Practice Profile: School-Based Cyberbullying Prevention Programs

Evidence Rating for Outcomes

Juvenile Problem & At-Risk Behaviors | Cyberbullying perpetration

Victimization | Cyberbullying victimization

Juvenile Problem & At-Risk Behaviors | Perpetration

Victimization | Bullying victimization

Victimization | Cyber-bystander behavior

Date: This profile was posted on February 07, 2023

Practice Goals

School-based Cyberbullying Prevention Programs aim to decrease cyberbullying perpetration and victimization and promote cyber-bystander behaviors among students in kindergarten through grade 12. Cyberbullying perpetration is the act of inflicting or receiving negative, damaging, or abusive language or harassment through different technology platforms (Pearce et al. 2011). Technology platforms may include but are not limited to text messages, email, instant messages, chatrooms, public or private social media, or videos through online platforms (Whittaker and Kowalski 2015). Examples of cyberbullying are 1) being intentionally excluded from information sharing, 2) online interactions or in-person interactions being shared online, 3) having passwords or personal information stolen, or 4) receiving harassment or threatening messages in gaming rooms (Bauman 2015). Individuals who witness bullying are referred to as bystanders or defenders. In instances of cyberbullying, individuals may be referred to as cyber-bystanders (Torgal et al. 2021).

Practice Components

Cyberbullying prevention programs in schools may employ some of the following programming components. These components are not mutually exclusive, and programs may use multiple components. Examples of common components appear below.

A self-efficacy or skill-building component involves teaching or training students to develop a competency that they can then apply by themselves in the moment. Examples are empathy perspective-taking activities, effective communication, modeling, feedback, role playing, and goal setting (Torgal et al. 2021; Polanin et al. 2022).
An empathy component involves utilizing empathy activation or perspective-taking activities. Examples are the empathy activation method of showing participants pictures of sad facial expressions or a video with a cyberbullying incident from the victim’s point of view (Torgal et al. 2021),
Curricula and prepared materials are uniform, prepared components that guide the delivery of the program by teachers, counselors, school staff, and students’ family members. Examples are class curricula, handouts, homework, and worksheets (Polanin et al. 2022).
A psychoeducation component focuses on developing awareness, understanding, and coping strategies to manage a condition and improve outcomes (Lukens and McFarlane 2004). Examples are raising awareness of cyberbullying, knowledge of cyber-safety, and general psychoeducation (Polanin et al. 2022).
Digital media materials are multimedia components used in programs designed to enhance student engagement and participation. Examples are informational websites, interactive games, and video or online courses (Torgal et al. 2021; Polanin et al. 2022).
A training component provides program participants with the knowledge, skills, and practice to accomplish a specific goal (Nation et al. 2003). Examples are bystander training that teaches participants how to recognize and avoid risky cyberbullying situations, and small group discussions on actions to take when witnessing an incident of cyberbullying (Torgal et al. 2021; Polanin et al. 2022).
School climate or school policy are components that refer to a summation of students’ perceptions of the educational structures, values, practices, and relationships that create their school experiences (Thapa et al. 2013). Examples are whole school participation, school reporting policy, school disciplinary policy, risk assessment, screening, and classroom management (Polanin et al. 2022).
Group or individual targeted responses are components that refer to delivering programs to a specific group of students rather than using a universal prevention approach. Group or individual sessions for those at risk for cyberbullying are examples (Polanin et al. 2022).

Juvenile Problem & At-Risk Behaviors | Cyberbullying perpetration
Polanin and colleagues (2022) analyzed 96 effect sizes from 44 studies evaluating school-based programs to reduce cyberbullying and found a statistically significant reduction in cyberbullying perpetration (g = -0.18) for student participants, compared with students who did not participate in a program. This finding indicates that cyberbullying prevention had a 76 percent probability of reducing cyberbullying perpetration.

Victimization | Cyberbullying victimization
Polanin and colleagues (2022) analyzed 75 effect sizes from 39 studies evaluating school-based programs to reduce cyberbullying and found a statistically significant reduction in cyberbullying victimization (g = -0.13) for student participants, compared with students who did not participate in a program. This finding indicates that cyberbullying prevention programs had a 73 percent probability of reducing cyberbullying victimization.

