spq0000082.supp

Bullying is a complex phenomenon, definedby repeated aggressive behavior with the intentto hurt others, as well as a perceived imbalanceof power between the bullies and the victims(Olweus, 1994). It is characterized by disrup-

tions in social, behavioral, and often, academicfunctioning (Rueger & Jenkins, 2014;Swearer,Siebecker, Johnsen-Frerichs, & Wang, 2010).The bullying literature has exploded over thepast three decades; however, various method-ological challenges plague the field. For exam-ple, different definitions and forms of bullying(i.e., physical, verbal, relational, and cyber),cut-off points, and time frames used to deter-mine involvement, have made comparisonsacross studies impossible. Meanwhile, under-standing the changes in perpetration and victim-ization over time has been less studied. Thisstudy sought to remedy this by examining fifththrough ninth grade students experiences withbullying over three time points (i.e., semesters).Specifically, we investigated the changes in sta-

This article was published Online First August 11, 2014.Ji Hoon Ryoo, Department of Educational Leadership,

Foundations and Policy, University of Virginia; CixinWang, Graduate School of Education, University of Cali-fornia, Riverside; Susan M. Swearer, Department of Edu-cational Psychology, University of Nebraska-Lincoln.

Correspondence concerning this article should be addressed

to Ji Hoon Ryoo, Department of Educational Leadership,Foundations and Policy, University of Virginia, 417 EmmetStreet South, Charlottesville, VA 22904-4265. E-mail:[email protected]

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School Psychology Quarterly 2014 American Psychological Association2015, Vol. 30, No. 1, 105122 1045-3830/15/$12.00 http://dx.doi.org/10.1037/spq0000082

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http://dx.doi.org/10.1037/spq0000082.suppmailto:[email protected]://dx.doi.org/10.1037/spq0000082http://dx.doi.org/10.1037/spq0000082http://dx.doi.org/10.1037/spq0000082mailto:[email protected]://dx.doi.org/10.1037/spq0000082.supp


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tuses with membership at each time point, andindividual characteristics that may moderate thetransition in bully/victim statuses over time.

Prevalence of and Changes in Bullying andPeer Victimization

Research suggests that the percentage of stu-dents engaging in bullying perpetration rangefrom 6.4% to 35% (Nansel et al., 2001;Sweareret al., 2010), while 30% to 60% students re-ported being victimized, with weekly victimiza-tion ranging from 6% to 15%(Card & Hodges,2008; Robers, Kemp, Truman, & Snyder,2013). The wide range of prevalence rates arelikely due to the fact that researchers have useddifferent cut-off criteria to determine bullyinginvolvement (i.e., daily vs. weekly involvement,or aggression/victimization scores falling 1 SDabove the mean; Schwartz, Proctor, & Chien,2001; Swearer et al., 2010). Using arbitrarycut-off criteria (e.g., 1 SD above the mean)based on the score distribution of a particularsample is problematic because it may lead todifferent classification across studies. Further-more, individuals who score close to the cut-offpoints may be misclassified (Bettencourt, Far-

rell, Liu, & Sullivan, 2013). Person-orientedapproaches, such as latent class analysis (LCA)or latent transition analyses (LTA) can remedythis limitation by using response patterns ofobserved variables to assign individuals to un-observed latent groups (Bye & Schechter, 1986;Collins & Wugalter, 1992).

The difference in prevalence rates acrossstudies may also be due to researchers assessingdifferent types of bullying behaviors (i.e., phys-ical, verbal, relational, and cyberbullying) inde-

pendently or in combination (Swearer et al.,2010). Although researchers generally agreethat physical, verbal, and relational bullying aredistinct constructs, the distinction between cy-berbullying and traditional bullying (physical,verbal, and relational bullying) is less clear.There has been debate regarding whether cyber-bullying is a unique phenomenon (Li, 2007;Olenik-Shemesh, Heiman, & Eden, 2012).Strong correlations between cyberbullying andtraditional bullying as well as cyber victimiza-tion and traditional victimization have been

documented (e.g.,Li, 2007). However, few re-searchers have separated different subtypes oftraditional bullying when examining its rela-

tionship with cyberbullying. One study foundthat students who engaged in cyberbullying be-longed to a highly aggressive group who alsofrequently engaged in other forms of bullying

(physical, verbal, and social;Wang, Iannotti, &Luk, 2012). Further research is needed to ad-vance our understanding of the relationship be-tween cyberbullying/victimization and differenttypes of traditional bullying/victimization(physical, verbal, and relational).

Only a few longitudinal studies have exam-ined the changes in bullying and victimizationover time. Research has demonstrated an initialincrease in bullying after the transition into mid-dle school, and then a general decreasing trendafterward(Pellegrini & Bartini, 2000;Pellegrini& Long, 2002). Studies have also shown a de-crease in peer victimization over time (Nylund,Bellmore, Nishina, & Graham, 2007; Smith,Madsen, & Moody, 1999). When examiningdifferent types of victimization over 3 years,researchers found social victimization increasedsignificantly from seventh grade through ninthgrade only for girls and then decreased at tenthgrade; however, there were no significantchanges in overt victimization (both physicaland verbal victimization) between seventh

grade and tenth grade (Rosen, Beron, & Under-wood, 2013). However, most extant studiesused variable-oriented approach (e.g., correla-tion or ANOVA) to examine stability/change inaggression over time (Pellegrini & Long, 2002;Strohmeier, Wagner, Spiel, & von Eye, 2010),which fail to capture individuals transitionpatterns. The correlation between time pointssimply indicates the association betweenthose degrees but not the changes. LTA is aperson-oriented approach that classifies the

heterogeneous subgroups and traces thechanges in membership over time. It alsoallows researchers to examine factors associ-ated with group membership and transitionsacross time (Bergman & Magnusson, 1997;Bergman, Magnusson, & El-Khouri, 2003;Collins & Lanza, 2010).

Fewer studies have examined the changes ingroup membership/latent status over time usingLTA. One study followed fourth graders for 3years and found four distinct groups: bully(12%24%), victim (25%39%), bully/victim

(7%12%), and not involved (37%40%), withstudents depressive symptoms and antisocialattitudes as significant predictors of status

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change during the transition to middle school(Williford, Boulton, & Jenson, 2014). Similarly,one study with sixth graders over two timepoints (2 years) also found four distinct classes:

nonvictimized aggressors (17%21%), aggres-sive-victims (21%24%), predominantly vic-timized (15%25%, the least stable group), andwell-adjusted youth (37%39%, the most stablegroup; Bettencourt et al., 2013). Looking atbullying perpetration specifically, one study fol-lowed fourth graders over a 3-year period andfound that three distinct groups of bullies ex-isted, including one group (82%) with consis-tently low scores for bullying, one group (7%)with high and consistent scores of bullying, andone group (11%) with moderate but declininglevels of bullying behavior (Reijntjes et al.,2013). To examine the stability of peer victim-ization during sixth to eighth grade, Nylund,Bellmore, Nishina, and Graham (2007) foundthree victim classes based on severity: fre-quently victimized, sometimes victimized, andnonvictimized, and found adolescents tended tomove from a more frequently victimized classinto a less frequently victimized class over time.Although above-mentioned studies have begunto examine the changes in bully/victim statuses

using LTA, they only followed students in asingle grade over a short period of time (e.g., 2to 3 years). Furthermore, to our knowledge,researchers have not yet examined the effects ofgrade and English language learner (ELL) statuson the transition between different bullying andvictimization groups. This study extends priorresearch by using a sequential design with stu-dents from fifth to ninth grades over three se-mesters to examine the stability and transition inclass membership in bullying perpetration and

victimization as well as individual factors (gen-der, grade, and ELL status) that may impact thetransition. Because students involved in bullyingand victimization represent heterogeneous sub-groups, using LTA to accurately identify differentsubgroups of students involved in bullying, andcharacteristics associated with each subgroup willprovide important information to guide bullyingprevention and intervention efforts.

