Effectiveness and sustainability of the ViSC Social Competence Program to prevent cyberbullying and cyber-victimization: Class and individual level moderators.

We investigated whether the general anti-bullying program ViSC sustainably prevents cyberbullying and cyber-victimization. A longitudinal randomized control group design was used to examine (i) program effectiveness immediately after a 1 year implementation phase and (ii) sustainable program effects 6 months later taking several moderators on the class level (class climate and ethnic diversity) and on the individual level (gender, age, internet usage, traditional bullying/victimization) into account. Effectiveness (e.g., the change between waves 2 and 1) was examined in 2,042 students (47.6% girls), aged 11.7 years (SD = 0.88) enrolled in 18 schools and 103 classes. Sustainability (e.g., the change between waves 3 and 2) was examined in a sub-sample of 6 schools and 35 classes comprising 659 students. The self-assessment multiple-item scales showed longitudinal and multiple group invariance. Factor scores were extracted to compute difference scores for effectiveness (Posttest minus Pretest) and sustainability (Follow-up test minus Posttest) for cyberbullying and cyber-victimization. Multilevel Modeling was applied to examine (i) the effectiveness and (ii) the sustainability of the ViSC intervention controlling for several individual and class level variables. Controlling for covariates, it was demonstrated that the ViSC program is effective in preventing cyberbullying and cyber-victimization and that the effects are sustainable after 6 months. The consequences for cyberbullying prevention are discussed.

[1]  C. Spiel,et al.  Bullying and Victimization in Ethnically Diverse Schools: Risk and Protective Factors on the Individual and Class Level , 2011 .

[2]  John W. Graham,et al.  Missing Data: Analysis and Design , 2012 .

[3]  T. Little,et al.  Effects of the KiVa Antibullying Program on Cyberbullying and Cybervictimization Frequency Among Finnish Youth , 2013, Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53.

[4]  C. Spiel,et al.  Traditional Bullying and Cyberbullying Identification of Risk Groups for Adjustment Problems , 2009 .

[5]  J. Garbarino.  The ecology of human development: Experiments by nature and design: by Urie Bronfenbrenner Cambridge, Mass.: Harvard University Press, 1979, 330 + p. , 1980 .

[6]  Herbert Scheithauer,et al.  Prävention von Cybermobbing und Reduzierung aggressiven Verhaltens Jugendlicher durch das Programm Medienhelden: Ergebnisse einer Evaluationsstudie , 2014 .

[7]  D. Farrington,et al.  Effectiveness of school-based programs to reduce bullying: a systematic and meta-analytic review , 2011 .

[8]  Roger E. Millsap,et al.  Assessing Factorial Invariance in Ordered-Categorical Measures , 2004 .

[9]  Robert S. Tokunaga,et al.  Following you home from school: A critical review and synthesis of research on cyberbullying victimization , 2010, Comput. Hum. Behav..

[10]  Tom A. B. Snijders,et al.  Multilevel Analysis , 2011, International Encyclopedia of Statistical Science.

[11]  D. Badaly,et al.  Longitudinal Associations of Electronic Aggression and Victimization with Social Standing During Adolescence , 2012, Journal of Youth and Adolescence.

[12]  Rosario Del Rey,et al.  Knowing, Building and Living Together on Internet and Social Networks: The ConRed Cyberbullying Prevention Program , 2012 .

[13]  Nicole A. Lazar,et al.  Statistical Analysis With Missing Data , 2003, Technometrics.

[14]  Christiane Spiel,et al.  Cyber-victimization and popularity in early adolescence: Stability and predictive associations , 2012 .

[15]  Alex R. Piquero,et al.  Self-Control Interventions for Children Under Age 10 for Improving Self-Control and Delinquency and Problem Behaviors: A Systematic Review , 2010 .

[16]  A. Demetriou,et al.  A longitudinal study of cyberbullying: Examining riskand protective factors , 2012 .

[17]  C. Spiel,et al.  Evidence-based practice and policy: When researchers, policy makers, and practitioners learn how to work together , 2012 .

[18]  Ersilia Menesini,et al.  Empowering Students Against Bullying and Cyberbullying: Evaluation of an Italian Peer-led Model , 2012 .

[19]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[20]  B. Muthén,et al.  A comparison of some methodologies for the factor analysis of non‐normal Likert variables , 1985 .

[21]  David P. Farrington,et al.  School-Based Programs to Reduce Bullying and Victimization: A Systematic Review , 2009 .

[22]  Dagmar Strohmeier,et al.  Nutzung gewalthaltiger Bildschirmspiele als längsschnittlicher Risikofaktor für Cyberbullying in der frühen Adoleszenz , 2014 .

[23]  Roderick J. A. Little,et al.  Statistical Analysis with Missing Data: Little/Statistical Analysis with Missing Data , 2002 .

[24]  Roel Bosker,et al.  Multilevel analysis : an introduction to basic and advanced multilevel modeling , 1999 .

[25]  Charles A. Maher,et al.  Bullying, Victimization, and Peer Harassment: A Handbook of Prevention and Intervention , 2006 .

[26]  Stef van Buuren,et al.  Flexible Imputation of Missing Data , 2012 .

[27]  Adrienne Nishina,et al.  Ethnic Diversity and Perceptions of Safety in Urban Middle Schools , 2006, Psychological science.

[28]  Alexander Robitzsch,et al.  Some Additional Multiple Imputation Functions, Especially for'mice' , 2015 .

[29]  Robert F. Boruch,et al.  Standards of Evidence: Criteria for Efficacy, Effectiveness and Dissemination , 2005, Prevention Science.

[30]  C. Spiel,et al.  National strategy for violence prevention in the Austrian public school system: Development and implementation , 2011 .

[31]  James A. Bovaird,et al.  Measurement models for ordered-categorical indicators. , 2012 .

[32]  N. Crick,et al.  Relational aggression, gender, and social-psychological adjustment. , 1995, Child development.

[33]  D. Rubin,et al.  Multiple Imputation for Nonresponse in Surveys , 1989 .

[34]  Stef van Buuren,et al.  MICE: Multivariate Imputation by Chained Equations in R , 2011 .

[35]  J. Schafer,et al.  A comparison of inclusive and restrictive strategies in modern missing data procedures. , 2001, Psychological methods.

[36]  D. Farrington,et al.  Successful Bullying Prevention Programs: Influence of Research Design, Implementation Features, and Program Components , 2012 .

[37]  D. Rubin,et al.  Fully conditional specification in multivariate imputation , 2006 .

[38]  R. Catalano,et al.  Longitudinal predictors of cyber and traditional bullying perpetration in Australian secondary school students. , 2012, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[39]  Robin M. Kowalski,et al.  Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. , 2014, Psychological bulletin.

[40]  S. Perren,et al.  Longitudinal Risk Factors for Cyberbullying in Adolescence , 2013 .

[41]  Peter K. Smith,et al.  Cyberbullying: its nature and impact in secondary school pupils. , 2008, Journal of child psychology and psychiatry, and allied disciplines.

[42]  Dorothy L. Espelage,et al.  Introduction: A Social-Ecological Framework of Bullying Among Youth. , 2004 .

[43]  Christiane Spiel,et al.  ViSC Social Competence Program. , 2012, New directions for youth development.

[44]  Christiane Spiel,et al.  Prevention of Cyberbullying and Cyber Victimization: Evaluation of the ViSC Social Competence Program , 2015 .

[45]  Sunil J Rao,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2003 .