Adaptation and Validation of the Adolescent Cyber Aggression Scale
DOI:
https://doi.org/10.15408/jp3i.v15i2.46772Keywords:
adaptation, adolescents, CFA, MIMIC, cyberbullying, quantitative studyAbstract
The present study aimed to cross-culturally adapt and evaluate the psychometric properties of the Adolescent Cyber-Aggression
Scale (CYB-AGS) for Indonesian adolescents. The adaptation process followed the International Test Commission Guidelines
for Translating and Adapting Tests. Psychometric evaluation included content validity using the Content Validity Index (CVI),
construct validity using confirmatory factor analysis (CFA) of a correlated two-factor model, composite reliability, and genderrelated differential item functioning (DIF) using a Multiple Indicators Multiple Causes (MIMIC) model. Participants were 467
Indonesian senior high school students (150 males, 32.1%; 317 females, 67.9%) aged 15-17 years (M = 16.02). Expert evaluation
demonstrated excellent content validity, with all items obtaining I-CVI values of 1.00 and S-CVI/Ave = 1.00. CFA supported
the correlated two-factor structure of direct and indirect cyber aggression (RMSEA = .038, 90% CI [.029, .046], CFI = .958, TLI
= .952, SRMR = .095). Composite reliability was satisfactory for direct cyber aggression (CR = .869) and indirect cyber
aggression (CR = .916). The MIMIC baseline model showed acceptable fit (RMSEA = .039, CFI = .935, TLI = .926, SRMR =
.096). Gender significantly predicted the latent levels of direct (standardized B = -.467, p < .001) and indirect (standardized B =
-.389, p < .001) cyber aggression, with females scoring lower than males in this sample. After controlling for latent direct cyber
aggression, a significant direct effect of gender on ACAS18 was detected (standardized B = -.142, p = .012), indicating uniform
DIF for this item. Overall, the Indonesian CYB-AGS provides satisfactory evidence of content validity, internal structure, and
reliability. However, gender comparisons, particularly for the direct cyber-aggression dimension, should account for the detected
DIF in ACAS18.
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