1. Background Research consistently indicates that numerous factors from multiple domains (e.g., individual, family) are associated with heavy alcohol use during adolescence 1 Vocabulary exists that attempts to characterize the nature of relations between these influences and heavy alcohol use. 2 Prominent terms include –Risk Factor:influence that increases the likelihood of more frequent heavy alcohol use –Resource (protective, ameliorative, compensatory, and promotive factors):influence that decreases the likelihood of more frequent heavy alcohol use

2. Background Despite the intuitive appeal of considering risk and resource influences as separate constructs, empirical evidence often does not support a clear distinction between them –For example, Stouthamer-Loeber and collegues found that between 29% and 43% of study variables (e.g., parent-child relationship) could serve either risk or resource functions 3 Thus, risk and resource factors may be best conceptualized as opposite ends of a single continuum, increasing the likelihood of poor outcomes at one end and decreasing the probability at the other

3. Present Study Few studies, however, have directly investigated this possibility The purpose of the present study is to determine empirically whether correlates of adolescent heavy alcohol use from individual, family, peer, and broader contextual domains are best conceptualized as risk factors, resources, or both (i.e., opposite ends of a single continuum)

4. Method Participants –Public-use data from the National Longitudinal Study of Adolescent Health (Add Health) were used 4 –The data set includes a nationally representative sample of 7th through 12th grade youth (data from approximately 6,500 youth available in the public domain) –To reduce heterogeneity due to developmental issues, 8th graders were the focus of the present analyses n = 992 (48% boys) Mean Age = 13.46 (SD =.78)

5. Method Procedures –Add Health currently includes three waves of assessment Wave I data were used in this study –Adolescents completed an In-School Questionnaire and an In-Home Interview –A parent (usually the mother) was asked to complete a Parent Questionnaire –Neighborhood/community characteristics of participating youth are also available in the public-use data set

6. Method Measures (see Appendix and Table 1 for more details) –Numerous indices of risk and resource factors from the following domains were investigated: Individual (e.g., problem solving) Family (e.g., family income) Peer (e.g., peer connectedness) Broader contextual (e.g., unemployment rate) –Heavy alcohol use Frequency of heavy drinking (5 or more drinks past year) Frequency of times drunk (past year)

7. Preliminary Procedures The design of the Add Health study has introduced numerous complexities for statistical analyses –To correct for design effects, procedures outlined by Add Health researchers were followed 5 Each potential measure of risk and resources was coded such that higher scores correspond to more positive aspects of the construct (e.g., higher levels of family connectedness) Descriptive statistics for study measures are reported in Table 1 Zero-order correlations of measures of risk and resources with indices of heavy alcohol use are presented in Table 2

8. Results Creating 3-Level Categorical Variables Each measure of risk and resources was trichotomized with categories representing the lower 20% (risk category), middle 60% (neutral category), and upper 20% (resource category) of the distribution The three-level categorical variables then were dummy coded so that the neutral category was the referent group

9. Results Primary Analyses Two sets of regression analyses were conducted with frequency of heavy drinking (Table 4) and times dunk (Table 5) as the dependent measures For each independent variable, the two dummy vectors (i.e., risk versus neutral and resource versus neutral) were entered as predictors

10. Results Criteria for Determining Risk, Resource, or Both If only the regression coefficient for the risk versus neutral comparison is a significant positive predictor, the variable will be considered a risk factor A variable will be conceptualized as a resource if only the regression coefficient for the resource versus neutral comparison is a negative predictor If regression coefficients for the risk versus neutral and resource versus neutral comparisons are both significant predictors, the variable will be considered simply a correlate of heavy alcohol use

11. Results and Conclusions Results are similar for measures of frequency of heavy drinking and times drunk (see Tables 4 and 5) 25% of measures appear to be best conceptualized as opposite ends of a single continuum –Pubertal status, school grades, same sex peer contact, association with deviant peers, and school connectednesss 30% of measures may be best conceptualized as risk factors –Poor problem solving abilities, participation in relatively few extracurricular activities, low levels of self-esteem, poor quality of parental romantic relationship, and relatively high quantity of opposite sex peer contact

12. Results and Conclusions 10% of measures may be best conceptualized as resources –High levels of parental education and parental monitoring Importantly, of the 75% of measures not explicitly classified into the single continuum category, –Approximately 1/3 are consistent with the single continuum conceptual framework. That is, Positive coefficient for risk vs. neutral comparison and Negative coefficient for resource vs. neutral comparison Results thus provide evidence that a large proportion of correlates of heavy alcohol use may be best conceptualized using a dimensional approach

13. References 1.Hawkins, J. D., Catalano, R. F., & Miller, J. Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychological Bulletin, 112, 64-105. 2.Kaplan, H. B. (1999). Toward and understanding of resilience: A critical review of definitions and models. In M. D. Glantz & J. L. Johnson (Eds.), Resilience and development: Positive life adaptations (pp. 17-83). New York: Kluwer Academic/Plenum Publishers. 3.Stouthamer-Loeber, M. Loeber, R., Farrington, D. P., Wikstrom P. H., & Wei, E. (2002). Risk and promotive effects in the explanation of persistent serious delinquency in boys. Journal of Consulting and Clinical Psychology, 70, 111-123. 4.Bearman, P. S., Jones, J., and Udry, J. R. (1997). The National Longitudinal Study of Adolescent Health: Research Design. Retrieved April 5, 2002, from http://www.cpc.unc.edu/addhealth. http://www.cpc.unc.edu/addhealth 5.Chantala, K., & Tabor, J. (1999). Strategies to perform a design-based analysis using the Add Health data. Retrieved July 14, 2002, from http://www.cpc.unc.edu/addhealth.http://www.cpc.unc.edu/addhealth