Individual Characteristics Associated With

Bullying and Victimization

Research has shown that some individualcharacteristics, such as gender, grade, immigra-

tion, or ELL status are associated with studentsinvolvement in bullying perpetration and peervictimization. However, how these characteris-tics impact the changes or transition in bully/

victim statuses is less clear.

Gender Differences

Research on gender differences in bullyingand aggression has yielded contradictory find-ings. Some studies have found that boys weremore likely to be involved in bullying than girls(Cook, Williams, Guerra, Kim, & Sadek, 2010).However, other studies have found that al-though boys engage in more physical and verbalaggression than girls (e.g.,Prinstein, Boergers,& Vernberg, 2001), the gender differences inindirect/relational aggression was close to zero(d .06), with girls engaging in slightly higherlevels of indirect/relational aggression (Card,Stucky, Sawalani, & Little, 2008).

When looking at different types of victimiza-tion, boys reported being overtly victimizedmore than girls(Martin & Huebner, 2007;Prin-stein et al., 2001). Girls have been found toexperience more relational victimization com-pared with boys(Dempsey, Fireman, & Wang,

2006;Rueger & Jenkins, 2014). However, theabsence of gender differences (Storch, Masia-Warner, Crisp, & Klein, 2005), and boys expe-riencing more relational victimization (Martin& Huebner, 2007) have also been documented.Regarding cyber victimization, the results arealso mixed, with some studies showing girlsexperiencing more cyber victimization (e.g.,Kowalski & Limber, 2007;Li, 2007), yet otherstudies finding no gender difference (e.g.,Jack-son & Cohen, 2012). These differences might

be due to different data collection methods used(self-report vs. peer/teacher report), differentage groups studied, and the use of differentdefinitions of bullying and victimization. Re-searchers have also suggested that in order tobetter understand gender differences in bully-ing, it is important to move beyond the meanlevel differences and to examine the process bywhich bullying unfolds(Underwood & Rosen,2011,p. 13). Thus, it is important to examinehow gender may influence the transition in bul-lying perpetration and victimization classes

over time. Limited research has suggested thataggressive behavior is less stable among girlsthan boys, while girls tend to transition out of

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serious aggression; boys involvement in ag-gression is more stable over time (Miller et al.,2013).

Grade Differences

Research has shown that bullying behaviorstend to increase at the end of elementary schoolto middle school years and then decrease duringthe high school years (Olweus, 1993;Pepler etal., 2006). Specifically, an increase in bullyingafter the initial transition to middle school hasbeen documented, suggesting students may usebullying as a way to gain social status with newpeers after school transitions(Pellegrini & Bar-tini, 2000; Pellegrini & Long, 2002). Further-more, compared with younger students, olderstudents were more likely to be a bully, lesslikely to be a victim (Cook et al., 2010), andtended to exhibit lower rates of physical bully-ing and higher rates of relational bullying (Coie& Dodge, 1998). Thus, as children mature, theylearn that there are greater negative conse-quences for physical bullying and fewer conse-quences for relational, verbal, and/or cyberbul-lying because they are more difficult to detect.

English Language Learner Differences

Immigrant students experience many chal-lenges at school, including discrimination(Shin, DAntonio, Son, Kim, & Park, 2011),limited access to educational resources (Kozol,2005), and increased risk for mental health dif-ficulties (Sue, 1994). Considering that about25% of children in U.S. have at least on foreign-born parent (U.S. Census Bureau, 2010), it isimportant to examine how immigrant or ELL

status impacts students experiences with bully-ing and/or peer victimization. However, only afew studies have examined the effect of ELLstatus or immigration on bullying/victimization.Some studies have shown that immigrants andELL students experience higher level of peervictimization compared with native speakers,possibly because of language barriers and cul-tural differences (von Grnigen, Perren, Ngele,& Alsaker, 2010;Koo, Peguero, & Shekarkhar,2012); however, other studies have not foundthese disparities (Boulton, 1995; Strohmeier,

Krn, & Salmivalli, 2011). Furthermore, mostof those studies were conducted in Europeancountries and not in the United States.

The Present Study

To date, most studies have used predeter-mined cut-off criteria to define bully/victim

groups and have examined prevalence ratesacross different age groups using cross-sectional designs, which results in a lack ofunderstanding in the dynamic changes amongbully/victim statuses. In previous approachessuch as correlation studies using cut-off scores,the degree of victimization and bullying weremeasured with the assumption that there exists asingle homogeneous population for each con-struct that can be measured using a continuousscale. The correlation between time points sim-

ply indicates the association between those de-grees but not the changes. Previous research hasshown that using predetermined cut-off pointsto define different victim groups may be prob-lematic because it fails to take developmentalchanges in peer victimization into consideration(Nylund et al., 2007). The current study em-ployed LTA and extends previous research byusing a sequential design with students fromfifth to ninth grades over three semesters toestimate the prevalence and degree of transi-

tioning between latent statuses. We identifiedunobserved heterogeneous subgroups and ob-served how students transitioned between theunobserved heterogeneous subgroups over time.Using LTA, the current study was guided by thefollowing research questions and hypotheses:

1. Are there distinct subgroups of studentsinvolved in bullying and victimizationwho engage in particular patterns of be-haviors? Based on the existing literature(Nylund et al., 2007; Reijntjes et al.,

2013), we hypothesized that there are dis-tinct victim classes and perpetrator classesbased on the frequency of involvementinstead of the type, for example, nonvic-tims, occasional victims, and frequent vic-tims as well as nonperpetrators, occa-sional perpetrators, and frequentperpetrators.

2. Is there change between latent statusesacross time? We hypothesized that there ischange between latent statuses across

time.a. If so, how can this change be charac-

terized? We hypothesized that each


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transition can be classified either: de-creasing, increasing, or stable.

b. Is the change in latent statuses affectedby student characteristics such as gen-

der, grade, and ELL status? Based onthe existing literature (e.g., Pellegrini& Long, 2002;Williford et al., 2014),we hypothesized that during the transi-tion to middle school, students willmove from a less involved victim orperpetrator group into a more fre-quently involved group. Over time,girls will become less involved in per-petration and victimization comparedwith boys (Miller et al., 2013;Nylundet al., 2007; Rosen et al., 2013). Wehypothesized that ELL students mayexperience more victimization overtime due to their language and culturaldifferences.