Juvenile Problem & At-Risk Behaviors | Perpetration
Polanin and colleagues (2022) analyzed 67 effect sizes from 22 studies evaluating school-based programs to reduce cyberbullying and found a statistically significant reduction in traditional bullying perpetration (g = -0.18) for student participants, compared with students who did not participate in a program. This finding indicates that cyberbullying prevention programs had a 78 percent probability of reducing traditional bullying perpetration.

Victimization | Bullying victimization
Polanin and colleagues (2022) analyzed 82 effect sizes from 24 studies evaluating school-based programs to reduce cyberbullying and found a statistically significant reduction in traditional bullying victimization (g = -0.16) for student participants, compared with students who did not participate in a program. This finding indicates that cyberbullying prevention programs had a 73 percent probability of reducing traditional bullying victimization.

Victimization | Cyber-bystander behavior
Torgal and colleagues (2021) analyzed 35 effect sizes from nine randomized controlled trial studies evaluating school-based cyberbullying prevention programs on promoting cyber-bystander behaviors (e.g., taking active steps to intervene by assisting the victim). They found that there were no statistically significant effects on cyber-bystander behavior.

Meta-Analysis Snapshot
	Literature Coverage Dates	Number of Studies	Number of Study Participants
Meta Analysis 1	2006-2019	50	45371
Meta Analysis 2	2012-2019	9	2434

Meta Analysis 1

Polanin and colleagues (2022) conducted a meta-analysis of school-based programs to reduce cyberbullying perpetration and victimization. Studies were included in the analysis if they 1) focused on students in K–12th grade, 2) tested the effects of a program on cyberbullying, 3) measured cyberbullying perpetration or victimization as well as in-person bullying, and 4) were published in English, Spanish, or Turkish. Since the terms cyberbullying, internet bullying, and computer bullying first appeared in the literature in the mid-1990s, the meta-analysis authors included any study published on or after 1995. All types of programs were included in the literature search, ranging from programs to reduce bullying perpetration and victimization to any school-based general violence prevention program. The authors included both studies that randomly assigned participants to conditions and studies that did not randomly assign participants. Electronic searches of the literature included some of the following databases to include both published and unpublished studies: Academic Search Complete, Education Full Text, ERIC, National Criminal Justice Reference Services Abstracts, ProQuest, PsycINFO, and PubMed.

A total of 320 effect sizes from 50 studies were identified. The studies included a total of 45,371 participants. About half of the study samples consisted of male students (51 percent). Studies included students who identified as white (65 percent) and as nonwhite (35 percent), and the mean student age was 13 years. The average length of the program was 22 weeks. Programs in the sample either directly targeted cyberbullying (76 percent) or targeted bullying in general (24 percent). Studies in the analysis were conducted both inside (n = 18) and outside the United States (n = 32) . Studies used randomized controlled trial study designs (n = 27) or quasi-experimental study designs (n = 23).

A multilevel, random effects model with robust variance estimates was used to produce a weighted average of the effect sizes. The overall average effects were then translated into the probability of positive impact metric, which indicated the probability that a randomly selected and implemented program would reduce cyberbullying perpetration or victimization. Separate analyses were conducted for four outcome categories: 1) cyberbullying perpetration (44 studies, 96 effect sizes), 2) cyberbullying victimization (39 studies, 75 effect sizes), 3) traditional bullying perpetration (22 studies, 67 effect sizes), and 4) traditional bullying victimization (24 studies, 82 effect sizes).

Meta Analysis 2

Torgal and colleagues (2021) reviewed the findings from the literature on the impact of school-based cyberbullying prevention programs on cyber-bystander outcomes with students in K–12th grade. Studies were included if they 1) were published on or after 1995 until 2019, 2) had an abstract written in English, 3) evaluated a school-based program designed for promoting positive cyber-bystander behavior and mitigating cyberbullying perpetration and victimization (or other forms of violence), and 4) measured an outcome of cyber-bystander behavior in an online context. Studies were excluded if they evaluated programs implemented outside of the school setting or in community settings. This meta-analysis was part of a larger project and utilized the same literature search procedures and methods as Meta-Analysis 1 (Polanin et al. 2022). Based on their supplemental literature search from November 2019, the meta-analysis authors included more specific search terms for the 2021 meta-analysis. For the supplemental search, the authors used Academic Search Complete, ERIC, PsycINFO, Psychology and Behavioral Sciences, and ProQuest databases in addition to manual searches of topic-specific journals, study reference lists, and author queries.

