Adaptation and Validation of the Cyberbullying Scale in Indonesian Version: Exploratory and Confirmatory Factor Analysis Approach
DOI:
https://doi.org/10.61194/psychology.v4i2.1048Keywords:
cyberbullying, instrument adaptation, psychometric validation, exploratory factor analysis, Indonesian adolescentsAbstract
Cyberbullying has become a major psychosocial issue among adolescents alongside the rapid growth of digital media use. This study aimed to adapt and evaluate the validity and reliability of the Indonesian version of the Cyberbullying Scale (CBS) developed by Stewart et al. (2014). A descriptive quantitative design was used involving 392 adolescents aged 12–18 years from 10 provinces in Indonesia, recruited through purposive sampling via an online survey. The adaptation followed WHO cross-cultural adaptation guidelines, including forward translation, backward translation, expert review, and pilot testing. Data were analyzed using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) with JASP software.
EFA results showed a unidimensional structure with an eigenvalue of 9.628 explaining 68.8% of the total variance, while all items demonstrated strong factor loadings (0.689–0.889) without item removal. CFA confirmed significant item loadings on the cyberbullying factor (0.643–0.976, p < 0.001). Reliability analysis indicated excellent internal consistency (α = 0.968). However, CFA global fit indices were below recommended thresholds (CFI = 0.839, TLI = 0.809, RMSEA = 0.182), suggesting that the one-factor model may not fully represent cyberbullying experiences in the Indonesian context. Overall, the Indonesian CBS showed strong reliability and item-level validity, although further refinement and cross-validation are needed before broader application among Indonesian adolescentsCyberbullying has become a major psychosocial issue among adolescents alongside the rapid growth of digital media use. This study aimed to adapt and evaluate the validity and reliability of the Indonesian version of the Cyberbullying Scale (CBS) developed by Stewart et al. (2014). A descriptive quantitative design was used involving 392 adolescents aged 12–18 years from 10 provinces in Indonesia, recruited through purposive sampling via an online survey. The adaptation followed WHO cross-cultural adaptation guidelines, including forward translation, backward translation, expert review, and pilot testing. Data were analyzed using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) with JASP software.
EFA results showed a unidimensional structure with an eigenvalue of 9.628 explaining 68.8% of the total variance, while all items demonstrated strong factor loadings (0.689–0.889) without item removal. CFA confirmed significant item loadings on the cyberbullying factor (0.643–0.976, p < 0.001). Reliability analysis indicated excellent internal consistency (α = 0.968). However, CFA global fit indices were below recommended thresholds (CFI = 0.839, TLI = 0.809, RMSEA = 0.182), suggesting that the one-factor model may not fully represent cyberbullying experiences in the Indonesian context. Overall, the Indonesian CBS showed strong reliability and item-level validity, although further refinement and cross-validation are needed before broader application among Indonesian adolescents.
References
Alfiasari, Z. M. (2018). Cyberbullying Behavior in Adolescents and Its Relation to Self-Control and Parental Communication. Journal of Family Science & Counseling, 11(2), 145–156. https://doi.org/10.24156/jikk DOI: https://doi.org/10.24156/jikk
Ambarita, A. P., & Zarzani, T. R. (2024). Social Media-Induced Cyber Bullying Behaviour Affecting Teenagers. International Journal of Sociology and Law, 2(1), 140–149. https://doi.org/10.62951/ijsl.v1i2.74 DOI: https://doi.org/10.62951/ijsl.v1i2.74
APJII. (2023). Indonesian Internet User Survey. Indonesian Internet Service Providers Association.
Barlett, C., & Coyne, S. M. (2014). A Meta-Analysis of Sex Differences in Cyber-Bullying Behavior: The Moderating Role of Age. Aggressive Behavior, 40, 474–488. https://doi.org/10.1002/ab.21555 DOI: https://doi.org/10.1002/ab.21555
Bronfenbrenner, U. (1979). The Ecology of Human Development. Harvard University Press. https://doi.org/10.4159/9780674028845 DOI: https://doi.org/10.4159/9780674028845
Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). Guilford Press.
Daulay, A. R. (2025). The Influence of Pancasila Values on Cyberbullying on Social Media: Case of Verbal Violence by Luluk Sofiatul Jannah. NuCSJo: Nusantara Community Service Journal, 2(2), 147–152. https://doi.org/10.70437/nucsjo.v2i2.288 DOI: https://doi.org/10.70437/nucsjo.v2i2.288
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson.