3. If an individual is in a particular latentstatus at Time t, what is the probabilitythat the individual will be in that latentstatus at Time t 1, and what is theprobability that the individual will be in adifferent latent status? We hypothesizedthat involvement in bullying and victim-

ization is a dynamic experience over time;however, due to limited research in theliterature, this research question is explor-atory in nature and we do not have specifichypothesis regarding the probability.

Method

Participants

Data for this study are part of a larger inter-

national longitudinal investigation involving re-searchers from the United States, Japan, Korea,Australia, and Canada (Konishi et al., 2009).Part of the first wave of the data collected in theU.S. were published in a study examining thedifferent experiences of bullying among stu-dents in special and general education(Swearer,Wang, Magg, Siebecker, & Frerichs, 2012). Inthe current study, data were collected from fifthto ninth graders over three semesters (Fall 2005,Spring 2006, and Fall 2006). Participants were1,180 students from fifth to ninth grades (mean

age 12.2, SD 1.29) in Fall 2005 (Time 1)attending nine schools in a midwestern city inthe United States. Due to students school tran-

sitions, the number of schools increased to 22over three semesters. Slightly more than half(52.9%) of participants were female, 46.5%were male, and gender information was not

available for 0.6% of participants at Time 1.Grades were distributed from fifth to ninth grades:fifth (10.0%), sixth (31.4%), seventh (26.4%),eighth (21.0%), and ninth grade (10.6%). Amongthe participants, 9.9% indicated that English wasnot their first language. The ethnicity of the sam-ple was predominantly Caucasian: Caucasian/White (80.2%), Black/African American (7.1%),Latino/Hispanic (5.4%), Asian American (2.4%),other (1.7%), and missing (3.2%). The attritionrates were 5.59% at Spring 2006 and 15.34% atFall 2006.

Procedure

Recruitment letters were distributed to allparents with children from fifth grade to ninthgrade in the participating schools. Parents wereinformed that the results would be confidential,and that they could withdraw their consent atany time without penalty. Approximately 53%of the consent forms were returned by the par-ents and/or guardians and 81.1% of them gave

consent for their children to participate. Stu-dents were also given a youth assent form.Almost all of the students (97%) assented toparticipate in the study. Only the studentswhose parents gave consent and the studentswho gave assent were included in the currentstudy. Students completed the instruments inlarge groups at school (e.g., classroom, lunch-room) during the regular school day. Trainedgraduate research assistants gave clear instruc-tions to the students, answered students ques-tions during data collection, and checked the

measures for any missing data.

Measures

Each student completed a demographic ques-tionnaire that included questions about gender,age, grade, first language use, and race/ethnicity. Then, students completed the Pacific-Rim Bullying measure (PRBm;Konishi et al.,2009; Swearer et al., 2012) which surveyedstudents experiences and concerns about bul-lying and victimization without using the word

bullying in order to avoid misunderstandingor different understandings of the bullying con-struct across countries and languages. Instead,


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students were provided with the following in-struction to capture three primary distinguishingcharacteristics of bullyingintentionality, rep-etition, and power differential: Students can be

very mean to one another at school. Mean andnegative behavior can be especially upsettingand embarrassing when it happens over andover again, either by one person or by manydifferent people in the group. We want to knowabout times when students use mean behaviorand take advantage of other students who can-not defend themselves easily. Data were alsocollected from school records that included de-mographics, students GPA, and office referraldata. As part of the longitudinal study, studentsalso completed additional counterbalancedmeasures on internalizing symptoms and cogni-tive functioning.

Students were asked to respond to six itemsfrom the PRBm to measure peer victimization:In the past 2 months, how often have otherstudents been mean or negative to you (a) bypushing, hitting, or kicking or other physicalways (jokingly)?; (b) by pushing, hitting, orkicking or other physical ways (on purpose)?;(c) by taking things from them or damagingtheir property?; (d) by teasing, calling them

names, threatening them verbally, or sayingmean things to you?; (e) by excluding orignoring them, spreading rumors or sayingmean things about them to others, or gettingothers not to like them?; (f) by using com-puter, e-mail, or phone text messages? Thesame six items tapped bullying perpetration.Response options were based on a 4-point Lik-ert-type scale, ranging from never, once ortwice, about once a week, to several times aweek.In the current study, because bullying was

defined as a purposeful aggressive behavior,one item by pushing, hitting or kicking or otherphysical ways (jokingly) was not included inthe analyses, because the word, jokingly ne-gated the purposeful aggressive nature of bul-lying. Instead of using all four responses (never,once or twice, about once a week, and severaltimes a week), the last two response optionswere combined intoonce or more a week, becausethey both represent regular weekly involvement invictimization and bullying perpetration. Internalconsistencies for the self-reported victimization

scale were .72 (Fall 2005), .75 (Spring 2006),and .78 (Fall 2006). Internal consistencies forthe self-reported bullying perpetration scale

were .73 (Fall 2005), .74 (Spring 2006), and .77(Fall 2006). Additional information regardingthe validity of PRBm can be found inSwearer,Wang, Magg, Siebecker, and Frerichs (2012).

For example, the bullying perpetration meanscore in PRBm correlated significantly with ag-gression measured by Childrens Social Behav-ior Scale (Crick & Grotpeter, 1995) and BullySurvey-Short (Swearer, 2006). The victimiza-tion mean score in PRBm correlated signifi-cantly with victimization measured by Chil-drens Social Experiences Questionnaire(Crick& Grotpeter, 1995) and Bully Survey-Short(Swearer, 2006).

Statistical Analyses

We conducted LTA, to represent the complexarray of response proportions in this data set ina format that is more parsimonious and easier tocomprehend, and at the same time to revealimportant scientific information contained inthe data. Using the same notations as that inCollins and Lanza (2010),the LTA with threetime lags in this study can be written as

P(Yy)

k11K

k21K

k31K

k1

(k2 k1)

(k3 k2)

t1

3

j1

J

rj,t1

Rj

(j,rj,t kt)

I(yj,trj,t)

where k 1, . . . , Kare the number of latentclasses, j 1, . . . ,J are observed variableshavingrj 1, . . . , Rjresponse categories (rj3 for items for bullying and victimization mea-sures),

ks are probability of membership in the

kth latent class,s are the item-response prob-abilities,Iis an indicator function,kj1 kjrep-resents the probability of a transition to latentstatus kat time j 1, conditional on member-

ship in latent statuskat timej. Multiple-GroupsLTA, denoted by g, can also be written as

P(Yy G g)

k11

K

k21

K

k31

K

k1,g(k2 k1),g(k3 k2),gt1

3

j1

J

rj,t1

Rj

(j,rj,tkt,g)I(yj,trj,t) .

