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Journal of Contemporary Criminal Justice29(4) 454 –474

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Article

Reporting and Clearance of Cyberbullying Incidents: Applying “Offline” Theories to Online Victims

Lynn A. Addington1

AbstractCyberbullying continues to receive growing research attention, but much of this work focuses on prevalence estimates. Little is known about responses to these incidents. The present study relies on traditional theoretical explanations as a basis for modeling predictors for reporting to authorities and police clearance of cyberbullying using two national data sources. Initial support is obtained for the importance of incident seriousness and solvability characteristics for cyberbullying reporting and clearance. These findings suggest the utility of traditional theory to explain responses to cyberbullying, and also highlight a need for measures tailored to the cyber context to comprehensively test such models.

Keywordscyberbullying, Uniform Crime Reporting Program, National Incident-Based Reporting System, National Crime Victimization Survey

Introduction

A great deal of research attention has focused on bullying experienced by children and adolescents, particularly at school (Hong & Espelage, 2012). Recently this interest has expanded to cyberbullying due to perceptions that this electronic version of bullying is increasing with the saturation of technology and Internet usage across youth of all ages (McQuade, Colt, & Meyer, 2009; Ybarra, 2004) as well as high profile cases where cyberbullying victims took their own lives (Stewart & Fritsch, 2011). The cur-rent cyberbullying literature tends to be primarily descriptive with a focus on

1American University, Washington, DC, USA

Corresponding Author:Lynn A. Addington, Department of Justice, Law & Society, American University, 4400 Massachusetts Avenue, NW, Washington, DC 20016-8043, USA. Email: [email protected]

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estimating the prevalence of the problem (Holfeld & Grabe, 2012; Tokunaga, 2010). Little is known about the responses to these incidents in terms of victim reporting to officials as well as any subsequent actions taken by law enforcement. Obtaining a bet-ter understanding of these responses is important in part to determine which victims are most likely to be identified by authorities and receive assistance as well as those who may be underserved. In addition to these substantive limitations, critics have noted the absence of theoretical grounding in the current cyberbullying literature and a need to examine the applicability of traditional theory (Tokunaga, 2010).1

The present article seeks to address these gaps and contribute to cyberbullying research by exploring reporting and clearance responses using theoretical explanations previously applied to in-person victimization. In addition to responding to critiques regarding the atheoretical nature of cyberbullying research, this approach is taken for two reasons. First, it allows an initial assessment of the applicability of traditional theory to responses to online victimization. Second, relying on a theoretical frame-work readily identifies limitations in current data and highlights future work needed to ameliorate these problems.

Background

Cyberbullying Literature

Definition and prevalence. In the study of traditional bullying, a consensus has grown around common elements used to define these incidents and behaviors (Law, Shapka, Hymel, Olson, & Waterhouse, 2012). Currently, no single definition exists for cyber-bullying (Qi, Smith, & Cross, 2012; Tokunaga, 2010). Most definitions originate from traditional bullying behaviors and view cyberbullying as its technological extension. Differences arise regarding particular characteristics such as requiring repeated expe-riences or intent. Based on his meta-analysis of cyberbullying research, Tokunaga (2010) attempts to unify the various definitions with “cyberbullying is any behavior performed through electronic or digital media by individuals or groups that repeatedly communicates hostile or aggressive messages intended to inflict harm or discomfort on others” (p. 278). Tokunaga specifically notes that cyberbullying is not limited to the school setting or to known offenders.

As with its definition, no consistent measure is available for the prevalence of cyberbullying (Tokunaga, 2010). Estimates range widely, which is due in part to a lack of consistent definition but also to differences in sample and reference period. Samples vary in the ages or grades included, but tend to focus on either middle or high school students (Tokunaga, 2010). Shorter reference periods (such as the past month or school year) yield smaller percentages of victims as compared to longer exposure periods such as lifetime prevalence. On an average, 20% to 40% of adolescents report being the victim of cyberbullying (Tokunaga, 2010; see also Patchin & Hinduja, 2012).

Coping with cyberbullying: Reporting victimization experiences. Most cyberbullying stud-ies do not address whether victims report their experiences. When reporting is included,



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it is defined broadly to include telling a peer, school official, parent, or other adult. Reporting to police is rarely examined (Hinduja & Patchin, 2009). This pattern stands in stark contrast to studies of in-person victimization where reporting to police receives a great deal of research attention (Addington, 2011). A consistent finding from the cyberbullying work is that juveniles tend to report their victimizations to parents and friends rather than authorities (Watkins & Maume, 2011). When officials are told, juveniles are more likely to invoke the assistance of teachers or school administrators rather than police (Watkins & Maume, 2011). One reason for this tendency is the fact that most victimizations involving juveniles occur at school. A similar pattern of reporting is observed in studies of traditional bullying (Unnever & Cornell, 2004).

In the cyberbullying literature, studies that discuss reporting behaviors include them under a larger rubric of victim coping strategies (Parris, Varjas, Meyers, & Cutts, 2012; Tokunaga, 2010). Other forms of coping strategies involve responses to the immediate incident (such as ignoring the offender or deleting the text or email) as well as victim behaviors to prevent future victimization (such as confronting the bully or using self-help technological options to block messages or particular people; Parris et al., 2012). Immediate avoidance strategies such as deleting the message are the most common actions (Parris et al., 2012). Reporting the incident to an adult is an infre-quently exercised option (Slonje, Smith, & Frisen, 2013; see also Holfeld & Grabe, 2012; Hinduja & Patchin, 2009). The most consistent reason that adolescents give for not reporting cyberbullying is a fear that an adult—particularly a parent—either will take away access to or closely monitor use of the Internet, computers, cell phones, or other devices (Kowalski, Limber, & Agatston, 2008; McQuade et al., 2009; Mishna, Saini, & Solomon, 2009; see also Tokunaga, 2010).

When adolescents report cyberbullying, patterns of who is told vary by age and sex of the victim. In their summary of several studies, McQuade and his colleagues (2009) found that reports to an adult decrease from preteen to teen victims (see also Slonje et al., 2013). Half of the preteens told their parents as compared to 35% of teens, and 27% of preteens told a teacher compared to 9% of teens. Conversely, patterns of telling friends increase with age where 44% of preteen cyberbully victims told a friend as compared to 72% of teens (McQuade et al., 2009, p. 87). Reporting also appears to vary by victim’s sex and the person being told. A study of middle school students found that girls are slightly more likely than boys to report an incident to a peer (57% vs. 50%) and boys more likely than girls to tell a teacher (39% vs. 21%; Hinduja & Patchin, 2009, pp. 60-61).

