ABSTRACT
The current study sought to examine the associations between involvement in bullying (traditional and cyber), attitudes about aggression, and animal abuse. Four hundred and thirty-nine undergraduate students (267 females and 172 males) enrolled in Introductory Psychology completed surveys assessing bullying involvement, normative beliefs about aggression, and animal abuse tendencies. Results revealed that animal abusers reported significantly higher rates of bullying (traditional and cyber) and significantly more accepting views of aggression when compared to non-abusers. A logistic regression model indicated that bullying perpetration (traditional and cyber), normative beliefs about aggression, and gender were significant predictors of animal abuse. In addition, the findings suggest that normative beliefs about aggression may serve as an underlying mechanism linking traditional bullying, cyberbullying, and animal abuse. Implications for prevention and intervention programs for aggression toward humans and animals are discussed.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 Due to a technical issue, data regarding the race and ethnicity of the participants were lost. The sample was drawn from the student body of the Metropolitan State University of Denver. Across MSU Denver, approximately 65% of students identify themselves as White, 22% Hispanic, 6% Black, 4% Asian, 1% American Indian/Alaska Native, and 1% Pacific Islander. These proportions are consistent with other studies conducted with this population (Henry & Sanders, Citation2007; Sanders & Henry, Citation2015; Sanders et al., Citation2013). As such, we believe that the race and ethnicity characteristics of the current sample are consistent with these proportions.
2 In order to assess potential multicollinearity among predictor variables, a linear regression model was run using participation in animal abuse as the dependent variable and the same hierarchical entry of predictor variables. In SPSS, use of linear regression allows for the generation of variance inflation factors (VIF) values for the predictor values. VIF values ranged from 1.00 to 1.49 for the predictors variables, indicating the multicollinearity was not a significant threat to the regression analysis.