In the analyses, we restricted parameters ofitem-response probabilities, , across times andgroups, that is, j,rj,tkt

Iyj,trj,t j,rjktIyj,trj and j,rj,tkt,g

Iyj,trj,t

j,rjktIyj,trj, respectively. The purposes of assuming

measurement invariance across times, groups,and latent statuses are the same as inCollins andLanza (2010;pp. 212213), because it is easy to


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interpret the latent transition models and theother is to help stabilize estimation and improvemodel identification.

To select the optimal number of latent sta-

tuses for bullying and victimization, we com-pared LTA models from two latent classes to sixlatent classes by applying information criteria(AIC and BIC). In the case of disagreement ofAIC and BIC, we selected the optimal numberby looking at trends of changes in both infor-mation criteria. The optimal number was used insubsequent latent transition analyses with group-ing variables: gender, grade, and ELL status.

Next, the testing hypotheses about change oftransition probabilities between times was con-ducted by using likelihood ratio difference test(LRDT). That is, we tested if a single transitionmatrix was enough to explain the change oflatent statuses across times. According to thework by Read and Cressie (1988; as cited inCollins & Lanza, 2010), the LRDT statistic,G

2 G22 G1

2, withd f d f2 d f1, is likelyto be approximated well by the 2 distributionwith df when df is relatively small. In thisstudy, there are 28 dfs that vary from 2 to 96with median 12.

In multiple Groups LTA, four models were

compared to obtain the best fitting models interms of restrictions on latent status prevalenceat Time 1 and transition probabilities (seeTable1). In Model 1, both prevalence and transitionswere free to vary across groups. In Model 2, theprevalence was constrained across groups. InModel 3, the transitions were constrained acrossgroups. In Model 4, both prevalence and tran-sitions were constrained. These hypothesis testsallowed us to examine the possible variances interms of latent status prevalence and transition

probabilities according to grouping variables.To compare four models, AIC and BIC werealso considered in addition to the LRDT be-cause the LRDT works only for nested models

and those four models are not fully nested. Wecompared Models 2, 3, and 4 with Model 1unless either Model 2 or Model 3 fit equallywell into the data and fit better than Model 1.When Model 2 or Model 3 fit equally well intothe data and fit better than Model 1, we com-pared Model 4 with the selected model via theresults of the information criteria.

Results

Students Experiences of Victimization

To identify the number of latent statuses,two-status to six-status models were comparedusing the information criteria, AIC and BIC,and the interpretability of latent classes in termsof item probabilities was considered. The low-est AIC was found for a six-status model, whilethe lowest BIC was found for a four-statusmodel. The four-status model, however, wasmore parsimonious and conceptually more ap-pealing than the six-status model in terms of

item probabilities. We classified four differentgroups of students who were bullied. Frequentvictims referred to the regularly (weekly) bul-lied students: they were regularly physicallyharmed (47% probability of endorsing the re-sponse), their property was regularly taken ordamaged (39%), they were regularly and ver-bally bullied (82%), and they were regularlyisolated from peers due to rumors (66%). How-ever, they were not highly involved with cyber-bullying (25% answeredweekly, 18% answered

Table 1Model Comparison of Selecting the Best Fitting Model

Item-response probabilitiesLatent status

prevalences at Time 1Transition

probabilities

LTA without groupingvariable

Model 1 Equal across times and latent statuses FreeModel 2 Equal across items and latent statuses Equal across times

Multiple groups LTAModel 1 Equal across times, groups and latent statuses Free Free

Model 2 Equal across times, groups and latent statuses Equal across groups FreeModel 3 Equal across times, groups and latent statuses Free Equal across groupsModel 4 Equal across times, groups and latent statuses Equal across groups Equal across groups


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once or twice during the past 2 months, and58% answered never). Occasional traditionalvictimsreferred to the students who were occa-sionally bullied physically, verbally, and rela-

tionally, but not electronically. They answeredhigh probabilities for once or twice during thepast 2 months in terms of being physicallyharmed (36%), their property was once or twicetaken or damaged (34%), they were once ortwice verbally bullied (59%), and they wereonce or twice isolated from peers due to rumors(42%). However, they were not involved withcyberbullying (99% answered never). Occa-sional cyber and traditional victimsreferred tothe occasional verbal, relational, and cyber vic-tims: They were once or twice verbally bullied(69%), isolated from peers due to rumors(69%), and cyber bullied (57%), but were lesslikely to experience physical bullying behavior(pushing/hitting/kicking, 24%) and propertydamage (38%). Infrequent victims referred tothe rarely bullied students. These statuses weremutually exclusive and exhaustive. Item proba-bilities (estimates) were summarized inTable2.The main difference between the occasionaltraditional victimsand theoccasional cyber andtraditional victimswere that the occasional tra-

ditional victims did not report experiencing cy-ber victimization.

The probability of being in the frequent, oc-casional traditional, and occasional cyber andtraditional victims statuses was 50% at Fall2005, 51% at Spring 2006, and 40% at Fall2006 (see estimates inTable 3). These results

suggested that after students returned from sum-mer break, they were less likely to be victim-ized. Specifically, the decrease mainly occurredamong the occasional traditional victim group

(from 29% to 18%). The probability of being anoccasional cyber and traditional victim in-creased from 10.3% (Fall 2005) to 12.2%(Spring 2006) and 12.8% (Fall 2006).

In this article, we focus our discussion on thedifference of 20% or higher between the prob-abilities at two different time points because thetransition probability of 20% is relatively highin this sample. We found that the total numberof frequent victims mainly stayed level fromFall 2005 to Spring 2006, but their group com-

position changed over time. Specifically, 24%of members in the frequent victim groupchanged into the occasional traditional victimgroup, meanwhile, 14% of members in the oc-casional traditional victim group and 14% oc-casional cyber and traditional victims changedinto the frequent victim group. There weregreater changes in statuses from Spring 2006 toFall 2006. About 35.9% of members in thefrequent victim group changed into the occa-sional traditional victimgroup, 46.1% of mem-

bers in the occasional traditional victim groupchanged into the infrequent victimgroup, andabout 28.4% of members in the occasional cy-ber and traditional victim group changed intothe infrequent victim group (seeTable 3). Thissuggests that after the summer break, most stu-dents experienced less victimization.

