The study proposes to identify contextual influences (family, school, neighborhood, and peer) as antecedent to and predictive of the acquisition and development of substance use from early childhood through early adolescence. This study expands on previous research by (a) focusing on attitude in early childhood as antecedent to later substance use and (b) including risk factors from several theoretical perspectives as predictive of attitude and use, particularly emphasizing the contextual environment of the child. The development of other problem behaviors, such as antisocial behavior and poor academic performance, will be studied along with the development of substance use. A cohort sequential design will be used to follow five grade cohorts (n=1000 1st-5th grade children) with annual assessments over four years (until grades 4 through 8). With this design we will be able to examine the development of attitudes toward the use of substances, across an eight-year development period, from age 6 (1st grade) to age 13 (8th grade), with only four years of data collection. Data will be obtained from multiple sources, including children, their parents, their teachers, principals, and official (school and court) records. First, third graders will be assessed via interview and older children will complete questionnaires. Parents and teachers will complete questionnaires, principals will be interviewed, and school and court records will be searched. These assessments will provide measures of family factors (e.g. relationship within the family, parenting skills, including monitoring, parent and sibling use, and parents stated toward use), peer factors, including neighborhood and school friends, (e.g. use, attitude, and anti-social behavior), neighborhood climate (e.g. crime, substance use, livability), school climate (e.g. average SES, average academic performance, substance use of students, school antisocial activity), personal factors(e.g., social skills, self-esteem) and demographics (SES, family size, family structure), as well as the target child's substance use, anti-social behavior, and academic performance. Data will be analyzed using state of the art statistical techniques [.g., Latent Growth Modeling (LGM) and Generalized Estimating Equations (GEE)], specifically designed for the analysis of longitudinal data. In addition, we plan to use regression and Structural Equation Modeling.
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