The present cross-sectional research examined the moderating role of resilience in the relationship between cyberbullying victimization and mental health outcomes, measured based on depression symptoms and life satisfaction. The sample consisted of 2,108 adolescents aged 12–17 who completed measures of cyberbullying victimization, resilience, depression, and life satisfaction. Structural equation models showed an appropriate fit of the moderation model of resilience in the relationship between cyberbullying victimization and mental health outcomes [χ2 (149) = 1,270.368; root-mean-squared error of approximation = 0.066; comparative fit index = 0.923; Tucker–Lewis index = 0.907]. Regression analyses revealed significant interaction effects, indicating that among adolescents with high levels of resilience, cyberbullying victimization was associated with fewer symptoms of depression and a smaller reduction in life satisfaction. This study highlights the importance of working on resilience in adolescents as a mechanism to deal with cyberbullying victimization.
Get full access to this article
View all access options for this article.
References
1.
HindujaS, PatchinJW. Cultivating youth resilience to prevent bullying and cyberbullying victimization. Child Abuse and Neglect, 2017; 73:51–62.
2.
SmithPK, MahdaviJ, CarvalhoM, et al.Cyberbullying: its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry, 2008; 49:376–385.
3.
KwanI, DicksonK, RichardsonM, et al.Cyberbullying and children and young people's mental health: a systematic map of systematic reviews. Cyberpsychology, Behavior, and Social Networking, 2020; 23:72–82.
4.
KowalskiRM, GiumettiGW, SchroederAN, et al.Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 2014; 140:1073–1137.
5.
HindujaS, Patchin JW (2015) Bullying beyond the schoolyard: preventing and responding to cyberbullying. Thousand Oaks, CA: Sage Publications.
6.
BrewerG, KerslakeJ. Cyberbullying, self-esteem, empathy and loneliness. Computers in Human Behavior, 2015; 48:255–260.
7.
CénatJM, BlaisM, LavoieF, et al.Cyberbullying victimization and substance use among Quebec high schools students: the mediating role of psychological distress. Computers in Human Behavior, 2018; 89:207–212.
8.
ChillemiK, AbbottJ-AM, AustinDW, et al.A pilot study of an online psychoeducational program on cyberbullying that aims to increase confidence and help-seeking behaviors among adolescents. Cyberpsychology, Behavior, and Social Networking, 2020; 23:253–256.
9.
Gámez-GuadixM, OrueI, SmithPK, et al.Longitudinal and reciprocal relations of cyberbullying with depression, substance use, and problematic internet use among adolescents. Journal of Adolescent Health, 2013; 53:446–452.
10.
DienerE, SuhEM, LucasRE, et al.Subjective well-being. Three decades of progress. Psychological Bulletin, 1999; 125:276–302.
11.
BilićV, Buljan FlanderG, RafajacB. Life satisfaction and school performance of children exposed to classic and cyber peer bullying. Collegium Antropologicum, 2014; 38:21–29.
12.
LayneCM, WarrenJS, WatsonPJ, et al. (2007) Risk, vulnerability, resistance, and resilience: toward an integrative conceptualization of posttraumatic adaptation. In FriedmanMJ, KeaneTM, ResickPA, eds. Handbook of PTSD: science and practice. New York: The Guilford Press, pp. 497–520.
13.
LutharSS, SawyerJA, BrownPJ. Conceptual issues in studies of resilience: past, present, and future research. Annals of the New York Academy of Sciences, 2006; 1094:105–115.
14.
SmithBW, TooleyEM, ChristopherPJ, et al.Resilience as the ability to bounce back from stress: a neglected personal resource?. The Journal of Positive Psychology, 2010; 5:166–176.
15.
WindleG. What is resilience? A review and concept analysis. Reviews in Clinical Gerontology, 2011; 21:152–169.
16.
ZhouP, ZhangC, LiuJ, et al.The Relationship Between Resilience and Internet Addiction: a Multiple Mediation Model Through Peer Relationship and Depression. Cyberpsychology, Behavior, and Social Networking, 2017; 20:634–639.
17.
LutharSS. (2006) Resilience in development: a synthesis of research across five decades. In CicchettiD, CohenDJ, eds. Developmental psychopathology: risk, disorder, and adaptation. Hoboken, NJ: John Wiley & Sons Inc., pp. 739–795.
18.
Earvolino-RamirezM. Resilience: a concept analysis. Nursing Forum, 2007; 42:73–82.
19.
PanJ-Y, ChanCLW. Resilience: a new research area in positive psychology. Psychologia, 2007; 50:164–176.
20.
WalshF. (2006) Strengthening family resilience. 2nd ed. New York: Guilford Press.
21.
MinJA, YoonS, LeeCU, et al.Psychological resilience contributes to low emotional distress in cancer patients. Supportive Care in Cancer, 2013; 2:2469–2476.
22.
FarberEW, SchwartzJA, SchaperPE, et al.Resilience factors associated with adaptation to HIV disease. Psychosomatics, 2000; 41:140–146.
23.
WindleG, BennettKM, NoyesJ. A methodological review of resilience measurement scales. Health and Quality of Life Outcomes, 2011; 9:8.
24.