Table 2Item Probabilities of the Latent Statuses ( Estimates) on the Victimization Item (Item 18)

Responsecategory Never Once or twice Once or more a week

Latent status LS1 LS2 LS3 LS4 LS1 LS2 LS3 LS4 LS1 LS2 LS3 LS4

AllItem18 (b) 0.2168 0.5994 0.7342 0.9371 0.3133 0.3583 0.2426 0.0597 0.4699 0.0423 0.0231 0.0032Item18 (c) 0.2565 0.6366 0.6119 0.9354 0.3510 0.3438 0.3815 0.0605 0.3925 0.0196 0.0066 0.0041Item18 (d) 0.0377 0.2629 0.2767 0.8836 0.1424 0.5884 0.6921 0.1023 0.8199 0.1487 0.0312 0.0142Item18 (e) 0.1265 0.5012 0.2216 0.8872 0.2103 0.4146 0.6907 0.1093 0.6632 0.0842 0.0877 0.0035Item18 (f) 0.5767 0.9932 0.3507 0.9505 0.1767 0.0000 0.5703 0.0439 0.2466 0.0068 0.0790 0.0055

Note. LS latent status; LS1 Frequent victim; LS2 Occasional traditional victim; LS3 Occasional cyber victim; LS4 Infrequent victim. Item 18: In the past 2 months, how often have other students been mean or negative to you (b) by pushing,

hitting, or kicking or other physical ways (on purpose)?; (c) by taking things from them or damaging their property?; (d) byteasing, calling them names, threatening them verbally, or saying mean things to them?; (e) by excluding or ignoring them,spreading rumors, or saying mean things about them to others, or getting others not to like them?; (f) by using computer, e-mail,or phone text messages? The bold values indicate the relatively higher probabilities that are greater than 0.3333.


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Gender. Model comparison results suggestthat each gender group had different initial latentstatus prevalence and different transition matrix.The probabilities of item parameters (estimates)were almost identical to those in the overall sam-ple, which indicated that the characteristics oflatent statuses were also identical to each other,and verified the assumption of measurementinvariance.

At Fall 2005, girls were more likely to be oc-casional cyber and traditional victims(31%) than

boys (3%); boys were more likely to be occa-sional traditional victims(28%) than girls (12%),and there were more infrequent victims amongboys (58%) than girls (46%). From Fall 2005 toSpring 2006, 26% of frequent victimboys movedinto the occasional traditional victimgroup, and22% of occasional traditional victimboys alsochanged into frequent victims. On the other hand,29% and 19% of girls in the frequent victimgroupbecame occasional cyber and traditional victimsand occasional traditional victims. From Spring

2006 to Fall 2006, all boys in the occasional cyberand traditional victimgroup moved to either thefrequent victim group (20%) or the infrequentvictim group (80%). For occasional cyber andtraditional victim girls, the majority (56%) stayed inthat group, and 38% transitioned into the infrequentvictimgroup. Most girls in frequent victims(62%)became occasional traditional victims (21%),occasional cyber and traditional victims(20%),and infrequent victims(22%). In addition, 39%of occasional traditional victims and 38% ofoccasional cyber and traditional victims be-

came infrequent victims. At both time points,girls (38% to 40%) were less likely to remain inthe frequent victim class than boys (43% to

65%). Boys (0% to 52%) were less likely toremain in the occasional cyber and traditionalvictim class than girls (56% to 79%). Furtherinformation on changes can be found in supple-mental materials (see Tables S9 and S10).

Grade. Results of four model comparisonssuggest the trend of being a member in each latentstatus was different across grade, but their changesin latent statuses were the same. On the otherhand, the probabilities of item parameters (esti-mates) were almost identical to those of overall

group, which also supported the measurement in-variance.

Fifth graders indicated the highest probabilityof beingfrequent victims(21%), followed by sixthgraders (12%), eighth graders (11%), seventhgraders (9%), and ninth graders (6%). On theother hand, ninth graders had a different trendfrom other students. Ninth graders reported lowerprobability of being in the occasional traditionalvictim group (11%) than other graders (rangingfrom 27% to 39%), while reporting higher prob-

ability of being in the occasional cyber and tra-ditional victim status (26%) than other grades(ranging from 4% to 13%). In terms of probabilityof being in a specific latent status in Fall 2005,there were three clusters indicating similar preva-lence: fifth and sixth graders, seventh and eighthgraders, and ninth graders. The transition proba-bilities were the same across grades, which indi-cated that all students stayed in the same latentstatus except for 24% of the frequent victimswhochanged into the occasional traditional victimgroup from Fall 2005 to Spring 2006. This trend

continued at the transition from Spring 2006 toFall 2006. In addition, 45% of the occasionaltraditional victims and 30% of the occasional

Table 3Latent Class Prevalence ( Estimate) and Transition Matrix Estimates ( Estimates) Over 3 Time Points

on the Victimization Item

Time

estimate estimate(a) estimate(b)

LS1 LS2 LS3 LS4 LS1 LS2 LS3 LS4 LS1 LS2 LS3 LS4

AllF05 0.1123 0.2886 0.1034 0.4957 LS1 0.5268 0.2388 0.1429 0.0915 LS1 0.4197 0.3588 0.0533 0.1682S06 0.1227 0.2914 0.1222 0.4936 LS2 0.1366 0.7181 0.0505 0.0928 LS2 0.0457 0.4096 0.0836 0.4611F06 0.0908 0.1827 0.1283 0.5982 LS3 0.1417 0.0172 0.6844 0.1568 LS3 0.1043 0.0000 0.6116 0.2842

LS4 0.0180 0.1122 0.0420 0.8278 LS4 0.0285 0.0415 0.0490 0.8810

Note. LS latent status; LS1 Frequent victim; LS2 Occasional traditional victim; LS3 Occasional cyber and traditionalvictim; LS4 Infrequent victim; (a) Transition matrix from Fall 2005 to Spring 2006; (b) Transition matrix from Spring 2006to Fall 2006. The bold values indicate the relatively higher transitions that are greater than 0.2000.


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cyber and traditional victims moved into the in-frequent victim group. Further information onchanges can be found in supplemental materials(Tables S12 and S13).

ELL status. Consistent with the results ofthe grade analyses, the probability of being amember in each latent status was different for thelanguage groups (ELL and non-ELL groups) buttheir changes of latent statuses were the same. Onthe other hand, the probabilities of item parame-ters ( estimates) were fairly similar comparedwith those of overall group, which also supportsthe measurement invariance.

We found differences in the probabilities of theoccasional victimand theinfrequent victimgroupsacross language groups: the English speakinggroup had higher probability in the occasionalvictim group (33%) than the ELL group (18%),and the ELL group had a higher probability to bein the infrequent victim(62%) group than in theEnglish speaking group (47%). Results also indi-cated that English speaking group had a slightlyhigher probability to be in the occasional cyberand traditional victimgroup (9%) than in the ELLgroup (6%), but a lower probability to be in thefrequent victim group (11%) than in the ELLgroup (13%). The probabilities fromfrequent vic-

tims to occasional traditional victims and fromoccasional cyber and traditional victims to theinfrequent victims were relatively higher (46%and 24%, respectively) after controlling for ELLstatus than probabilities from frequent victims tooccasional traditional victims and from occa-sional cyber and traditional victimsto infrequentvictimsin the overall sample (24% and 16%, re-spectively). Further information on changes canbe found in supplemental materials (see TablesS15 and S16).