Hofstede, G. (2011). Dimensionalizing Cultures: The Hofstede Model in Context. Online Readings in Psychology and Culture, 2(1), 1–26. https://doi.org/10.9707/2307-0919.1014 DOI: https://doi.org/10.9707/2307-0919.1014
Hong, J. S. (2018). Experiences in Bullying and/or Peer Victimization of Vulnerable, Marginalized, and Oppressed Children and Adolescents: An Introduction to the Special Issue. American Journal of Orthopsychiatry, 88(4), 399–401. https://doi.org/10.1037/ort0000330 DOI: https://doi.org/10.1037/ort0000330
Hu, L. T., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118 DOI: https://doi.org/10.1080/10705519909540118
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power Analysis and Determination of Sample Size for Covariance Structure Modeling. Psychological Methods, 1(2), 130–149. https://doi.org/10.1037/1082-989X.1.2.130 DOI: https://doi.org/10.1037/1082-989X.1.2.130
Marsinun, R., & Riswanto, D. (2020). Teenage Cyberbullying Behavior on Social Media. Analitika: Journal of Master of Psychology UMA, 12(2). https://doi.org/10.31289/analitika.v12i2.3704 DOI: https://doi.org/10.31289/analitika.v12i2.3704
Mukramin, S., Ismail, L., & Nursida, A. (2024). Strategies to Prevent Cyberbullying. Journal of Social Research, 12(2), 100–109.
Ni’mah, S. A. (2023). The Effect of Cyberbullying on Adolescent Mental Health. Proceedings of the 3rd National Seminar on Language, Literature and Culture (Sepeera), 329–338. https://e-proceeding.unmas.ac.id/
Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). McGraw-Hill.
Nurmasitah, S. (2025). Exploring Adolescent Cyberbullying Behavior in Whatsapp Media: The Role of Communication Patterns in the Family. JKKP (Journal of Family Welfare and Education), 12(1), 15–26. https://doi.org/10.21009/JKKP.121.02 DOI: https://doi.org/10.21009/JKKP.121.02
OECD. (2019). PISA 2018 Results (Volume I): What Students Know and Can Do. OECD Publishing. https://doi.org/10.1787/5f07c754-en DOI: https://doi.org/10.1787/5f07c754-en
Ramadhanti. (2022). Teachers’ Strategies in Overcoming Student Bullying Behavior in Elementary Schools. Basic Journal, 6(3), 4566–4573. https://doi.org/10.31004/basicedu.v6i3.2892 DOI: https://doi.org/10.31004/basicedu.v6i3.2892
Rusuli, I. (2022). Adolescent Psychosocial: A Synthesis of Erick Erikson’s Theory with Islamic Concepts. Journal of As-Salam, 6(1), 75–89. https://doi.org/10.37249/assalam.v6i1.384 DOI: https://doi.org/10.37249/assalam.v6i1.384
Rusyidi, B. (2020). Understanding Cyberbullying Among Adolescents. Journal of Conflict Resolution Collaboration, 2(2), 100–110. https://doi.org/10.24198/jkrk.v2i2.29118 DOI: https://doi.org/10.24198/jkrk.v2i2.29118
Sad-Houari, N., & Sad-Houari, K. (2026). Illuminating the Shadow: Building an Advanced Semantic Cyberbullying Detection System. Journal of Intelligent and Fuzzy Systems, 50(4), 1015–1032. https://doi.org/10.1177/18758967251388262 DOI: https://doi.org/10.1177/18758967251388262
Salmah. (2024). Development of a Measurement Tool for Bullying Behavior in the Cyber World in Students Using Social Media. Journal of Psychology and Counseling Guidance, 8(1). https://doi.org/10.6734/LIBEROSIS.V2I2.3027
Selwyn, N., & Aagaard, J. (2021). Banning mobile phones from classrooms—An opportunity to advance understandings of technology addiction, distraction and cyberbullying. British Journal of Educational Technology, 52(1), 8–19. https://doi.org/10.1111/bjet.12943 DOI: https://doi.org/10.1111/bjet.12943
Setiawan, R. F. (2025). The Effect of Cyberbullying on Student Mental Health in the Special Region of Yogyakarta. Journal of Psychology and Health, 1(4), 161–166.
Shahzad, K., Khan, S. A., Javeed, A. M. D., & Iqbal, A. (2026). Factors influencing cyberbullying among citizens: a systematic review of articles published in refereed journals from 2010 to 2023. Global Knowledge, Memory and Communication, 75(3–4), 1170–1204. https://doi.org/10.1108/GKMC-11-2023-0422 DOI: https://doi.org/10.1108/GKMC-11-2023-0422
Siroj, M., & Zulfa, A. (2024). Dampak Cyberbullying Pada Remaja di Media Sosial: The Impact of Cyberbullying on Teenagers on Social Media. JICN: Journal of Intellectual and Scholars of the Archipelago, 1(2).
Stewart, R. W. (2014). The Development and Psychometric Investigation of the Cyberbullying Scale. Journal of Interpersonal Violence, 29(12), 2218–2238. https://doi.org/10.1177/0886260513517552 DOI: https://doi.org/10.1177/0886260513517552
Tazkiyah, I., Fadillah, A. R., & Kusuma, F. W. (2021). The Role of Anonymity in Cyberbullying on Social Media. National Seminar on Technology and Information Systems 2021.
Widayanti, T. (2022). Social Media as a Platform for Cyberbullying in the Distance Learning Era. Health Sciences and Pharmacy Journal, 6(2), 42–48. https://doi.org/10.32504/hspj.v6i2.719 DOI: https://doi.org/10.32504/hspj.v6i2.719
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