The Role of Moral Disengagement in Underage Drinking and Heavy Episodic Drinking

Abstract

Objectives: The current study had two aims. First, to develop a moral disengagement scale contextualized to underage drinking. Second, to investigate Bandura's (1986) self-regulatory model within the context of underage drinking. Method: Two different samples of students participated in the study. The first sample included 619 (362 females) adolescents (M_age = 15.3 years, SD = 1.09 years) and the second sample 636 (386 females) adolescents (M_age = 15.3 years, SD = 1.03 years). Students in the first sample completed the Underage Drinking Disengagement Scale (UDDS), and measures of engagement in underage drinking and heavy episodic drinking. Students in the second sample completed these measures as well as scales of general moral disengagement, personal standards, and anticipatory guilt associated with underage drinking. Results: For the UDDS, exploratory and confirmatory factor analyses verified a single factor structure. The UDDS was more strongly associated with engagement in underage drinking and heavy episodic drinking than a general measure of moral disengagement. A moderated mediation analysis revealed that adolescents who negatively evaluated underage drinking reported more anticipatory guilt, and more anticipatory guilt was associated with less engagement in underage drinking and less heavy episodic drinking. This relationship was weaker at high compared to low levels of underage drinking disengagement. Conclusions/Importance: Understanding how adolescents self-regulate their drinking, and ways that such self-regulation may be deactivated or disengaged, may help identify those adolescents at increased risk of drinking underage and of engaging in heavy episodic drinking.

Keywords:

• underage drinking
• moral disengagement
• guilt
• alcohol
• adolescent

Declaration of Interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the article.

THE AUTHORS

Catherine Angela Quinn recently completed a combined Ph.D. and Masters in Clinical Psychology through Macquarie University, Sydney. She is currently a Postdoctoral Research Fellow at the Centre for Youth Substance Abuse Research, Queensland University of Technology. Her research interests include adolescent substance use, school bullying, youth wellbeing, and mental health.

Kay Bussey Ph.D. is an Associate Professor of Psychology at Macquarie University, Sydney, Australia. She has been the recipient of a Fulbright Award and is on the editorial board of the British Journal of Developmental Psychology while also serving as a consultant for numerous psychology journals and scientific organizations. Her publications span gender development, moral development, children's participation in the legal system, child protection, school bullying, and children and adolescents’ antisocial behavior.

GLOSSARY

• Moral disengagement: A social cognitive process that enables justification of transgressive behavior thereby negating guilt or remorse for the transgressive behavior.

• Self-regulatory model: It is the social cognitive theory model of self-directed behavior that is achieved through self-monitoring, judgment and self-reaction.

• Social cognitive theory: An agentic theory of human behavior proposed by Albert Bandura (1986). It is an integrative theory which considers environmental, cognitive, self-regulatory and self-reflective influences on human adaption and change.

• Transgressive behavior: A behavior which violates law, duty, or moral code.

• Underage drinking disengagement. A moral disengagement contextualized to underage drinking (i.e., a process whereby individuals justify or excuse underage drinking without being constrained by self-sanctions).

Notes

¹ Bootstrapping is preferred over the product of coefficients (ab or c–c’) Sobel test because it is not reliant on sample size, it maintains reasonable control of the Type 1 error rate and does not rely on a normal distribution of ab, which is often positively skewed (Preacher & Hayes, 2004; MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). Boostrapping randomly generates a large number of samples (e.g., 5000) from the existing data, and computes an indirect effect (ab) in each sample (Preacher & Hayes, 2004). This random resampling is then used to generate confidence intervals for the indirect effect. The indirect effect is deemed significant when the bootstrapping confidence interval does not contain zero (Hayes, 2009).