Students Experience of Bullying

Perpetration

Results indicated a three-status model forbullying perpetration (instead of a four-statusmodel in victimization). Frequent perpetratorreferred to the group engaging in bullying be-haviors frequently (once or more a week) phys-ically (46%), verbally (66%), relationally(50%), and online (30%), as well as by destroy-ing others property (30%). Infrequent perpetra-torreferred to the group of students who rarelyengaged in bullying behavior (less than 5%).Occasional verbal/relational perpetrator re-

ferred to the group of students who were in-volved in bullying behaviors occasionally (onceor twice in the past 2 months) mainly verbally(61%) and relationally (39%), and to a lesser

degree through physical harm to others (27%),destroying property (13%), and online methods(16%). The item probabilities ( estimates)were summarized inTable 4.

Latent status prevalence indicates that moststudents were in the infrequent perpetratorgroup ranging from 66% to 72%, followed bythe occasional verbal/relational perpetratorgroup ranging from 22% to 30%, and the fre-quent perpetratorgroup ranging from 4% to 6%(see estimates in Table 5). The infrequentperpetrators and the occasional verbal/rela-tional perpetrators tended to stay in theirgroups from Fall 2005 to Spring 2006 (88% and75%, respectively), and the frequent perpetra-torsspread out to other latent statuses: 24% tothe infrequent perpetratorsand 35% to the oc-casional verbal/relational perpetrators. In ad-dition, 19% ofoccasional verbal/relational per-petratorsmoved into the infrequent perpetratorgroup. From Spring 2006 to Fall 2006, the in-frequent perpetrators (91%) tended to stay intheir group, and the frequent perpetrators and

the occasional verbal/relational perpetratorsspread out to other latent statuses. For example,34.9% ofoccasional verbal/relational perpetra-tors moved into the infrequent perpetratorgroup (see estimates inTable 5).

Gender. The best fitted model with thegender covariate was the most parsimoniousone constraining both latent status prevalenceand transition probabilities, suggesting therewere no gender differences.

Grade. Different from gender, the trends of

change varied across grades. In terms of latentstatus prevalence, sixth and ninth graders wereless likely to be frequent perpetrators(3% and1%, respectively) than other grades (probabili-ties ranging from 7% to 9%) in Fall 2005. Onthe other hand, eighth graders were less likely tobe infrequent perpetrators (55%) than othergraders (probabilities of being an infrequentperpetrator ranging from 66% to 77%). Simi-larly, eighth graders were more likely to be inthe occasional verbal/relational perpetrators(38%) group than other graders (probabilities

ranging from 20% to 27%).Most frequent perpetrators among sixth and

ninth graders remained in the same group (85%


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and 100%, respectively) during the school year(from Fall 2005 to Spring 2006); however, only28% and 0% of the frequent perpetratorsamong sixth and ninth graders remained in thesame status from Spring 2006 to Fall 2006. Thatis, the total number of frequent perpetratorsamong sixth and ninth graders remained stableduring the school year and then decreased as

those students became seventh and tenth grad-ers. However, 78% and 62% of the frequentperpetrators among fifth and eighth graderstended to remain in the same status when theybecame sixth and ninth graders, after the schooltransition. Another interesting finding was that38% of the occasional verbal/relational perpe-tratorsamong fifth graders moved into the fre-quent perpetrator group after transiting intomiddle schools. Furthermore, about 5% of fifthgraders were in the frequent perpetratorgroupin Spring 2006, but the probability increased

16% in Fall 2006, which was the biggest in-crease across grades. Further information onchanges can be found in supplemental materials(see Tables S25 and S26).

ELL status. Results of the model compar-ison suggested group difference for both latentstatus prevalence and transition matrix proba-bilities. The ELL group had slightly higher

probability of being in the frequent perpetratorgroup and the occasional verbal/relational per-petrator group, 8% and 31%, than those inEnglish speaking groups, 5% and 25%, respec-tively. That is, the ELL group reported slightlymore involvement in bullying perpetration. Onthe other hand, the frequent perpetratorsin theELL group tended to either stay in the samestatus (30% at Spring 2006 and 54% at Fall2006) or move to the infrequent perpetratorgroup (70% at Spring 2006 and 46% at Fall2006), but did not move to the occasional ver-

Table 4 Estimate of Probabilities of Item Parameters on the Bullying Item

Responsecategory Never Once or twice Once or more a week

Latent status LS1 LS2 LS3 LS1 LS2 LS3 LS1 LS2 LS3

AllItem22 (b) 0.2644 0.9740 0.7217 0.2754 0.0253 0.2678 0.4603 0.0006 0.0105Item22 (c) 0.3943 0.9886 0.8605 0.3068 0.0114 0.1331 0.2989 0.0000 0.0064Item22 (d) 0.1206 0.9513 0.3371 0.2226 0.0429 0.6131 0.6568 0.0058 0.0498Item22 (e) 0.2398 0.9604 0.5953 0.2605 0.0394 0.3862 0.4996 0.0002 0.0185Item22 (f) 0 .4717 0.9803 0.8297 0.2234 0.0178 0.1579 0.3049 0.0019 0.0124

Note. LS latent status; LS1 Frequent perpetrator; LS2 Infrequent perpetrator; LS3 Occasional verbal/relationalperpetrator. Item 22: In the past 2 months, how often have you been mean or negative toward others? (b) by pushing, hitting,or kicking or other physical ways (on purpose)?; (c) by taking things from them or damaging their property?; (d) by teasing,calling them names, threatening them verbally, or saying mean things to them?; (e) by excluding or ignoring them, spreadingrumors, or saying mean things about them to others, or getting others not to like them?; (f) by using computer, e-mail, or phone

text messages? The bold values indicate the relatively higher probabilities that are greater than 0.3333.

Table 5Latent Class Prevalence ( Estimate) and Transition Matrix Estimates ( Estimates) Over 3 Time Points

on the Bullying Item

Time

estimate estimate(a) estimate(b)

LS1 LS2 LS3 LS1 LS2 LS3 LS1 LS2 LS3

AllF05 0.0512 0.6884 0.2604 LS1 0.4171 0.2387 0.3466 LS1 0.4569 0.3133 0.2297S06 0.0392 0.6642 0.2966 LS2 0.0093 0.8750 0.1157 LS2 0.0265 0.9117 0.0617F06 0.0564 0.7215 0.2222 LS3 0.0444 0.1907 0.7648 LS3 0.0702 0.3492 0.5806

Note. LS latent status; LS1 Frequent perpetrator; LS2 Infrequent perpetrator; LS3 Occasional verbal/relationalperpetrator; (a) Transition matrix from Fall 2005 (F05) to Spring 2006 (S06); (b) Transition matrix from Spring 2006 to Fall 2006(F06). The bold values indicate the relatively higher transitions that are greater than 0.2000.


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bal/relational perpetratorgroup.Frequent per-petrators among the English speaking grouptended to move into the occasional verbal/relational perpetrator group (40% at Spring

2006 and 28% at Fall 2006) and infrequentperpetrator (18% at Spring 2006 and 27% atFall 2006). Further information on changes canbe found in supplemental materials (see TablesS28 and S29).

