AIMS: To demonstrate an integrative methodology to explore psychological constructs, we used multiple methods in the examination of alcohol expectancies-a psychological construct that is generally measured using survey methodology. We then used the methodology in order to assess the relationship of alcohol expectancy dimensions to drinking-related outcomes. DESIGN: We developed alcohol expectancy models using a cognitive paradigm designed to maximize cognitive activation of expectancies, in order to delineate dimensions of alcohol expectancies. Next, using multidimensional scaling (MDS), we defined heuristic models representing domains for arousing, sedating, positive, and negative alcohol expectancies. We then assessed these models in a separate sample using confirmatory factor analysis (CFA) and covariance structure modeling. SETTING: The research was conducted at a large public university in the southeastern US. PARTICIPANTS: A total of 927 male and female college students ranging in age from 17 to 35 years participated. MEASUREMENTS: Measures included the Alcohol Expectancy Inventory, the Quantity Frequency Index (QFI), and the Drinking Styles Questionnaire. FINDINGS: Single indicator models representing expectancies for arousing and positive effects of alcohol did not differ significantly in the prediction of drinking, and both accounted for significantly more variance in self-reported drinking than expectancies for sedating and negative effects of alcohol consumption. Expectations for the sedating effects of alcohol accounted for significantly more variance than those for negative effects. Expectations for sedating effects added unique variance in the prediction of drinking when all predictors were simultaneously modeled. CONCLUSIONS: The multiple methods integrated here can be used in the development and testing of alcohol expectancy models. This integrative methodology warrants further development and validation.
AIMS: To demonstrate an integrative methodology to explore psychological constructs, we used multiple methods in the examination of alcohol expectancies-a psychological construct that is generally measured using survey methodology. We then used the methodology in order to assess the relationship of alcohol expectancy dimensions to drinking-related outcomes. DESIGN: We developed alcohol expectancy models using a cognitive paradigm designed to maximize cognitive activation of expectancies, in order to delineate dimensions of alcohol expectancies. Next, using multidimensional scaling (MDS), we defined heuristic models representing domains for arousing, sedating, positive, and negative alcohol expectancies. We then assessed these models in a separate sample using confirmatory factor analysis (CFA) and covariance structure modeling. SETTING: The research was conducted at a large public university in the southeastern US. PARTICIPANTS: A total of 927 male and female college students ranging in age from 17 to 35 years participated. MEASUREMENTS: Measures included the Alcohol Expectancy Inventory, the Quantity Frequency Index (QFI), and the Drinking Styles Questionnaire. FINDINGS: Single indicator models representing expectancies for arousing and positive effects of alcohol did not differ significantly in the prediction of drinking, and both accounted for significantly more variance in self-reported drinking than expectancies for sedating and negative effects of alcohol consumption. Expectations for the sedating effects of alcohol accounted for significantly more variance than those for negative effects. Expectations for sedating effects added unique variance in the prediction of drinking when all predictors were simultaneously modeled. CONCLUSIONS: The multiple methods integrated here can be used in the development and testing of alcohol expectancy models. This integrative methodology warrants further development and validation.
Authors: Seth C Kalichman; Demetria Cain; Lisa Eaton; Sean Jooste; Leickness C Simbayi Journal: Am J Public Health Date: 2011-07-21 Impact factor: 9.308
Authors: Gregory A Aarons; Rebecca S Wells; Karen Zagursky; Danielle L Fettes; Lawrence A Palinkas Journal: Am J Public Health Date: 2009-09-17 Impact factor: 9.308
Authors: Paul A Pilkonis; Lan Yu; Jason Colditz; Nathan Dodds; Kelly L Johnston; Catherine Maihoefer; Angela M Stover; Dennis C Daley; Dennis McCarty Journal: Drug Alcohol Depend Date: 2012-12-01 Impact factor: 4.492