The results included 35 effects sizes across 9 studies. All the studies included in the analysis utilized randomized controlled trial designs. A total of 2,434 students participated in the studies. Across the study samples, 50.5 percent of the participants were male. Student ages ranged from 10–18 years old. Five programs were implemented in middle schools and four programs were implemented with a mixed middle and high school sample. Most of the programs were implemented in a small group format (n = 7), such as a classroom-based computer lab or a single player game, and two were implemented as a classroom-wide curriculum. Seven of the studies were delivered by teachers, one used trained support staff, and one was delivered by the program evaluators. Most of the studies were conducted in Poland (n = 6) and the three remaining studies were conducted in Belgium, Germany, and the United States. All studies utilized randomized controlled trial designs.

The cyber-bystander outcome was conceptualized as bystander behavior in an online context. Examples of positive cyber-bystander outcomes were active cyber-bystander behavior, attitudes on comforting the victim, and intervening bystander behavior. Examples of negative cyber-bystander outcomes were passive cyber-bystander behavior, attitudes on doing nothing, and negative cyber-bystander behavior. This meta-analysis utilized a statistical method that provided an estimation of an overall treatment effect by combining statistical information from numerous studies measuring comparable constructs. The effect size measure used was Hedges’ g. The synthesis of the studies was conducted utilizing random-effects models.

These sources were used in the development of the practice profile:

Bauman, Sheri. 2015. “Types of Cyberbullying” Cyberbullying: What Counselors Need to Know. American Counseling Association, 53–58.

Hawkins, Lynn D., Debra J. Pepler, and Wendy M. Craig. 2001. “Naturalistic Observations of Peer Interventions in Bullying.” Social Development 10(4):512–27.

Lukens, Ellen P., and William R. McFarlane. 2004. “Psychoeducation as Evidence-Based Practice: Considerations for Practice, Research, and Policy.” Brief Treatment and Crisis Intervention 4(3):205–25.

Nation, Maury, Cindy Crusto, Abraham Wandersman, Karol L. Kumpfer, Diana Seybolt, Erin Morrissey–Kane, and Katrina Davino. 2003. “What Works in Prevention: Principles of Effective Prevention Programs.” American Psychologist 58(6–7):449–56.

Nickerson, A.B., and D. Mele–Taylor. 2014. “Empathetic Responsiveness, Group Norms, and Prosocial Affiliations in Bullying Roles. School Psychology Quarterly 29(1):99–109.

Pearce, Natasha, Donna Cross, Helen Monks, Stacey Waters, and Sarah Falconer. 2011. “Current Evidence of Best Practice in Whole-School Bullying Intervention and Its Potential to Inform Cyberbullying Interventions.” Journal of Psychologists and Counsellors in Schools 21:1–21.

Salmivalli, Christina. 2010. “Bullying and The Peer Group: A Review.” Aggression and Violent Behavior 15(2):112–20.

Thapa, Amrit, Jonathan Cohen, Shawn Guffey, and Ann Higgins–D’Alessandro. 2013. “A Review of School Climate Research.” Review of Educational Research 83(3):357–85.

Whittaker, Elizabeth, and Robin M. Kowalski. 2015. “Cyberbullying via Social Media.” Journal of School Violence 14:11–29.

Date Created: July 17, 2024

Rating Process

Learn how this and other practices are rated

Practice Snapshot

Age: 10 - 18

Gender: Male, Female

Race/Ethnicity: White, Other

Setting (Delivery): School

Practice Type: Bullying Prevention/Intervention, Classroom Curricula, Conflict Resolution/Interpersonal Skills, School/Classroom Environment

Unit of Analysis: Persons

Researcher:
Joshua R. Polanin
Principal Researcher
American Institutes for Research

1325 G Street, NW, Suite 900
Washington, DC 20005
United States

Phone: 202.405.5509
Website
Email

Practice Profile: School-Based Cyberbullying Prevention Programs

Practice Summary

Practice Description

Practice Goals

Practice Components

Meta-Analysis Outcomes

Meta-Analysis Methodology

Cost

Other Information

Evidence-Base (Meta-Analyses Reviewed)

Additional References

Related Programs