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Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of nonnested models using the Bayesian information criterion to compare multiple models and identify the optimum number of clusters. The current study clustered 36 young men and women on the basis of their baseline heart rate and heart rate variability (HRV), chronic alcohol use, and reasons for drinking. Two cluster groups were identified and labeled the high alcohol risk and normative groups. Compared to the normative group, individuals in the high alcohol risk group had higher levels of alcohol use and more strongly endorsed disinhibition and suppression reasons for use. The high alcohol risk group showed significant HRV changes in response to positive and negative emotional and appetitive picture cues, compared to neutral cues. In contrast, the normative group showed a significant HRV change only to negative cues. Findings suggest that individuals with autonomic self-regulatory difficulties may be more susceptible to heavy alcohol use and use of alcohol for emotional regulation.

Finding groups using model-based cluster analysis: Heterogeneous emotional self-regulatory processes and heavy alcohol use risk.