Smoking Consequences Questionnaire: A Reevaluation of the Psychometric Properties Across Two Independent Samples of Smokers

Drug use outcome expectancies are a central construct to psychosocial theories of addictive disorders. In tobacco literature, the Smoking Consequences Questionnaire (SCQ; Brandon & Baker, 1991) is a tool used to assess this construct. Despite its common use, the SCQ has received little psychometric evaluation. In the current report, samples from 2 studies were used to examine the assumed SCQ structure, develop a novel truncated scale, and evaluate the psychometric properties of the novel scale. In Study 1, the 4-factor SCQ structure was examined using data from 343 (32.4% female; Mage = 43.7; SD = 10.8) adult nontreatment-seeking smokers. Results from Study 1 indicated that the 4-factor SCQ structure did not adequately explain covariance between items. Instead, results provided evidence for a 5-factor structure that tapped into outcome expectancies related to (a) immediate negative consequences (IC), (b) long-term negative consequences (LTC), (c) sensory satisfaction (SS), (d) negative affect reduction, and (e) appetite-weight control (AW). In Study 2, the 5-factor structure of the SCQ was confirmed and the construct validity was evaluated in 582 (48.2% female; Mage = 36.9; SD = 13.5) treatment-seeking adult smokers. Study 2 found evidence for measurement invariance across sex and overtime of the 5-factor structure as well as substantial construct validity. Results from 2 independent samples challenge the traditional 4-factor model of the SCQ, and instead, provide evidence for a novel 5-factor SCQ structure with strong validity and reliability. Alternate scoring algorithms for the SCQ, including a 5-subscale scheme, warrant consideration to ensure optimal measurement precision and construct differentiation. Public Significance Statement The present study provides initial evidence for the construct validity of a shorter, multifaceted Smoking Consequences Questionnaire. This briefer measure that taps into more specific smoking outcome expectancies may be easier to administer and score in clinical and research settings, and will assist clinicians in developing more accurate personalized treatment plans for smokers.