Visiting Porn Sites Online: Contrasting the Predictive Value of Studying Personal Characteristics, In-person Activities, and the Social Learning Approach

• In Event: Cyberbullying, Cyber Deviance, and Digital Sensoring from Smartphones: Empirical Tests of Social Learning and Self-Control Theories

Abstract

Nearly half (48.8%) of our sample of 812 college students reported visiting a porn site in the past six months. We predicted their cyber sexual activity using Akers’ social learning theory (SLT). Three binary logistic regression analyses were conducted. When accounting for controls, four out of ten variables in Model A (X2= 189.582; p>0.001) were significant (gender, ethnicity, year in school, and the sexuality scale). When accounting for controls and in-person sexual activities, seven out of fifteen variables in Model B (X2= 295.802; p>0.001) were significant (gender, ethnicity, year in school, the sexuality scale, race, number of sex partners, and frequency of masturbation). When accounting for controls, in-person sexual activities, and SLT variables, three seven out of nineteen variables in Model C (X2= 439.888; p>0.001) were significant (gender, race, sexuality scale, frequency of masturbation, differential peer association, differential reinforcement, and definitions favorable). Akers’ SLT variables fully mediated ethnicity, year in school, and number of sex partners, but it only partially mediated gender, race, and sexuality scale. In each model, gender was the strongest variable. These finding inform the research on porn use, as well as on the applicability of social learning theory.

Authors

• Danielle Tolson Cooper, University of New Haven

• Jennifer L. Klein, University of Texas at Tyler