Prevention Science

, Volume 19, Issue 8, pp 1091–1101 | Cite as

Empirically Based Mean Effect Size Distributions for Universal Prevention Programs Targeting School-Aged Youth: A Review of Meta-Analyses

  • Emily E. Tanner-SmithEmail author
  • Joseph A. Durlak
  • Robert A. Marx


This review of reviews presents an empirically based set of mean effect size distributions for judging the relative impact of the effects of universal mental health promotion and prevention programs for school-age youth (ages 5 through 18) across a range of program targets and types of outcomes. Mean effect size distributions were established by examining the findings from 74 meta-analyses of universal prevention and promotion programs that included more than 1100 controlled outcome studies involving over 490,000 school-age youth. The distributions of mean effect sizes from these meta-analyses indicated considerable variability across program targets and outcomes that differed substantially from Cohen’s (1988, Statistical power analysis for the behavioral sciences (2nd ed.)) widely used set of conventions for assessing if effects are small, medium, or large. These updated mean effect size distributions will provide researchers, practitioners, and funders with more appropriate evidence-based standards for judging the relative effects of universal prevention programs for youth. Limitations in current data and directions for future work are also discussed.


Effect sizes Meta-analysis Universal prevention Youth 


Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflicts of interest. JAD acknowledges that he is an author of several meta-analyses included in this review, but he was not involved in extracting any data from those meta-analyses for the purposes of this review.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

Informed Consent

For this type of study, formal consent is not required.

Supplementary material

11121_2018_942_MOESM1_ESM.docx (275 kb)
ESM 1 (DOCX 275 kb)


  1. Bloom, H. S., Hill, C. J., Black, A. R., & Lipsey, M. W. (2008). Performance trajectories and performance gaps as achievement effect-size benchmarks for educational interventions. Journal of Research on Educational Effectiveness, 1, 289–328. Scholar
  2. Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. West Sussex: Wiley. Scholar
  3. Bradshaw, C. P., Waasdorp, T. E., & Leaf, P. J. (2012). Effects of school-wide positive behavioral interventions and supports on child behavior problems. Pediatrics, 130, e1136–e1145. Scholar
  4. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  5. Cooper, H., & Koenka, A. C. (2012). The overview of reviews: Unique challenges and opportunities when research syntheses are the principal elements of new integrative scholarship. American Psychologist, 67, 446–462. Scholar
  6. Cox, D. R., & Snell, E. J. (1989). Analysis of binary data (2nd ed.). New York: Chapman & Hall/CRC.Google Scholar
  7. Durlak, J. A. (1995). School-based prevention programs for children and adolescents. Thousand Oaks: Sage. Scholar
  8. Durlak, J. A. (1997). Effective prevention programs for children and adolescents. New York: Plenum. Scholar
  9. Durlak, J. A. (2009). How to select, calculate, and interpret effect sizes. Journal of Pediatric Psychology, 34, 917–928. Scholar
  10. Furr-Holden, C. D., Ialongo, N. S., Anthony, J. C., Petras, H., & Kellam, S. G. (2004). Developmentally inspired drug prevention: Middle school outcomes in a school-based randomized prevention trial. Drug and Alcohol Dependence, 73, 149–158. Scholar
  11. Grucza, R. A., Plunk, A. D., Hipp, P. R., Cavazos-Rehg, P., Krauss, M. J., Brownson, R. C., & Bierut, L. J. (2013). Long-term effects of laws governing youth access to tobacco. American journal of Public Health, 103(8), 1493–1499.CrossRefGoogle Scholar
  12. Harris, D. N. (2009). Toward policy-relevant benchmarks for interpreting effect sizes: Combining effects with costs. Educational Evaluation and Policy Analysis, 31, 3–29. Scholar
  13. Hill, C. J., Bloom, H. S., Black, A. R., & Lipsey, M. W. (2007). Empirical benchmarks for interpreting effect sizes in research. Child Development Perspectives, 2, 172–177. Scholar
  14. Kumpfer, K. L., & Alvarado, R. (2003). Family-strengthening approaches for the prevention of youth problem behaviors. American Psychologist, 58, 457. Scholar
  15. Lippke, S., Nigg, C. R., & Maddock, J. E. (2012). Health-promoting and health-risk behaviors: Theory-driven analyses of multiple health behavior change in three international samples. International Journal of Behavioral Medicine, 19, 1–13. Scholar
  16. Lipsey, M. W., & Cullen, F. T. (2007). The effectiveness of correctional rehabilitation: A review of systematic reviews. Annual Review of Law and Social Science, 3, 297–320. Scholar
  17. Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Prisma Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6, e1000097. Scholar
  18. Peters, L. W., Kok, G., Ten Dam, G. T., Buijs, G. J., & Paulussen, T. G. (2009). Effective elements of school health promotion across behavioral domains: A systematic review of reviews. BMC Public Health, 9, 182. Scholar
  19. Plonsky, L., & Oswald, F. L. (2014). How big is “big”? Interpreting effect sizes in L2 research. Language Learning, 64, 878–912. Scholar
  20. Sandler, I., Wolchik, S. A., Cruden, G., Mahrer, N. E., Ahn, S., Brincks, A., & Brown, C. H. (2014). Overview of meta-analyses of the prevention of mental health, substance use and conduct problems. Annual Review of Clinical Psychology, 10, 243. Scholar
  21. Substance Abuse & Mental Health Data Archive (SAMHDA). (2016). National Survey on Drug Use and Health 2015 (NSDUH-2016-DS0001). Retrieved from
  22. Wagenaar, A. C., Salois, M. J., & Komro, K. A. (2009). Effects of beverage alcohol price and tax levels on drinking: A meta-analysis of 1003 estimates from 112 studies. Addiction, 104, 179–190. Scholar
  23. Wiefferink, C. H., Peters, L., Hoekstra, F., Ten Dam, G., Buijs, G. J., & Paulussen, T. G. (2006). Clustering of health-related behaviors and their determinants: Possible consequences for school health interventions. Prevention Science, 7, 127–149. Scholar

Copyright information

© Society for Prevention Research 2018

Authors and Affiliations

  1. 1.Department of Counseling Psychology and Human ServicesUniversity of OregonEugeneUSA
  2. 2.Loyola University ChicagoChicagoUSA
  3. 3.Department of Human and Organizational DevelopmentVanderbilt UniversityNashvilleUSA

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