Discussion

Few studies have explored changes in bully-ing and victimization over three time points.Previous studies have mainly used observed

data of frequency measures instead of usinglatent status to capture the changes in bullyingand victimization, which neither focuses on in-dividual change over time nor reveals thechange of latent statuses representing the truemembership in terms of bully/victim statuses.This study used LTA with five perpetrationitems and five victimization items to modelindividual changes in bullying behavior andvictimization and examined the effects of gen-der, grade, and ELL status on the transition.Because LTA is a data-driven method, resultsmay vary if different measures and differentpopulations are used. However, based on previ-ous research (e.g.,Reijntjes et al., 2013) and onour results, it is likely that the groups will bepartially based on frequency (not involved,sometimes involved, and frequently involved).It is unlikely that the groups will be basedsolely on the type of bullying/victimizationbecause students tend to engage in differenttypes of bullying behaviors/victimization si-multaneously, making it impossible to sepa-

rate different groups only by the type of bul-lying/victimization.

Prevalence Rates

Results from the current study indicate thatmost students (66% to 72%) rarely participatedin bullying perpetration, 22% to 30% of stu-dents occasionally engaged in bullying behaviortoward others, and only 4% to 6% studentsregularly bullied others. On the other hand,most students (49% to 60%) were rarely in-

volved with victimization, 18% to 29% studentswere occasional traditional victims, 10% to13% students were occasional cyber and tradi-

tional victims, and 9% to 12% students werefrequent victims. The finding is somewhat con-sistent with previous research which found thata small percentage of individuals accounted for

the majority of antisocial behaviors (Falk et al.,2014). Our findings highlight the importance oflatent statuses and their transitions, as the prev-alence rates vary based on the cut-off criteria(weekly vs. monthly) and different forms ofbullying/victimization measured (Swearer et al.,2010). Traditional bullying interventions focuson teaching the bullying perpetrators socialskills, and group formats are often used. How-ever, having a group of frequent perpetrators ina social skills group may actually promote ag-gression and reinforce bullying behaviors(Dishion, McCord, & Poulin, 1999; Dodge,Dishion, & Lansford, 2006). Considering thesmall number of frequent perpetrators, individ-ualized interventions may be more appropriateto target specific behavioral deficiencies andadjustment difficulties among bully perpetra-tions.

Victimization and Bullying Classes

Using latent class analysis, Nylund et al.

(2007)suggested that victim groups are betterclassified by the severity of victimization in-stead of types. Partially consistent withNylundet al. (2007),results from the current investiga-tion indicated four distinct victim groups basedon both severity (weekly vs. monthly) and type(traditional vs. cyber). Specifically, we foundtwo groups of occasionally victimized studentswho differed in the types of victimization theyexperienced. One group reported experiencingcyber and relational and verbal victimization, as

well as physical victimization (although to alesser degree). The other group reported expe-riencing physical, verbal, and relational victim-ization, but not cyber victimization. The findingsupports the claim that cyber victimization is adistinct construct from physical, verbal, and re-lational victimization (Smith, 2012), as it dif-ferentiated the two occasional victim groups inour study. Furthermore, findings from the cur-rent study suggest that both severity and typeare important in distinguishing different victimgroups.

Our results identified three groups of bullyingperpetrators based on the severity instead of thetype, namely frequent perpetrators, occasional


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verbal/relational perpetrators, and infrequentperpetrators. These results are consistent withprevious findings(Reijntjes et al., 2013;Wang,Iannotti, & Luk, 2012) in that type is not an

important factor in distinguishing different per-petrator groups. It appears that all perpetratorsuse different forms of bullying behaviors totarget their victims, although perpetrators differin the frequency of their involvement.

Changes in Bullying Behaviors

and Victimization

Consistent with previous research (e.g., Ny-lund et al., 2007;Smith et al., 1999), we foundthat bullying and victimization decreased overtime and students tended to transition from amore frequent perpetrator class into a less fre-quent perpetrator class over time. This findingsuggests other malleable factors in the environ-ment may have an influence on group member-ship (i.e., maturation, peer norms). However,the patterns were different during the schooltransition year. The prevalence and frequencyof bullying perpetration increased from fifth tosixth grades. During the sixth grade year, mostof the frequent perpetrators remained in the

same status, and then became less involved inperpetration when they became seventh graders.It is likely that the new sixth graders used bul-lying behaviors as a means to gain social statusafter their school transition. Our findings areconsistent with social dominance theory andprevious studies, which have found that bully-ing tends to increase after the school transition,and then decreases afterward (Pellegrini & Bar-tini, 2000;Pellegrini & Long, 2002). Consider-ing the unique pattern during school transitions,

carefully designed interventions should targetnew sixth grade students as they adjust to themiddle school environment and to new peergroups. Interventions should focus on teachingsocial-emotional learning skills to students(e.g.,www.casel.org)and appropriate ways tonavigate new peer groups and social hierarchies(Faris & Felmlee, 2014).

An interesting finding is that although victim-ization decreased in general, cyber victimiza-tion increased over time. The increase in cybervictimization may be related to increased access

to the Internet and mobile devices as adoles-cents get older. The distinct pattern of cybervictimization also suggests that it is a unique

form of peer victimization (Smith, 2012).Adults need to educate students about cybersafety and monitor students technology use tohelp prevent cyber victimization.

We found that although bullying behaviorsand victimization are highly stable for the in-frequently involved groups (83%91% infre-quent perpetrators and victims tended to stay inthe same group), it is less stable among thefrequently involved groups (54%58% frequentperpetrators and 47%58% frequent victimstransitioned into other groups). This findingsuggests that bullying behaviors and victimiza-tion are dynamic phenomena, which challengesthe notion that aggression and victimization arehighly stable from late childhood to adoles-cence (Scholte, Engels, Overbeek, de Kemp, &Haselager, 2007). Early intervention is criticalin order to teach bullying perpetrators replace-ment prosocial behaviors before their aggres-sion becomes stable (Bettencourt et al., 2013).

Gender differences. Different fromMilleret al. (2013),current results did not indicate anygender differences in bullying perpetration tosupport the assertion that aggressive behavior isless stable among girls than boys. However, wefound that girls were more likely to experience

verbal/relational and cyber victimization thanboys, and boys were more likely to be physi-cally victimized. This gender difference in ver-bal/relational and cyber victimization is consis-tent with previous research (Kowalski &Limber, 2007; Li, 2007), but different fromother studies finding no gender differences (e.g.,Jackson & Cohen, 2012;Storch et al., 2005). Inaddition, different from Cook, Williams,Guerra, Kim, and Sadek (2010),we found boyswere less likely to be victimized in general

compared with girls. The inconsistent findingsin this study and previous studies may speak tothe complexity of the role of gender in peervictimization. Other factors (e.g., grade, schoolclimate, peer norms) in addition to gender arelikely to play a role in peer victimization. Con-sistent with previous research (Nylund et al.,2007), we found girls were less likely to remainin thefrequent victimclass than boys over time.Furthermore, girls were more likely to remain inthe occasional cyber and traditional victimclass than boys, which is consistent with previ-

ous findings that girls experience more cybervictimization than boys (Kowalski & Limber,2007;Li, 2007). Over time, peer victimization


