B M Quigley1, R L Collins. 1. Research Institute on Addictions, Buffalo, New York 14203, USA.
Abstract
OBJECTIVE: Modeling, or the imitation of another's behavior, has been proposed to influence alcohol consumption. The literature dealing with effects of modeling on alcohol consumption was reviewed using meta-analytic procedures in order to determine the strength of the modeling effect and the variables that moderate the effect. METHOD: Thirteen studies were examined in which participant's alcohol consumption in the presence of a high consumption model was compared to a low consumption model condition or a no-model condition. Analyses were conducted for the four dependent measures utilized in the literature: amount consumed, blood alcohol concentration, number of sips taken and volume per sip. Mean effect sizes (d) were calculated for each dependent measure and moderator variables were examined. RESULTS: Modeling had a significant effect on all four dependent measures, with the strongest effects being on amount consumed and blood alcohol concentration. In addition, analyses identified numerous variables that moderate the effect of modeling on alcohol consumption, including the drinking history of the participant, the drinking task used and the nature of the interaction between model and participant. CONCLUSIONS: Results indicated that modeling has a strong effect on alcohol consumption; however, several variables do mediate this effect.
OBJECTIVE: Modeling, or the imitation of another's behavior, has been proposed to influence alcohol consumption. The literature dealing with effects of modeling on alcohol consumption was reviewed using meta-analytic procedures in order to determine the strength of the modeling effect and the variables that moderate the effect. METHOD: Thirteen studies were examined in which participant's alcohol consumption in the presence of a high consumption model was compared to a low consumption model condition or a no-model condition. Analyses were conducted for the four dependent measures utilized in the literature: amount consumed, blood alcohol concentration, number of sips taken and volume per sip. Mean effect sizes (d) were calculated for each dependent measure and moderator variables were examined. RESULTS: Modeling had a significant effect on all four dependent measures, with the strongest effects being on amount consumed and blood alcohol concentration. In addition, analyses identified numerous variables that moderate the effect of modeling on alcohol consumption, including the drinking history of the participant, the drinking task used and the nature of the interaction between model and participant. CONCLUSIONS: Results indicated that modeling has a strong effect on alcohol consumption; however, several variables do mediate this effect.
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