RobertsonTW, YanZ, RapozaKA. Is resilience a protective factor of internet addiction?. Computers in Human Behavior, 2018; 78:255–260.
25.
HuT, ZhangD, WangJ. A meta-analysis of the trait resilience and mental health. Personality and Individual Differences, 2015; 76:18–27.
26.
GrychJ, HambyS, BanyardV. The resilience portfolio model: understanding healthy adaptation in victims of violence. Psychology of Violence, 2015; 5:343–354.
27.
MoschellaEA, TurnerS, BanyardVL. Posttraumatic growth as a mediator of self-blame and happiness in the context of interpersonal violence. Violence and Victims, 2018; 33:1088–1101.
28.
LutharSS, GoldsteinA. Children's exposure to community violence: implications for understanding risk and resilience. Journal of Clinical Child and Adolescent Psychology, 2004; 33:499–505.
29.
O'DonnellDA, Schwab–StoneME, MuyeedAZ. Multidimensional resilience in urban children exposed to community violence. Child Development, 2002; 73:1265–1282.
30.
HambyS, GrychJ, BanyardV. Resilience portfolios and poly-strengths: identifying protective factors associated with thriving after adversity. Psychology of Violence, 2018; 8:172–183.
31.
SapounaaM, WolkeD. Resilience to bullying victimisation: the role of the individual, family and peer characteristics. Child Abuse and Neglect, 2013; 73:997–1006.
32.
CalveteE, OrueI, EstévezA, et al.Cyberbullying in adolescents: modalities and aggressors' profile. Computers in Human Behavior, 2010; 26:1128–1135.
33.
Gámez-GuadixM, Villa-GeorgeF, CalveteE. Psychometric properties of the Cyberbullying Questionnaire (CBQ) among Mexican adolescents. Violence & Victims, 2014; 29:232–247.
34.
HjemdalO, FriborgO, StilesTC, et al.A new scale for adolescent resilience: grasping the central protective resources behind healthy development. Measurement and Evaluation in Counseling and Development, 2006; 39:84–96.
35.
Ruvalcaba-RomeroNA, Gallegos-GuajardoJ, Villegas-GuineaD. Validation of the resilience scale for adolescents (READ) in Mexico. Journal of Behavior, Health & Social Issues, 2014; 6:21–34.
36.
AndreuY, GaldónMJ, DuraE, et al.Psychometric properties of the Brief Symptoms Inventory-18 (BSI-18) in a Spanish sample of outpatients with psychiatric disorders. Psicothema, 2008; 20:844–850.
37.
DerogatisLR, MelisaratosN. The Brief Symptom Inventory: an introductory report. Psychological Medicine, 1983; 13:595–605.
38.
GaldónMJ, DuráE, AndreuY, et al.Psychometric properties of the Brief Symptom Inventory-18 in a Spanish breast cancer sample. Journal of Psychosomatic Research, 2008; 65:533–539.
39.
DienerE, EmmonsR, LarsenR, et al.The life satisfaction scale. Journal of Personality Assessment, 1985; 49:71–75.
40.
AtienzaF, PonsD, BalaguerI, et al.Psychometric properties of the satisfaction with life scale in adolescents. Psicothema, 2000; 12:314–319.
41.
PonsD, AtienzaFL, BalaguerI, et al.Satisfaction with life scale: analysis of factorial invariance for adolescents and elderly persons. Perceptual and Motor Skills, 2000; 91:62–68.
42.
RosseelY. Lavaan: an R package for structural equation modeling and more. Version 0.5–12 (BETA). Journal of Statistical Software, 2012; 48:1–36.
43.
R Core Team (2019) R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. https://www.R-project.org (accessed Apr.9, 2020).
44.
HuLT, BentlerPM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 1999; 6:1–55.
45.
ReiseSP, WidamanKF, PughRH. Confirmatory factor analysis and item response theory: two approaches for exploring measurement invariance. Psychological Bulletin, 1993; 114:552–566.
46.
AkaikeH. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 1974; 19:716–723.
47.
IBM Corp. (2019) IBM SPSS statistics and IBM SPSS modeler. https://www.ibm.com/analytics/spss-statisticys-software (accessed Apr.9, 2020).
48.
LiCH. Confirmatory factor analysis with ordinal data: comparing robust maximum likelihood and diagonally weighted least squares. Behavior Research Methods, 2016; 48:936–949.
49.
FoldnesN, HagtvetKA. The choice of product indicators in latent variable interaction models: post hoc analyses. Psychological Methods, 2014; 19:444–457.
50.
HayesAF. (2017) Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: The Guilford Press.
51.
CohenJ, CohenP. (1983) Applied multiple regression/correlation analysis for the behavioral sciences. 2nd ed. Mahwah, NJ: Erlbaum.
52.
ZychI, Ortega-RuizR, Marín-LópezI. Cyberbullying: a systematic review of research, its prevalence and assessment issues in Spanish studies. Psicología Educativa, 2016; 22:5–18.
53.
MedranoJL, Lopez RosalesF, Gámez-GuadixM. Assessing the links of sexting, cybervictimization, depression, and suicidal ideation among university students. Archives of Suicide Research, 2018; 22:153–164.