Police involvement with cyberbullying. The very limited work that considers law enforce-ment in connection with cyberbullying suggests that these incidents come to the atten-tion of police infrequently. A study of middle school students found 2.7% of cyberbullying victims indicated that police were notified (Hinduja & Patchin, 2009, p. 62). Similarly, when victims inform adults about cyberbullying, these adults often do not call police. Parents do not tend to report their child’s cyberbullying victimizations due to uncertainty about involving the police (McQuade et al., 2009, p. 53).2 Anec-dotal evidence suggests that school officials may recommend victims or their parents



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call police, but officials themselves may be reluctant to directly contact police (Kow-alski et al., 2008). This hesitancy may be due to a lack of clarity over the school’s authority to intervene, especially for incidents that occur outside of school or involve students from other schools (Kowalski et al., 2008; McQuade et al., 2009).3

Perhaps due to the small number of cases that appear to come to the attention of police, no published study has examined police actions in response to reported cyber-bullying. As such, little is known about the characteristics of these cases or what police do. This omission is unfortunate as certain cyberbullying incidents do come to the attention of police. In addition, policy discussions have described two roles for police to address cyberbullying that could increase the number of cases involving law enforcement (McQuade et al., 2009; Stewart & Fritsch, 2011). One recommended role is for police to employ existing criminal laws as a mechanism to pursue cyber-bullying cases. While a growing number of states are enacting laws specifically aimed at cyberbullying, most of these laws concern school policy requirements rather than criminalizing cyberbullying per se (Cyberbullying Research Center, 2013; National Conference of State Legislatures, n.d.). In the absence of specific cyberbul-lying criminal laws, researchers have noted the applicability to cyberbullying of tra-ditional criminal laws on the books such as harassment, menacing, and stalking (McQuade et al., 2009; Stewart & Fritsch, 2011). A second role for police is to serve as a clear authority to whom victims can report incidents and obtain necessary ser-vices and assistance (Tokunaga, 2010; Vandebosch, Beirens, D’Haese, Wegge, & Pabian, 2012). For traditional forms of bullying that occur at school, teachers are easily identifiable resources for help (Unnever & Cornell, 2004; Watkins & Maume, 2011). The ubiquitous nature of cyberbullying that is not limited to any particular physical location does not readily lend itself to a comparable authority figure. Police could serve this function, especially if it were coupled with a larger policy initiative publicizing and supporting such a role.

Theoretical Framework

Explanations of victim reporting decisions. In the traditional criminal victimization litera-ture, Black’s social stratification hypothesis played a dominant role in framing initial explanations of police reporting patterns (Baumer, 2002). The stratification hypothesis utilizes Black’s (1976) behavior of law theory, which suggests that the amount of law one receives varies depending on the social rank of those involved in the incident. Here, stratification concerns social status and power as defined by characteristics such as sex, race, age, and income. Victims from a lower social rank (such as women, racial minorities, the young, and poor) are hypothesized to receive “less law” and therefore are less likely to call the police than those from a higher social rank.

Despite the prominence of Black’s stratification hypothesis in developing a framework for testing variations in police reporting, it has received limited support. Gottfredson and Gottfredson (1988) provide a contrasting explanation by applying a rational choice framework to various decision points in the criminal justice system including reporting to police. They identify crime seriousness as one of the most




influential determinants of reporting, and subsequent studies of in-person victimiza-tion support this hypothesis (e.g., Bosick, Rennison, Gover, & Dodge, 2012). Researchers observe comparable patterns with school crime and bullying. Victimization in school is more likely to be reported to police and school officials when the incident is more serious (as measured by injury and presence of a weapon; Watkins & Maume, 2011). Similarly with bullying, victims are more likely to report when the bullying is chronic (Unnever & Cornell, 2004). Studies considering other forms of cyber-victimization such as cyberstalking also have found indicia of seri-ousness (measured as feeling intimidated and losing time from work) predict police reporting (Reyns & Englebrecht, 2010).

Explanations of police clearance decisions. Studies of arrest patterns focus mainly on homicide clearance (but see Addington & Rennison, 2008; Roberts, 2009) and use two competing theoretical frameworks: one that incorporates extralegal characteristics based on Black’s stratification hypothesis and one that focuses on nondiscretionary “solvability” factors. To explain clearance patterns, characteristics rooted in Black’s hypothesis are used to suggest that police may engage in “victim preferencing” and be more likely to make an arrest in cases involving victims of higher social statuses (such as men, Whites, and adults) rather than those of lower statuses. In contrast, nondiscre-tionary characteristics explain arrest patterns based on the ease with which cases can be solved. For homicides, these characteristics relate to the available evidence and are measured in terms of weapons (with firearms less likely to leave evidence than per-sonal weapons), victim–offender relationship (with known offenders being easier to identify and arrest than strangers), and location (with home locations serving to protect crime scene evidence and suggest possible offenders as compared to other or outside locations; Riedel, 2008).

This extralegal framework is not supported in the homicide literature regarding age and sex as clearances more often occur in cases involving young victims, especially children, and females (both hypothesized as lower status groups) rather than older victims and males (see Riedel, 2008, for a summary). With regard to race, mixed sup-port has been obtained for Black’s hypothesis that fewer arrests will be made for minority race victims and more for minority race offenders (Riedel, 2008). In contrast, studies consistently find the predicted relationship between clearance and nondiscre-tionary solvability characteristics (Riedel, 2008).

Research Questions

The present study seeks to explore the applicability of traditional theoretical explana-tions to reporting and clearance in cyberbullying cases. Predictors rely on Black’s social stratification hypothesis (victim demographic characteristics), Gottfredson and Gottfredson’s decision-making model (incident seriousness), and nondiscretionary clearance factors (available evidence). Two exploratory research questions are posed: (1) do victim or incident seriousness characteristics predict reporting to authorities by cyberbully victims and (2) do victim or incident evidentiary characteristics predict



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police clearance of cyberbullying incidents. Based on previous research examining in-person victimization, it is hypothesized that incident seriousness will explain report-ing and solvability characteristics will explain clearance of cyberbullying cases.

