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Cigarette smoking is a leading cause of preventable illness and death in the United States. Yet, when people are able to quit, the negative effects of smoking to their health diminish over time. For this reason, it is important for behavioral scientists to understand the mechanisms which underly smoking cessation. In this study, signal detection analysis was used to determine which baseline characteristics of 602 smokers were best able to discriminate those who had quit smoking from those who had not six and eighteen months later. Variables included in this analysis were chosen based on prior research showing that they were correlated with smoking cessation. These included variables from the Transtheoretical model, and also addiction and perceived stress. Three algorithms were developed using signal detection methodology which identified subgroups of individuals who were highly likely and unlikely to quit smoking. The variables which were consistently able to discriminate outcome for certain subgroups were perceived stress, and variables from the Transtheoretical model such as selfefficacy, the benefits of smoking , and the experiential processes of change . These findings have implications as to which subgroups have particular low rates of smoking cessation and what types of interventions may be most effective to help these individuals quit. A step-care approach to interventions using the Transtheoretical model based on subgroup characteristics is suggested.

ASSESSING PREDICTORS OF SMOKING CESSATION: AN APPLICATION OF SIGNAL DETECTION METHODOLOGY