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decreased in frequency and developed into moreverbal/relational and cyber forms, especially forgirls. In general, our findings provide support toGilligans theory (Gilligan, 1982) that girls are

more concerned with relationships than boys.Grade differences. Our results indicated

that the youngest students at school (sixth grad-ers in the middle school and ninth graders in thehigh school) were less likely to be the frequentperpetrators and the oldest students at school(eighth graders in the middle school) were morelikely to engage in bullying perpetration, spe-cifically, verbal and relational bullying behav-iors. Considering that eighth graders were theoldest students at their middle school, they weremore likely to be taller, stronger, and have morepower compared with younger students, whichmay put them at a physical and social advantageto engage in bullying behaviors toward youngerstudents. Comparisons across grade levels indi-cated the probability of being a frequent victimdecreased from fifth to ninth grade, which alsosupports the decrease in peer victimization overtime(Smith et al., 1999). In addition, we foundthat ninth graders were more likely to be occa-sional cyber and traditional victims comparedwith other students. It is possible that ninth

graders have more access to Internet and cellphones, which increases their chance of beingcyber and traditional victims.

The effect of ELL status. Few studieshave examined the relationship between firstlanguage spoken and involvement in bullying/victimization over time. Although some previ-ous studies found that ELL students experi-enced higher level of peer victimization thannative speakers (von Grnigen et al., 2010),results from the current study did not support

this difference. Instead, ELL students weremore likely to be infrequent victims and to bebully perpetrators (frequentor occasional ver-bal/relational perpetrators) than native speak-ers; however, we were unable to test whetherthe difference is significant at each latent statusbecause the latent membership was not reportedin the LTA. We do not know whether ELLstudents mainly bullied other students of differ-ent ethnic backgrounds or within their own eth-nic groups. It is possible that as the minorityethnic group in their schools, ELL students en-

gaged in bullying behaviors toward othersslightly more often than their peers because theyfelt frustrated about language and cultural dif-

ferences. Future studies (e.g., qualitative studiesinterviewing ELL students about the reasons oftheir perpetration and victimization) are neededto better understand this phenomenon. Over-

time, native speakers were more likely to tran-sition from the frequent perpetratorsto the oc-casional verbal/relational perpetrators thanELL students. However, it is not clear whetherthis is related to their English language compe-tencies or not. As ELL students become moreproficient in English, are they more likely toengage in relationally bullying behaviors? Thisis an interesting question that deserves furtherexploration.

Limitations and Future Directions

There are several limitations of the currentstudy. First, only students self-report were used.Students may have underreported their experi-ences of bullying and peer victimization due tosocial desirability(Ivarsson, Broberg, Arvidsson,& Gillberg, 2005). However, one may argue thatstudents views regarding bullying and victimiza-tion are most important because they reflect theirpersonal experiences and are possibly more accu-rate because the bullying may be undetected by

teachers (Card & Hodges, 2008). Future studiesshould integrate information from multiple infor-mants (e.g., peers and teachers) to provide betterconstruct validity. Furthermore, students in thecurrent study were recruited from one city in theMidwest and the majority of students were Euro-pean American. The findings may not be readilygeneralizable to students living in rural areas orother socially and politically different areas. Fur-thermore, we did not assess ELL students Englishlanguage competency. Future studies should ex-

amine ELL students English language competen-cies to rule out whether language difficulties maycontribute to their experiences of bullying/victimization. Future researchers may also con-sider translating measures into languages otherthan English in order to capture the experiences ofvictimization among students with limited Englishlanguage competencies. In addition, the PRBmeasure has only shown acceptable psychometricproperties, with internal consistency above .70and somewhat limited support for the validity ofthe measure (only correlations with other mea-

sures). It is important to mention that differentitems in the PRB measure capture different sub-types of bullying behaviors, which contributes to


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relatively low reliability. Future studies are neededto continue examining the psychometric proper-ties of this measure, for example, using confirma-tory factor analysis to examine the factor structure

of the measure and reliabilities within the factorstructure(Raykov, 1997,2004). Last but not least,the occasional involvement groups refer to thestudents who self-reported bullying and/or victim-izationonce or twice in the past 2 months, whichmay fail to capture the repetitive nature of bully-ing. This is a limitation of the PRB measure,which was designed to assess bullying involve-ment in students across several countries (i.e.,Canada, Japan, Australia, Korea, and the UnitedStates). Given that the length of the school year isdifferent across countries and the structure of theschool day also varies across countries, the PacificRim research team delimited involvement to thepast 2 months in order to create a measure thatwas relevant across several Pacific Rim countries.

Implications

Because students involved in bullying and vic-timization represent heterogeneous subgroups,with different degrees of involvement and types ofbullying, it is important to develop prevention and

intervention efforts that address these differences.For example, interventions for the occasional cy-ber victim group should include psychoeducationon cyber safety to promote awareness, while in-terventions for the frequent victims should focuson strategies for dealing with physical, verbal, andrelational victimization. Interventions for frequentperpetrators should be more intense and involvepsychotherapy and coordination between homeand school(Swearer, Wang, Collins, Strawhun, &Fluke, 2014). Meanwhile, because results indi-

cated that cyber victimization tended to co-occurwith traditional victimization, we suggest that ed-ucators (a) identify such a group of students whoexperience pervasive (all different types of) vic-timization for specific interventions (e.g., psy-choeducation on the appropriate use of technol-ogy; and (b) continue their efforts on reducinggeneral victimization instead of shifting the focusfrom traditional victimization to cyberspace(Salmivalli, Sainio, & Hodges, 2013).

Considering that the oldest students (eighthgraders in the middle school) were more likely to

engage in bullying behaviors, and bullying perpe-tration increased after students transitioned intomiddle school, school personnel should focus their

intervention resources on students in the sixth andeighth grades. Furthermore, it is important forschool personnel to be aware of and pay attentionto verbal/relational and cyberbullying, because re-

sults suggest that peer victimization develops intomore verbal/relational and cyber types over time,especially for girls. Considering the gender differ-ences in peer victimization, different interventionsmay be warranted for boys and girls. Interventionsfor girls may focus on relationship issues andappropriate use of social media, whereas interven-tions for boys may address physical bullying. It isimportant for teachers and parents to talk to ado-lescents about cyber safety and to supervise ado-lescents Internet and mobile device use to help

prevent cyber victimization. It is also importantfor adults to take reports of verbal/relational bul-lying and cyberbullying seriously and to intervenenot only during physical bullying, but also withtypes of bullying that might be less obvious. Con-sidering that a small percentage of students areresponsible for the majority of bullying behaviors,it is important to consider the use of individualizedspecific interventions for those frequent perpetra-tors. Only when bullying interventions are devel-opmentally based, gender- and culturally sensi-tive, and address all types of bullying, will

American schools be free from bullying and cruelbehaviors.

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