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The authors examined the global- and event-level associations between alcohol intoxication and 10 behavioral risks during the 1st year of college. Participants (n = 1113; 62% female; 54% Caucasian) completed 30 days of Web-based self-monitoring that assessed alcohol consumption and involvement in 10 behavioral risks. Generalized estimating equations analyses were used to determine which behaviors covaried with event-level versus global indices of intoxication as well as the moderating effects of gender on the intoxication-behavior associations. Alcohol use was globally related to 8 of the 10 behavioral risks; more important, 5 of the 10 behaviors specifically covaried with daily alcohol intoxication. The differential pattern of associations observed can inform clinical work by identifying groups of students who may most benefit from various intervention approaches and content.

Self-regulation skills, which subsume goal-directed behavior and short-term delay of gratification for long-term gains, have been shown to be differentially related to alcohol consumption and alcohol-related consequences. J. M. Brown, W. R. Miller, and L. A. Lawendowski (1999) described the Self-Regulation Questionnaire (SRQ), and K. B. Carey, D. J. Neal, and S. E. Collins (2004) provided preliminary psychometric evidence for the SRQ and proposed a short version (SSRQ) of the measure. The goals of this study were to further examine the psychometric properties of the SSRQ. Participants (N = 237) were recruited from an introductory psychology course and completed a questionnaire packet that included the SSRQ. Factor analyses indicated that the SSRQ showed 2 distinct factors, an Impulse Control factor and a Goal Setting factor. Validity evidence showed differential patterns of relations between these 2 subscales and measures of self-control, alcohol use, and alcohol-related consequences.

This research tested a multilevel structural equation model of associations between 3 aspects of affective functioning (state affect, trait affect, and affective lability) and 3 alcohol outcomes (likelihood of drinking, quantity on drinking days, and dependence symptoms) in a sample of 263 college students. Participants provided 49 days of experience sampling data over 1.3 years in a longitudinal burst design. Within-person results: At the daily level, positive affect was directly associated with greater likelihood and quantity of alcohol consumption. Daily negative affect was directly associated with higher consumption on drinking days and with higher dependence symptoms. Between-person direct effects: Affect lability was associated with higher trait negative, but not positive, affect. Trait positive affect was inversely associated with the proportion of drinking days, whereas negative affectivity predicted a greater proportion of drinking days. Affect lability exhibited a direct association with dependence symptoms. Between-person indirect effects: Trait positive affect was associated with fewer dependence symptoms via proportion of drinking days. Trait negative affect was associated with greater dependence symptoms via proportion of drinking days. The results distinguish relations of positive and negative affect to likelihood versus amount of drinking and state versus trait drinking outcomes, and highlight the importance of affect variability for predicting alcohol dependence symptoms.

Analysis of alcohol use data and other low base rate risk behaviors using ordinary least squares regression models can be problematic. This article presents 2 alternative statistical approaches, generalized linear models and bootstrapping, that may be more appropriate for such data. First, the basic theory behind the approaches is presented. Then, using a data set of alcohol use behaviors and consequences, results based on these approaches are contrasted with the results from ordinary least squares regression. The less traditional approaches consistently demonstrated better fit with model assumptions, as demonstrated by graphical analysis of residuals, and identified more significant variables potentially resulting in theoretically different interpretations of the models of alcohol use. In conclusion, these models show significant promise for furthering the understanding of alcohol-related behaviors.