Methodology

Data

To explore these research questions, this study relies on data from the National Crime Victimization Survey’s School Crime Supplement (NCVS-SCS) and the Uniform Crime Reporting Program’s (UCR) National Incident-Based Reporting System (NIBRS). Although criminologists have advocated using the NCVS and UCR data together in a complementary way (Biderman & Lynch, 1991; Lynch & Addington, 2007), such studies are unusual (Addington & Rennison, 2008). Here, the NCVS-SCS and NIBRS are used together in order to gain insights on responses to cyberbullying that could not be accomplished with either data set alone due to limitations inherent in each. Detailed information on each of these sources is presented below.

NCVS-SCS. This study uses NCVS-SCS 2009 data, which constitute one of the most recently available years from the National Archive of Criminal Justice Data (NACJD; United States Department of Justice, Office of Justice Programs, Bureau of Justice Statistics [BJS], n.d.). The NCVS is collected using a stratified, multistage cluster sample of households. Each household member aged 12 and older is interviewed regarding his or her experience with personal victimizations sustained during the pre-vious 6 months. The SCS is a periodic supplement to the NCVS that has been collected every other year since 1999. It gathers information from household members aged 12 to 18 concerning certain victimization experiences at school as well as characteristics of their school (BJS, n.d.). For the present research, the NCVS-SCS data have three primary strengths. First, these data rely on a large sample size (n = 4,414 for 2009),4 which results in an adequate number of cases to identify relatively rare experiences like cyberbullying victimization. Second, the SCS asks questions about cyberbullying that permit identification of these victims. Finally, the NCVS-SCS captures relevant demographic details about the victim.

NCVS-SCS data have certain limitations, and four are relevant to this study. First, data are only collected for household members between ages 12 and 18. While cyber-bullying is less likely to occur among very young children,5 these victims are excluded by design from the NCVS sample. Second, to be eligible to answer the SCS questions, the respondent must attend school at some point during current academic year. This criterion excludes those who are completely homeschooled or who dropped out of school. To the extent these individuals are more likely to experience cyberbullying, the number of victims may be undercounted. Third, while the main NCVS collects details about the victimization incident, the SCS does not gather comparable information for cyberbullying such as the location, offender characteristics, or police notification and activity including arrests. As a result, this information is not available. Finally, the




NCVS-SCS asks about victim reporting of cyberbullying, but it is limited to reporting to “a teacher or some other adult at school” (NCVS-SCS Q20c). No explicit informa-tion is collected about reporting to police.6 As discussed earlier, teachers are frequently the authority to whom adolescents report their victimization experiences (Watkins & Maume, 2011). While reporting to a teacher likely constitutes a good proxy for report-ing to an official, measurement error is introduced because school officials are not police. Despite these limitations, the NCVS-SCS provide a relevant source of cyber-bullying data for this study based on its national coverage and collection of reporting information.

NIBRS. This study also uses victim-level data from the 2008 NIBRS Extract Files provided by the NACJD (2010). These data are selected as they constitute one of the more recent years of publicly available NIBRS data, and the 2008 data year is the most comparable to the 2009 NCVS-SCS, which uses a reference period covering the 2008-2009 school year. NIBRS data provide victim and offender demographics as well as information about the crime such as whether a computer was used and if an arrest was made.

One caveat in analyzing NIBRS data is its limited coverage. NIBRS is a substan-tial departure in crime data collection for law enforcement agencies and requires a lengthy certification process (see Addington, 2004, for details). As a result, the con-version to NIBRS has been gradual and has yet to achieve 100% coverage nation-ally. In 2008, 31 states were NIBRS certified. Within these 31 states, 9 states fully reported in NIBRS and an additional 4 states had over 94% NIBRS participation (Justice Research and Statistics Association [JRSA], n.d.-b). Overall, NIBRS agen-cies covered approximately 25% of the U.S. population in 2008 (JRSA, n.d.-b). Law enforcement agencies that participate in NIBRS tend to represent smaller population areas. No agency covering a population of more than 1 million participated in NIBRS in 2008 (JRSA, n.d.-a). Because participation is voluntary, NIBRS states and law enforcement agencies do not constitute a representative sample of U.S. law enforcement agencies or states. This nonrepresentativeness suggests exercising cau-tion when interpreting the results and generalizing beyond the NIBRS-participating agencies included in this study (but see Addington, 2008). Given this caveat, under-standing the capabilities of NIBRS is important for informing on the issue of police-reported cyberbullying.

Case definition and sample. For the present study, both the NCVS-SCS and NIBRS data sets include only those victims who report being cyberbullied. For the NCVS-SCS, a student counts as a cyberbully victim if that student answered “yes” to any of six par-ticular behaviors that include threats and insults via various electronic media as well as online exclusion activity. The most common behavior reported is being threatened or insulted through text messaging, which half the victims reported. These criteria resulted in a sample of 272 cases or 6% of all NCVS-SCS respondents.

While cyberbullying is not a NIBRS crime per se, information from the NIBRS file can be used to define these cases. This information concerns the type of crime and



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whether a computer was used in the crime. To measure cyberbullying, the crime of intimidation is used. The Federal Bureau of Investigation (FBI) defines intimidation as to “unlawfully place another person in reasonable fear of bodily harm through the use of threatening words and/or other conduct but without displaying a weapon or subject-ing the victim to actual physical attack” (FBI, 1992, p. 23). This definition is compa-rable to the “menacing” offense used by McQuade and colleagues (2009) and Stewart and Fritsch (2011) in their discussions of existing laws that could criminalize cyber-bullying behavior. To identify cyberbullying victims within intimidation incidents, cases are included if “computer” is selected for the NIBRS variable “offender sus-pected of using” to commit the crime. In addition, the victim and all offenders must have been 18 years old or younger.7 Selecting incidents where both victim and offender are 18 years of age or younger best operationalized cyberbullying rather than adult-related offenses of cyber-harassment and cyber-stalking. These criteria result in a sample size of 221 cyberbullying victims in the NIBRS file.

As noted by one anonymous reviewer, the case selection criteria result in relatively small samples, especially in light of the size of the original NCVS-SCS and NIBRS data. These final numbers, though, appear to be consistent with the nature of the under-lying data. For the NCVS-SCS, the survey’s reference period is limited to the current school year. This short time period will identify fewer victims than a longer one. For NIBRS, previous research indicates that few cases come to the attention of police. NIBRS does not have national coverage and large jurisdictions are underrepresented, which also reduces the number of available cases.

Measures. The measures used in this study are described below. The outcome mea-sures are discussed first and then the predictor variables. The frequencies for all the variables as well as their coding are presented in the appendix.

