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Utilizing Data for Effective Needs Assessment in Youth Substance Use Prevention

This comprehensive resource by Linda Becker, PhD, focuses on using data effectively in assessing the needs related to youth substance use. It highlights logical models, school performance measures, and significant differences in substance use rates among students. The text discusses confidence intervals, key risk and protective factors associated with alcohol and drug use, and the importance of understanding the context of data. This guide aims to provide insights for prevention programs and invites reader feedback to improve future assessments.

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Utilizing Data for Effective Needs Assessment in Youth Substance Use Prevention

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  1. Using Data in Your PRI Needs Assessment Linda Becker, PhD., Prevention Research Manager September 2011

  2. Table of Contents

  3. Logic Model

  4. School Performance HYS Measures of School Performance

  5. Confidence Intervals Significant differences: numerically • Smoked cigarettes in the state: 14.9% (± 1.4%) Between 13.5% and 16.3% • Smoked cigarettes at my school: 20.0% (± 2.2%) Between 17.8% to 22.2% Conclusion: Difference IS statistically significant (confidence intervals do not overlap)

  6. Confidence Intervals Significant differences: graphically

  7. Confidence Intervals Significant differences: graphically

  8. School Performance

  9. CORE GIS Social Indicators

  10. Consumption

  11. Youth Substance Use

  12. Youth Substance Use

  13. Intervening Variables

  14. Alcohol Availability

  15. Norms

  16. Risk/Protective Factors Most Strongly Associated with Alcohol Use • Parental attitudes favorable towards drug use. • Early initiation of drugs. • Intentions to use drugs. • Friends’ use of drugs. • Social skills. • Data on all of the risk and protective factors are available at the end of the data book.

  17. Risk/Protective Factors Most Strongly Associated with Alcohol Use

  18. Risk/Protective Factors Most Strongly Associated with Alcohol Use

  19. Additional HYS Data

  20. Additional HYS Data

  21. Risk and Protective Factors

  22. Additional CORE Data

  23. Your Comments Matter Please e-mail your comments to: • Sarah.Mariani@dshs.wa.gov • Scott.Waller@dshs.wa.gov • Linda.Becker@dshs.wa.gov

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