Perceived parental monitoring was measured with a five-item Likert-style scale that evaluated adolescent perceptions of parental knowledge of whereabouts, activities, and friendships (DiClemente et al., 2001; Fletcher, Steinberg, & Williams-Wheeler, 2004; Kodl & http://www.selleckchem.com/products/U0126.html Mermelstein, 2004; Simons-Morton, 2004). Response options range from 1 = almost nothing to 3 = a lot. Higher scores indicated greater monitoring (Cronbach��s �� = .82). Depression symptoms were assessed with the CES-D inventory. The CES-D is a valid and reliable 20-item self-report measure designed to assess depression symptoms in the general population (Radloff, 1977, 1991; Roberts, Andrews, Lewinsohn, & Hops, 1990). Scores range from 0 to 60, with scores above 22 in adolescents indicative of a clinical level of depressive symptoms.

Impulsivity was measured with the impulsivity subscale of the Temperament & Character Inventory (TCI; 5 True/False items; Cloninger, Przybeck, Svrakic, & Wetzel, 1994). Impulsivity and similar constructs as measured by the TCI, and its predecessor the Temperament Personality Questionnaire, have been linked to adolescent smoking and substance use (Audrain-McGovern et al., 2004; Wills, Vaccaro, & McNamara, 1994; Wills, Windle, & Cleary, 1998). Data Analysis Univariate statistics were generated to describe the study population in terms of demographics and smoking. Univariate estimates were generated with SAS v. 9.1.3 software. Latent Growth Modeling A latent growth modeling (LGCM) was conducted to assess the effects of hedonic capacity on smoking.

LGCM is a structural equation modeling method that models repeated observed measures (measured variables) on factors (latent variables) representing random effects (��s; Duncan & Duncan, 1995). A level factor is used to represent baseline and trend factors are used to represent growth or rate of change across time (i.e., each unit change in time is associated with a �� change in a given process). In the present analysis, we conducted a two-part LGCM (Olsen & Schafer, 2001). The two-part LGCM method is ideal for modeling count variables with a preponderance of zeros (zero inflated), such as the number of cigarettes smoked in the past thirty days in a community sample of adolescents. As there are more adolescent nonsmokers than smokers, there will be a preponderance of zeros in the number of cigarettes smoked per month.

However, instead of eliminating nonsmokers, the two-part model permits the inclusion of two correlated latent growth curves, one for initiation of use (modeling transition from nonsmoking to smoking) and one for change in the number of cigarettes smoked per month among smokers. As such, the two-part model Drug_discovery includes a binary part for modeling smoking versus nonsmoking and a continuous part for modeling change in the number of cigarettes smoked among adolescents who reported previous smoking.