Outcome measures. Two outcome measures are used: reported to officials (for NCVS-SCS) and clearance (for NIBRS). Reported to officials is measured as reported to a teacher or other adult at school and is coded as a dichotomous variable of reported or not. Clearance is measured as a case that ended in an arrest or was otherwise excep-tionally cleared.8 It is also coded as a dichotomous variable of cleared or not.

Predictor measures. Based on Black’s social stratification hypothesis, several vic-tim characteristics are posited to be related to reporting and clearance, particularly sex, race, and economic status. In addition, incident variables in the NCVS-SCS and NIBRS data permit exploration of alternative theoretical explanations concerning seri-ousness (for reporting) and solvability (for clearance).

Victim characteristics. For the NCVS-SCS data, victim characteristics include sex, age, race, and household income. Sex is coded as a victim being female or not. Age is a continuous variable. Race is dichotomized as White and non-White due to the small number of minority victims who are not African American. Household income is categorized as lowest (under $25,000), low (between $25,000 and $49,999), middle




(between $50,000 and $74,999), and high (over $75,000). Because of the large amount of item missing data for income, a missing income category is included to prevent a substantial loss of cases.

For NIBRS data, victim characteristics include sex, age, and race. Information about victim income is not collected in the NIBRS data. As with the NCVS-SCS data, sex is coded as a victim being female or not, and age is a continuous variable. Unlike the NCVS-SCS, NIBRS includes victims of all ages. For purposes of cyberbullying, only two victims were under the age of 11 and the decision was made to remove these cases from the data set. The final NIBRS ages range from 11 to 18. For race, the NIBRS models compare White and non-White victims due to the small number of minority victims who are not African American.

Offender characteristics. As noted above, a limitation with the NCVS-SCS data is the lack of incident details for cyberbullying victimizations. As such, no offender characteristics are available. For NIBRS, the offender demographic characteristics focus on offender sex. Here, sex is coded as the offender being male or not.9 The deci-sion to use one offender demographic was made due to the large amount of missing offender demographic data and the high correlation of these missing data with clear-ance. Offender sex is selected since it is the demographic characteristic with the least amount of missing data (as compared to offender race and age).

Incident characteristics. To explore the relationship between seriousness and reporting, the NCVS-SCS data are limited as incident characteristics are not collected for cyberbullying incidents with the exception of frequency with which the cyberbul-lying occurred.10 Based on the guidance from previous studies of traditional bullying (Unnever & Cornell, 2004), frequency is used as an indicator of seriousness and is measured as more than one time per week.

For NIBRS, no comparable frequency measure is available due to the incident-based nature of these data. Other incident characteristics are collected and can be used to explore two of the solvability factors identified in previous studies: location and victim–offender relationship. For cyberbullying, location is defined as home or not based on findings from previous studies finding that home location predicted clear-ance of traditional, in-person crimes. Victim–offender relationship also is included in the analyses. In previous homicide clearance studies, known relationships such as inti-mate partners or family members increase the likelihood of a case being cleared. Here, victim–offender relationship is categorized as friend, otherwise known (which includes intimate partners, family members, and other known relationships), and stranger/unknown relationship. These relationship groups are selected given the frequency with which friends appear as a relationship in this adolescent sample.

Analyses Conducted

The NCVS-SCS and NIBRS data are analyzed separately but both use binary logistic regression models given the dichotomous outcome measures. As indicated in the



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appendix, most predictor variables have little missing data. Three exceptions concern income, victim–offender relationship, and offender sex. For the NCVS income variable and the NIBRS victim–offender relationship variable, “unknown” categories are included in the analysis to minimize the loss of cases. A problem arose with offender sex in the NIBRS data. Here, an “unknown” category could not be included to account for missing data. Cases with missing offender demographic data are not cleared, so these missing data perfectly predict the outcome variable. Instead, missing data are handled using complete case analysis (Allison, 2002). This technique resulted in a loss of 20% of the cases. To explore the possible introduction of bias, models were esti-mated with and without the offender sex variable. While no differences in statistical significance among the remaining correlates were observed, caution will need to be exercised in interpreting the offender sex variable because of the relationship between its missing cases and clearance.

Two additional analytical concerns arise from the NCVS due to its design. One issue concerns the stratified, multistage cluster sample employed by the NCVS. Ideally, variances should be adjusted to account for this complex sampling design to ensure accurate significance testing. Due to the extreme subsetting of the data, vari-ance adjustments such as Taylor series linearization could not be satisfactorily accom-plished. Caution must be exercised in interpreting significance tests relying on these unadjusted standard errors, especially coefficients with a p value close to the 0.05 alpha level. An additional analytical issue concerns whether to weight the data. The NCVS-SCS data include weights to take into account non-response as well as post-stratification on age, race, and sex (BJS, n.d.). Due to the exploratory nature of this study which does not seek to make inferences about the U.S. population of students, unweighted data are used.

Findings

Descriptive Analyses of Cyberbullying

Before turning to multivariate analyses, a few descriptive findings are of note, espe-cially for the NIBRS data, since no current research has examined cases that come to the attention of police. In both the NCVS-SCS and NIBRS data, victims tend to be female, White, and around 15 years of age (see the appendix). Examining the NCVS-SCS data indicates that over two thirds of victims (68.4%) do not report their experi-ences to school officials, which is consistent with previous studies (Tokunaga, 2010). Almost one third of cyberbullying victims are from households with the highest income, which may be related to a greater accessibility to technology for these students. Almost 16% of cyberbullying victims report being victimized at least once a week.

Of those incidents that come to the attention of police, over a quarter (28.5%) are cleared. The NIBRS data suggest that offenders tend to be male and that most cyberbul-lying occurs between known offenders, specifically friends, and at home. These charac-teristics may reflect the nature of cases that come to the attention of police. The NIBRS data did not provide a seriousness measure in terms of frequency, but incidents involving




male offenders might be viewed as more serious (especially given the large proportion of female victims) and more likely to involve police. Cases with a known offender may be more likely to come to the attention of police, because victims may believe in these situations police are better able to handle the incident. Over three fourths of cyberbully-ing involving police occurs at home. The dominance of home locations may be a func-tion of police being the source of these data rather than school officials, for example, who might receive more reports of school-related cyberbullying.

Predictors of Reporting Cyberbullying

Table 1 presents the logistic regression model that predicts reporting by cyberbully-ing victims. Being female is the only victim characteristic that is a statistically sig-nificant predictor of reporting. Specifically, the odds of reporting to a teacher or other adult at school increase by 1.98 for female rather than male victims. Income (specifically the lowest income category) is not statistically significant at the 0.05 alpha level, but would be if a 0.10 alpha level were selected. This relationship sug-gests that cyberbullying victims in the lowest household income bracket (under $25,000) are more likely to report to a teacher than those in the highest household income bracket (over $75,000). Both these findings are contrary to Black’s hypoth-esis that these “lower” status victims would be less likely to report than higher status victims. These findings are similar to patterns observed for traditional in-person victimization studies.

