Moving Beyond Drinking to Have a Good Time: a Person-Centered Approach to Identifying Reason Typologies in Legal-Aged College Student Drinkers.

Alcohol use, reasons for use, and consequences of use continue to be a major concern in college student populations. This is especially true for students of legal drinking age who may experience different reasons for and greater negative consequences of alcohol use than students under 21 years old. Although multiple studies have used person-centered approaches to understand motivations for and ultimately prevent alcohol use, few have identified multiple typologies of reasons for alcohol use. The current study used latent class analysis to identify homogeneous subtypes of reasons for alcohol use and how classification was associated with alcohol-related consequences in college students aged 21 years old and older (N = 2300) from the 2013 Indiana College Substance Use Survey. Four profiles of reasons for alcohol use emerged across males and females: social drinkers, feel good drinkers, relaxed escaping drinkers, and emotion coping drinkers. Although the likelihood of consequences differed across gender, the emotion coping drinkers were more likely to experience all negative consequences, suggesting that it was a high-risk class. In general, this pattern of risk continued with the feel good drinkers and female relaxed escaping drinkers. These results can help optimize college substance use prevention and intervention efforts to (1) identify and understand characteristics of high- and low-risk student drinkers and (2) tailor the content of interventions to those specific profiles resulting in more effective approaches to reducing alcohol use.

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PURPOSE: To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. METHODS: We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. RESULTS: Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. CONCLUSIONS: Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed.