Anker, J. J., & Carroll, M. E. (2011). Females are more vulnerable to drug abuse than males: Evidence from preclinical studies and the role of ovarian hormones. In J. C. Neill & J. Kulkarni (Eds.), Biological basis of sex differences in psychopharmacology (pp. 73–96). Berlin, Germany: Springer-Verlag.
Google Scholar
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice Hall.
Google Scholar
Cash, T. (2000). The multidimensional body self relations questionnaire. Virginia, VA: Old Dominion University.
Google Scholar
Carver, C. S. (1997). You want to measure coping but your protocol’s too long: Consider the Brief COPE. International Journal of Behavioral Medicine, 4, 92–100.
Article
PubMed
Google Scholar
Catalano, R. F., Haggerty, K. P., Hawkins, J. D., & Elgin, J. (2011). Prevention of substance use and substance use disorders: The role of risk and protective factors. In Y. Kaminer & K. C. Winters (Eds.), Clinical manual of adolescent substance abuse treatment (pp. 25–63). Washington, DC: American Psychiatric Publishing.
Google Scholar
Centers for Disease Control and Prevention. (2005). Youth Risk Behavior Survey. http://www.cdc.gov/YRBSS.
Centers for Disease Control and Prevention. (2013). Methodology of the youth risk behavior surveillance system—2013. Morbidity and Mortality Weekly Report, 62(RR01), 1–23. https://www.cdc.gov/mmwr/preview/mmwrhtml/rr6201a1.htm.
Google Scholar
Chen, X., Burgdorf, K., Dowell, K., Roberts, T., Porowski, A., & Herrell, J. M. (2004). Factors associated with retention of drug abusing women in long-term residential treatment. Evaluation and Program Planning, 27, 205–212.
Article
Google Scholar
Chung, T., Ye, F., Hipwell, A. E., Stepp, S. D., Miller, E., Borrero, S., & Hawk, M. (2017). Alcohol and marijuana use in pathways of risk for sexually transmitted infection in white and black adolescent females. Substance Abuse, 38, 77–81.
Article
PubMed
Google Scholar
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. 2nd edn. Hillsdale, NJ: Erlbaum.
Google Scholar
Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health Social Behavior, 24, 385–96.
Article
PubMed
Google Scholar
Derogatis, L. R. (1993). Brief Symptom Inventory (BSI): Administration, scoring, and procedure manual (4th ed.). Minneapolis, MN: National Computer Systems Pearson.
Google Scholar
Elliot, D. L., Goldberg, L., Moe, E. L., DeFrancesco, C. A., Durham, M. B., McGinnis, W., & Lockwood, C. (2008). Long-term outcomes of the ATHENA (Athletes Targeting Health Exercise & Nutrition Alternatives) program for female high school athletes. Journal of Alcohol and Drug Education, 52, 73–92.
PubMed
PubMed Central
Google Scholar
Epstein, J. A., Botvin, G. J., Diaz, T., Baker, E., & Botvin, E. M. (1997). Reliability of social and personal competence measures for adolescents. Psychological Reports, 81, 449–450.
Article
PubMed
Google Scholar
Faggiano, F., Minozzi, S., Versino, E., & Buscemi, D. (2014). Universal school-based prevention for illicit drug use. Cochrane Database of Systematic Reviews, 12, CD003020 doi:10.1002/14651858.CD003020.pub3.
Google Scholar
Fearnow-Kenney, M., Hansen, W. B., & McNeal, Jr., R. B. (2002). Comparison of psychosocial influences on substance use in adolescents: Implications for prevention programming. Journal of Child and Adolescent Substance Abuse, 11, 1–24.
Article
Google Scholar
Haas, A. L., Barthel, J. M., & Taylor, S. (2016). Sex and drugs and starting school: Differences in precollege alcohol-related sexual risk taking by gender and recent blackout activity. The Journal of Sex Research, 54, 741–751. doi:10.1080/00224499.2016.1228797.
Hartz, S. M., Pato, C. N., Medeiros, H., Cavazos-Rehg, P., Sobell, J. L., Knowles, J. A., et al. (2014). Comorbidity of severe psychotic disorders with measures of substance use. JAMA Psychiatry, 71, 248–254.
Article
PubMed
PubMed Central
Google Scholar
Hodder, R. K., Freund, M., Wolfenden, L., Bowman, J., Nepal, S., Dray, J., et al. (2017). Systematic review of universal school-based ‘resilience’ interventions targeting adolescent tobacco, alcohol or illicit substance use: A meta-analysis. Preventive Medicine, 100, 248–268.
Article
PubMed
Google Scholar
Holway, G. V., Tillman, K. H., Brewster, & K. L. (2017). Binge drinking in young adulthood: The influence of age at first intercourse and rate of sex partner accumulation. Archives of Sexual Behavior, 46, 525–537.
Article
PubMed
Google Scholar
Hilbe, J. (2014). Modeling count data. New York, NY: Cambridge University Press.
Book
Google Scholar
Johnston, L. D., O’Malley, P. M., Miech, R. A., Bachman, J. G., & Schulenberg, J. E. (2016a). Demographic subgroup trends among adolescents in the use of various licit and illicit drugs, 1975–2015 (Monitoring the Future Occasional Paper No. 86). Ann Arbor, MI: Institute for Social Research, University of Michigan.
Johnston, L. D., O’Malley, P. M., Miech, R. A., Bachman, J. G., & Schulenberg, J. E. (2016b). Monitoring the Future national survey results on drug use, 1975–2015: Overview, key findings on adolescent drug use. Ann Arbor: Institute for Social Research, The University of Michigan.
Google Scholar
Kumpfer, K. L., Smith, P., & Summerhays, J. F. (2008). A wakeup call to the prevention field: Are prevention programs for substance use effective for girls? Substance Use and Misuse, 43, 978–1001.
Article
PubMed
Google Scholar
Marschall-Lévesque, S., Castellanos-Ryan, N., Vitaro, F., & Séguin, J. R. (2014). Moderators of the association between peer and target adolescent substance use. Addictive Behaviors, 39, 48–70.
Article
PubMed
Google Scholar
Masten, A. S., & Powell, J. L. (2003). A resilience framework for research, policy, and practice. In S. S. Luthar (Ed.), Resilience and vulnerability: Adaptation in the context of childhood adversities (pp. 1–28). New York, NY: Cambridge University Press.
Google Scholar
NIDA. (2016). Substance Use in Women. https://www.drugabuse.gov/publications/research-reports/substance-use-in-women.
Primack, B. A., Gold, M. A., Switzer, G. E., Hobbs, R., Land, S. R., & Fine, M. J. (2006). Development and validation of a smoking media literacy scale for adolescents. Archives of Pediatric and Adolescent Medicine, 160, 369–374.
Article
Google Scholar
R Core Team. (2015). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org/.
Google Scholar
Rosenberg, M. (1989). Society and the adolescent self-image. Princeton, NJ: Princeton University Press.
Google Scholar
Salvy, S. J., De La Haye, K., Bowker, J. C., & Hermans, R. C. (2012). Influence of peers and friends on children’s and adolescents’ eating and activity behaviors. Physiology and Behavior, 106, 369–378.
Article
PubMed
PubMed Central
Google Scholar
Scheier, L. M. (2015). Theoretical models of drug use etiology: Foundations of prevention. In L. M. Scheier (Ed.), Handbook of adolescent drug use prevention (pp. 67–83). Washington DC: American Psychological Association.
Chapter
Google Scholar
Schinke, S. P., & Schwinn, T. M. (2005). Gender-specific computer-based intervention for preventing drug abuse among girls. American Journal of Drug and Alcohol Abuse, 31, 609–616.
Article
PubMed
PubMed Central
Google Scholar
Schwarz, G. E. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.
Article
Google Scholar
Schwarzer, R., & Jerusalem, M. (1995). Generalized self-sfficacy scale. In J. Weinman, S. Wright & M. Johnston (Eds.), Measures in health psychology: A user’s portfolio. Causal and control beliefs (pp. 35–37). Windsor, UK: NFER-Nelson.
Google Scholar
Schwinn, T. M., Hopkins, J. E., & Schinke, S. P. (2016). Developing a web-based intervention to prevent drug use among adolescent girls. Research on Social Work Practice, 26, 8–13.
Article
PubMed
Google Scholar
Schwinn, T. M., Hopkins, J. E., Schinke, & S. P., Liu, X. (2017). Using Facebook ads with traditional paper mailings to recruit adolescent girls for a clinical trial. Addictive Behaviors, 65, 207–213.
Article
PubMed
Google Scholar
Schwinn, T. M., Schinke, S. P., & Di Noia, J. (2010). Preventing drug abuse among adolescent girls: Outcome data from an internet-based intervention. Prevention Science, 11, 24–32.
Article
PubMed
PubMed Central
Google Scholar
Schwinn, T. M., Schinke, S. P., Hopkins, J. E., & Thom, B. (2016). Risk and protective factors associated with adolescent girls’ substance use: Data from a nationwide Facebook sample. Substance Abuse, 37, 546–570.
Article
Google Scholar
U.S. Department of Health and Human Services. (2014). The health consequences of smoking—50 yearsof progress: A report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. https://www.surgeongeneral.gov/library/reports/50-years-of-progress/exec-summary.pdf.
Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S. 4th edn. New York, NY: Springer.
Book
Google Scholar
Wakai, S., Sampl, S., Hilton, L., & Ligon, B. (2014). Women in prison: Self-injurious behavior, risk factors, psychological function, and gender-specific interventions. The Prison Journal, 94, 347–364.
Article
Google Scholar
Wechsberg, W. M., Deren, S., Myers, B., Kirtadze, I., Zule, W. A., Howard, B., & El-Bassel., N. (2015). Gender-specific HIV prevention interventions for women who use alcohol and other drugs: State of the science and future directions. Journal of Acquired Immune Deficiency Syndromes, 69, 128–139.
Article
Google Scholar
Weiss, F. L., & Nicholson, H. J. (1998). Friendly PEERsuasion against substance abuse: The Girls Incorporated model and evaluation. In J. Valentine, J. A. De Jonc & N. J. Kennedy (Eds.), Substance abuse prevention in multicultural communities. New York, NY: Haworth Press.
Google Scholar
Welch, B. L. (1947). The generalization of student’s problem when several different population variances are involved. Biometrika, 34, 28–35.
PubMed
Google Scholar
Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27, 1–25.
Google Scholar