Table 1. Binary Logistic Regression Model Predicting Reporting to School Officials, NCVS-SCS 2009 (Unweighted Data).

Variables β/Exp(β) SE p value

Victim characteristics Female 0.68/1.98 0.30 .02* Age −0.12/0.89 0.08 .13 White 0.06/1.06 0.32 .85 Lowest incomea 0.84/2.32 0.44 .06 Low incomea 0.30/1.35 0.39 .44 Middle incomea −0.06/0.94 0.44 .89 Unknown/missing incomea 0.31/1.36 0.41 .46 Cyberbullying frequency 0.89/2.43 0.35 .01*

−2 log likelihood 317.08Model χ2 22.36*Hosmer and Lemeshow 6.72Nagelkerke R2 .11N 272

Note. NCVS-SCS = National Crime Victimization Survey’s School Crime Supplement.aAs compared to high income.*p < .05.



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Incident seriousness appears to predict reporting. Victims who were cyberbullied once a week or more are statistically significantly more likely to report to a teacher than those who were cyberbullied less often. The odds of reporting increase by 2.43 for frequent victims as compared to less frequent victims. This finding is consistent with Gottfredson and Gottfredson’s model and in-person victimization studies that find crime seriousness an important predictor of reporting to police. This finding is also consistent with studies of traditional bullying where chronic victims are more likely to report their experiences (Unnever & Cornell, 2004).

Predictors of Police Clearance for Cyberbullying Cases

Table 2 presents the logistic regression model predicting clearance. Two predictors are statistically significant: stranger/unknown victim–offender relationships and male offenders. Incidents involving strangers or unknown offenders are statistically signifi-cantly less likely to be cleared than those involving friends. A stranger or unknown offender decreases the odds of clearance by 0.29 as compared to friends who are offenders. No statistically significant difference in clearance is observed between offenders who are friends and those who are otherwise known. This finding is consis-tent with nondiscretionary explanation of clearance found in homicide research. Cyberbullying incidents that involve male rather than female offenders are statistically

Table 2. Binary Logistic Regression Model Predicting Police Clearance, NIBRS 2008.

Variables β/Exp(β) SE p value

Victim characteristics Female 0.57/1.76 0.40 .16 Age −0.05/0.95 0.09 .57 White 0.57/1.77 0.47 .23Offender characteristics Male offender 1.11/3.03 0.38 .003*Incident characteristicsVictim-offender relationship Otherwise knowna −0.27/0.77 0.40 .51 Stranger/unknowna −1.24/0.29 0.61 .04*Location Home 0.06/1.06 0.41 .89

−2 log likelihood 213.44Model χ2 14.36*Hosmer and Lemeshow 14.00Nagelkerke R2 0.11N 174

Note. NIBRS = National Incident-Based Reporting System.aAs compared to friend.*p < .05.




significantly more likely to be cleared. Incidents with male offenders increase the odds of clearance by 3.03. As noted above, caution should be exercised in interpreting offender sex since the missing cases dropped from the analysis predict an incident not being cleared. The loss of these cases also may explain the large odds ratio reported.

Discussion

This study sought to explore reporting and clearance patterns for cyberbullying using theoretical explanations typically applied to in-person victimization. The findings obtained provide important initial insights regarding the applicability of traditional theories to cyberbullying as well as a preliminary understanding of cyberbullying cases that come to the attention of police, which is the area largely ignored in the lit-erature. The following discussion places these findings in a larger context and explores how they can provide guidance for future work to improve data sources by developing requisite measures tailored to the cyber context.

This study posed two exploratory research questions. The first focused on reporting and considered whether victim demographics or incident seriousness predicts report-ing to authorities. As hypothesized from the traditional victimization literature, the findings obtained provide preliminary support for the positive relationship between seriousness and reporting suggested by the Gottfredson and Gottfredson framework. These findings also can be considered in a policy context. Patterns where females and frequent victims are more likely to report affect local officials’ understanding of the extent and nature of the cyberbullying problem at their schools and in turn the policies devoted to addressing the problem as they understand it. Underreporting by certain victims can result in a misallocation of resources. Future work should consider the extent to which reporting patterns accurately reflect the underlying problem to ensure effective policy. Reporting also affects who receives support and services. Seriousness may represent an effective filter for identifying those victims in most need of assis-tance, but future work should confirm this relationship and ensure that all victims in need of assistance receive support.

The second research question examined clearance and whether victim and offender demographics or available evidence predict police clearance. As hypothesized from the traditional victimization literature, preliminary support is found for the nondiscre-tionary clearance explanation since cases involving strangers or unknown offenders are much less likely to be cleared than those involving friends or otherwise known offenders. Known offenders are likely easier to clear because of the information about the offender and other evidence may be available. Incidents involving male offenders also predict clearance. As suggested above, one explanation for this finding may be that cases involving male offenders are viewed as more serious by police, especially given the large proportion of female victims.11 Traditional theoretical explanations suggest that police take greater effort to handling and clearance of cases considered to be more serious (Gottfredson & Gottfredson, 1988; Klinger, 1997).12

In addition to addressing these two research questions, this study sought to illus-trate the utility of NCVS-SCS and NIBRS data in a complementary way. The findings



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obtained show that certain patterns can be readily identified by using victim and police data together. One interesting finding is the similarity of victim characteristics where the most are White, an average age of 15, and girls in both the NCVS-SCS and NIBRS. This pattern suggests comparable demographics of cyberbullying victims overall (whether they reported their experiences or not) and those whose cases came to the attention of police. Future work is needed to explore this finding, specifically how these cases may differ in ways not identified in this study to better understand the nature of this subset of cyberbullying cases that are reported to police.

The present study is an important first step in examining reporting and clearance in the context of cyberbullying. While both the NCVS-SCS and NIBRS have limitations for studying cyberbullying, exploring these data sets using a theoretical framework provides a basis for identifying additional measures needed to allow for more comprehensive tests of these theoretical models. In particular, applying traditional theory to reporting and clearance in this study highlights the need to translate certain in-person incident charac-teristics to the cyberbullying context in order to more accurately test these theories.13 Two examples concern measures of seriousness and clearance factors. While the following discussion arises from the NCVS-SCS and NIBRS context of this study, the underlying issues are applicable to those considering collecting data in this area.

