Latent Class Analysis of Internet Users: Do Low Self-Control and Deviant Online Peers Predict Class Membership?

Limited research has identified distinct Internet user subgroups based on self-reported cybercrime offending. Additionally, few explore the influence of low self-control and deviant online peer associations on the likelihood of being assigned into different cybercrime offending classes. The current study conducted a latent class analysis to identify unobserved subgroups based on the self-reported cybercrime behaviors of 1,137 Internet users, identifying three latent classes: (1) No Cybercrime, (2) High Cybercrime, and (3) Illegal Downloader. Analyses revealed that low self-control and deviant online peer associations significantly increased the likelihood of membership in the “High Cybercrime” class relative to the other two classes. These findings emphasize the role of individual traits and social influences in understanding Internet users’ behaviors and identifying high-risk individuals.