With regard to seriousness, the traditional in-person victimization literature tends to focus on violent crime. As a result, seriousness often is measured in terms of weapon use and injury (Bosick et al., 2012). These characteristics are not applicable to cyber-bullying. Others, though, could be identified and developed such as frequency (as used in the present study) as well as chronic experiences, potential for harm, and costs. Studies of traditional bullying measure “chronicity” by using a composite indicator that includes frequency as well as duration of the bullying, number of bullies, and number of locations where the bullying occurred. Chronic experiences with cyberbul-lying could be measured similarly with regard to frequency and duration, but adapted for the cyber context where “location” might be different forms of cyberbullying such as texts, emails, or postings on social media. The potential for harm could use content of the messages or posting of information on particular websites. With regard to mes-sage content, threats of physical violence or other criminal activity such as sexual harassment or extortion could be counted as indicators of serious cyberbullying inci-dents (Kowalski et al., 2008). Posting compromising pictures in general or posting information on adult websites may be considered as creating greater potential harm to the victim and included as indicia of seriousness (Wolak, Finkelhor, & Mitchell, 2012). Finally, costs for cyberbullying could be operationalized as lost time from school. This list is not intended to be exhaustive, but illustrative. Future research is needed to vali-date these measures and develop others.

With regard to clearance factors, traditional views of evidence concerning weapons are not applicable to cyberbullying. Other measures such as location need to be adapted to the cyber context. Unlike in-person victimization where all actors are present in one location, measures of location become challenging in the cyberbullying since it is unclear where the cyberbullying occurs or where it should be determined to occur (i.e., where the offender acts or where the victimization occurs in terms of the victim




receiving the text, for example). Given the nature of cyberbullying, it is also unclear whether the location has a similar relationship with clearance. The current study sug-gests that home location does not predict clearance, but more work is needed to explore this relationship. Other clearance measures need to be developed for the cyberbullying context. The present study suggests that cases involving known offenders are more likely to be cleared. This finding may be attributable to the offenders being known. Alternatively, it may be that “known offender” is a proxy for characteristics about the cyberbullying that lead the victim to be able to identify the offender such as the type of message, witnesses, or an admission by the offender. These underlying characteris-tics could be developed as clearance factors. These examples are somewhat specula-tive, as so little is known about cyberbullying cases that are reported to police. More work is needed to identify how police handle these cases and what characteristics are relevant to clearance in this context.

This study is an important initial examination of reporting and clearance for cyberbullying cases, but it is not without its limitations. The main caveats arise from measurement issues in both datasets and are discussed, in part, above in connection with the ways to improve measures. Two of these issues concern the NCVS-SCS data. First, no measure is collected regarding reporting to police, so this study relied on reports to teachers and adults. Second, limited information is available about these cases, particularly offender and incident information as well as reasons why incidents were or were not reported by the victim. An additional set of limitations concerns NIBRS data and how cyberbullying is measured. Here, a proxy measure of intimidation using a computer is used to measure cyberbullying. This measure has face validity, but it makes two assumptions regarding police practice. First, it assumes that police are applying the law in this manner.14 Second, it assumes that law enforcement agencies follow similar NIBRS reporting practices. Local agencies may vary in how they utilize optional data elements such as the offender’s use of a computer (Addington, 2004). This variation could introduce measurement error into the counts of these cases.

Conclusion

This study sought to explore the applicability of traditional theories to reporting and clearance of cyberbullying. The findings obtained provide three important insights. First, this work finds that traditional theoretical explanations provide useful guidance, but measures tailored to cyber-related incidents are needed to more fully test these theories. Second, with regard to reporting, this study finds that females and victims of frequent cyberbullying are more likely to report than males and victims who experi-ence cyberbullying but do so infrequently. This result suggests the need for additional work to ensure that victims who need assistance are being identified by authorities. Third, given the lack of information about how police handle cyberbullying cases, this study provides unique insights regarding the cases that come to the attention of police. More work is needed to better understand police use of discretion in these cases and other incident characteristics that predict clearance. Based on these findings, this study



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Appendix. Study Variables Frequencies and Mean, NCVS-SCS 2009 and NIBRS 2008.

Variable Coding

Frequencies (%)

NCVS-SCS NIBRS

Outcome variable Reported to officials 1 = reported

0 = not reported31.668.4

n/a

Clearance 1 = cleared0 = not cleared

n/a 28.571.5

Victim characteristics Female 1 = female

0 = maleMissing

59.940.1

0

70.6290.5

Age ContinuousNCVS-SCS 12-18NIBRS 11-18

M = 15 years M = 14.8 years

Race 1 = white0 = nonwhiteMissing

70.628.4

0

82.415.42.3

Household income LowestLowMiddleHighUnknown/missing

12.920.215.135.716.2

n/a

Offender characteristics Male 1 = male

0 = femaleMissing

n/a 4336.720.4

Incident characteristics Victim–offender

relationshipFriendOtherwise knownStranger/unknown

n/a 50.718.630.8

Home location HomeOther

n/a 78.721.3

Cyberbullying frequency

1 = more than 1x/wk0 = less than 1x/wk

15.884.2

n/a

Note. Income is included in the binary logistic model as a categorical variable. High income is used as the comparison group. Victim–offender relationship is included in the binary logistic regression model as a categorical variable. Friend is used as the comparison group. NCVS-SCS n = 272. NIBRS n = 221. Percentages might not equal 100% due to rounding. NCVS-SCS = National Crime Victimization Survey’s School Crime Supplement; NIBRS = National Incident-Based Reporting System.

outlines the next steps for future research by starting to identify additional measures needed to study reporting and clearance. Improving available data resources will enable comprehensive testing of theoretical models, which in turn can help inform policy to support victims and official handling of these incidents.




Acknowledgment

An earlier version of this paper was presented at the 2010 annual meeting of the Association of State UCR Programs in Boston, MA.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

1. More recent studies have applied traditional theoretical explanations to cyberbullying including routine activity theory (Navarro & Jasinski, 2012) and general strain theory (Hay, Meldrum, & Mann, 2010; Patchin & Hinduja, 2011).

2. One anonymous reviewer noted that cyberbullying may not be a crime and therefore would not be reported. Researchers have identified a number of reasons why victims (or others such as parents or bystanders) might invoke police assistance whether or not the incident is actually a crime such as needing help in general (e.g., Xie & Lauritsen, 2012). The deci-sion to report also includes identifying the incident as a crime (Gottfredson & Gottfredson, 1988). This determination, though, is made by the citizen and may or may not be technically accurate. Few lay people know that every statute and more publicized cases of cyberbully-ing may lead the average citizens to believe that this behavior is within the scope of police authority. This confusion is not limited to the general public. School resource officers are often unclear whether their state has a cyberbullying law (Hinduja & Patchin, 2012).

3. As noted by one anonymous reviewer, many cyberbullying laws give school officials the authority to act (Hinduja & Patchin, 2011). Despite this authority, confusion may arise over exercising it. In the years since the shootings at Columbine High School, schools have been inundated by changes in state laws and local policies concerning student safety and bully-ing issues (Muschert & Peguero, 2010). The frequency of these changes might contribute to this confusion.

4. While almost 9,000 National Crime Victimization Survey (NCVS) respondents are between the ages of 12 and 18 during the fielding of the 2009 School Crime Supplement (SCS; n = 8,986), less than half of these respondents participated in the SCS questions due to refusals and ineligibility.

5. A growing number of elementary school students, though, have access to computers and the Internet. Reports indicate that 63% of kindergarteners and first-grade students spend time online (McQuade, Colt, & Meyer, 2009).

6. NCVS-SCS respondents might include school resource officers or other school security personnel as “some other adult,” but these officials cannot be particularly identified based on the structure of the question.

7. Offenders also are included if no age is known due to concerns that known offender age would skew the clearance findings. Over a quarter (28%) of offender ages are unknown.

8. The term clearance is used rather than the more specific term arrest. This terminology parallels the Federal Bureau of Investigation’s practice of considering a crime cleared or



Addington 471

“solved” for crime reporting purposes if there has been either an arrest or activity con-stituting clearance by exceptional means (Federal Bureau of Investigation [FBI], 2004). Clearance by exceptional means refers to situations where a suspect has been identified, but circumstances beyond the agency’s control prevent an arrest.

9. The National Incident-Based Reporting System (NIBRS) Extract File includes demo-graphic information for up to three offenders. Offender sex is measured using information from the first offender since the vast majority of cases (83%) involve one offender.

10. The NCVS-SCS measures frequency categorically as experiencing cyberbullying behav-iors once or twice during the school year, once or twice a month, once or twice week, or almost every day.

11. A model not presented included an interaction term for female victims and male offenders. As this interaction was not significant, it is not reported in the final model presented in Table 2.

12. Even within the context of cybercrime, research suggests that police view different cyber-crimes as being of varying seriousness and worthy of different attention (Holt & Bossler, 2012).

13. Some omitted measures do not need to be adapted and could be readily available from the NCVS-SCS if information for cyberbullying comparable to the incident details gathered for other victimizations such as victim-offender relationship and offender details as well as direct measure of police reporting and reasons for reporting (or not).

14. An anonymous reviewer suggested that a state may have a specific law criminalizing cyberbullying. In this situation, the police might not use the intimidation crime designa-tion. A post hoc review of the states included in the NIBRS file indicated that 2 of the 27

states in the sample had laws that specifically criminalized cyberbullying in 2008.

References

Addington, L. A. (2004). The effect of NIBRS reporting on item missing data in murder cases. Homicide Studies, 8, 1-21.

Addington, L. A. (2008). Assessing the extent of nonresponse bias on NIBRS estimates of vio-lent crime. Journal of Contemporary Criminal Justice, 24, 32-49.

Addington, L. A. (2011). Current issues in victimization research and the NCVS’s ability to study them. In A dialogue between bureau of justice statistics and the justice system com-munity. Retrieved from http://www.bjs.gov/content/pub/pdf/bjsworkshop.pdf

Addington, L. A., & Rennison, C. M. (2008). Co-occurring rape: Do additional crimes affect how victims and police respond to rape? Journal of Quantitative Criminology, 24, 205-226.

Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage.Baumer, E. (2002). Neighborhood disadvantage and police notification by victims of violence.

Criminology, 40, 579-616.Biderman, A. D., & Lynch, J. P. (1991). Understanding crime incidence statistics: Why the

UCR diverges from the NCS. New York, NY: Springer.Bureau of Justice Statistics. (n.d.). National Crime Victimization Survey: School crime supple-

ment 2009 codebook. Ann Arbor, MI: ICPSR.Black, D. (1976). The behavior of law. New York, NY: Academic Press.Bosick, S. J., Rennison, C. M., Gover, A. R., & Dodge, M. (2012). Reporting violence to the

police: Predictors through the life course. Journal of Criminal Justice, 40, 441-451.Cyberbullying Research Center. (2013). State cyberbullying laws. Retrieved from http://cyber-

bullying.us/Bullying_and_Cyberbullying_Laws.pdf


http://www.bjs.gov/content/pub/pdf/bjsworkshop.pdf

http://cyberbullying.us/Bullying_and_Cyberbullying_Laws.pdf

http://cyberbullying.us/Bullying_and_Cyberbullying_Laws.pdf



Federal Bureau of Investigation. (1992). Uniform crime reporting handbook (National Incident-Based Reporting System edition). Washington, DC: U.S. Government Printing Office.

Federal Bureau of Investigation. (2004). Uniform crime reporting handbook. Washington, DC: U.S. Government Printing Office.

Gottfredson, M. R., & Gottfredson, D. M. (1988). Decision making in criminal justice. New York, NY: Plenum.

Hay, C., Meldrum, R., & Mann, K. (2010). Traditional bullying, cyber bullying, and devi-ance: A general strain theory approach. Journal of Contemporary Criminal Justice, 26, 130-147.

Hinduja, S., & Patchin, J. W. (2009). Bullying beyond the schoolyard: Preventing and respond-ing to cyberbullying. Thousand Oaks, CA: Corwin Press.

Hinduja, S., & Patchin, J. W. (2011). Cyberbullying: A review of the legal issues facing educa-tors. Preventing School Failure, 55, 71-78.

Hinduja, S., & Patchin, J. W. (2012). School law enforcement and cyberbullying. In J. W. Patchin & S. Hinduja (Eds.), Cyberbullying prevention and response: Expert perspectives (pp. 161-184). New York, NY: Routledge.

Holfeld, B., & Grabe, M. (2012). An examination of the history, prevalence, characteristics and reporting of cyberbullying in the United States. In Q. Li, D. Cross, & P. K. Smith (Eds.), Cyberbullying in the global playground: Research from international perspectives (pp. 117-142). Hoboken, NJ: Wiley-Blackwell.

Holt, T. J., & Bossler, A. M. (2012). Police perceptions of computer crimes in two Southeastern cities: An examination from the viewpoint of patrol officers. American Journal of Criminal Justice, 37, 396-412.

Hong, J. S., & Espelage, D. L. (2012). A review of research on bullying and peer victimization in school: An ecological system analysis. Aggression and Violent Behavior, 17, 311-322.

Justice Research and Statistics Association. (n.d.-a). 50 largest NIBRS agencies. Retrieved from http://www.jrsa.org/ibrrc/index.html

Justice Research and Statistics Association. (n.d.-b). Status of NIBRS in the states. Retrieved from http://www.jrsa.org/ibrrc/background-status/nibrs_states.shtml

Klinger, D. A. (1997). Negotiating order in patrol work: An ecological theory of police response to deviance. Criminology, 35, 277-306.

Kowalski, R. M., Limber, S. P., & Agatston, P. W. (2008). Cyber bullying: Bullying in the digi-tal age. Malden, MA: Blackwell Publishing.

Law, D. M., Shapka, J. D., Hymel, S., Olson, B. F., & Waterhouse, T. (2012). The changing face of bullying: An empirical comparison between traditional and internet bullying and victimization. Computers in Human Behavior, 28, 226-232.

Lynch, J. P., & Addington, L. A. (2007). Conclusion. In J. P. Lynch & L. A. Addington (co-editors), Understanding crime statistics: Revisiting the divergence of the NCVS and the UCR (pp. 297-334). Cambridge, UK: Cambridge University Press.

McQuade, S. C., III., Colt, J. P., & Meyer, N. B. B. (2009). Cyber bullying: Protecting kids and adults from online bullies. Westport, CT: Praeger.

Mishna, F., Saini, M, & Solomon, S. (2009). Ongoing and online: Children’s and youth’s per-ceptions of cyber bullying. Children and Youth Services Review, 31, 1222-1228.

Muschert, G. W., & Peguero, A. A. (2010). The Columbine effect and school antiviolence policy. Research in Social Problems and Public Policy, 17, 117-148.

National Archive of Criminal Justice Data. (2010). National incident-based reporting system, 2008: Extract files [Computer file]. Ann Arbor, MI: ICPSR.


http://www.jrsa.org/ibrrc/index.html

http://www.jrsa.org/ibrrc/background-status/nibrs_states.shtml


Addington 473

National Conference of State Legislatures. (n.d.). Cyberbullying and the states. Retrieved from http://www.ncsl.org/issues-research/justice/cyberbullying-and-the-states.aspx

Navarro, J. N., & Jasinski, J. L. (2012). Going cyber: Using routine activities theory to predict cyberbullying experiences. Sociological Spectrum, 32, 81-94.

Parris, L., Varjas, K., Meyers, J., & Cutts, H. (2012). High school students’ perceptions of cop-ing with cyberbullying. Youth & Society, 44, 284-306.

Patchin, J. W., & Hinduja, S. (2011). Traditional and nontraditional bullying among youth: A test of general strain theory. Youth & Society, 43, 727-751.

Patchin, J. W., & Hinduja, S. (2012). Cyberbullying: An update and synthesis of the research. In J. W. Patchin & S. Hinduja (Eds.), Cyberbullying prevention and response: Expert perspec-tives (pp. 13-35). New York, NY: Routledge

Qi, L., Smith, P. K., & Cross, D. (2012). Research into cyberbullying: Context. In Q. Li, D. Cross, & P. K. Smith (Eds.), Cyberbullying in the global playground: Research from inter-national perspectives (pp. 3-12). Hoboken, NJ: Wiley-Blackwell.

Reyns, B.W., & Englebrecht, C. M. (2010). The stalking victim’s decision to contact the police: A test of Gottfredson and Gottfredson’s theory of criminal justice decision making. Journal of Criminal Justice, 38, 998-1005.

Riedel, M. (2008). Homicide arrest clearances: A review of the literature. Sociology Compass, 2, 1145-1164.

Roberts, A. (2009). Contributions of the National Incident-Based Reporting System to substan-tive knowledge in criminology: A review of research. Sociology Compass, 3, 433-458.

Slonje, R., Smith, P. K., & Frisen, A. (2013). The nature of cyberbullying, and strategies for prevention. Computers in Human Behavior, 29, 26-32.

Stewart, D. M., & Fritsch, E. J. (2011). School and law enforcement efforts to combat cyberbul-lying. Preventing School Failure, 55, 79-87.

Tokunaga, R. S. (2010). Following you home from school: A critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior, 26, 277-287.

United States Department of Justice, Office of Justice Programs, Bureau of Justice Statistics. (n.d.). National Crime Victimization Survey: School Crime Supplement, 2009. Ann Arbor, MI: ICPSR.

Unnever, J. D., & Cornell, D. G. (2004). Middle school victims of bullying: Who reports being bullied? Aggressive Behavior, 30, 373-388.

Vandebosch, H., Beirens, L., D’Haese, W., Wegge, D., & Pabian, S. (2012). Police actions with regard to cyberbullying: The Belgian case. Psicothema, 24, 646-652.

Watkins, A. M., & Maume, M. O. (2011). School victims and crime reporting. Youth Violence and Juvenile Justice, 9, 333-351.

Wolak, J., Finkelhor, D., & Mitchell, K. J. (2012). How often are teens arrested for sexting?: Data from a national sample of police cases. Pediatrics, 129, 4-12.

Xie, M., & Lauritsen, J. L. (2012). Racial context and crime reporting: A test of Black’s strati-fication hypothesis. Journal of Quantitative Criminology, 28, 265-293.

Ybarra, M. L. (2004). Linkages between depressive symptomatology and Internet harassment among young regular Internet users. CyberPsychology & Behavior, 7, 247-257.

Author Biography

Lynn A. Addington is an associate professor in the Department of Justice, Law & Society at American University in Washington, DC. Her research focuses on violent victimization, the measurement of crime, and utilization of national crime statistics. Her publications have


http://www.ncsl.org/issues-research/justice/cyberbullying-and-the-states.aspx



appeared in a range of outlets including the American Behavioral Scientist, Homicide Studies, Journal of Quantitative Criminology, and Justice Quarterly. She is also the co-editor (with James P. Lynch) of Understanding Crime Statistics: Revisiting the Divergence of the NCVS and UCR (2007, Cambridge University Press).



journal of contemporary criminal justice reporting and

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