American Journal of Community Psychology

, Volume 51, Issue 1–2, pp 254–263 | Cite as

Evaluating New York City’s Smoke-Free Parks and Beaches Law: A Critical Multiplist Approach to Assessing Behavioral Impact

  • Michael JohnsEmail author
  • Micaela H. Coady
  • Christina A. Chan
  • Shannon M. Farley
  • Susan M. Kansagra
Original Paper


This article describes the evaluation of the law banning smoking in New York City’s parks and beaches that went into effect in 2011. We discuss the practical and methodological challenges that emerged in evaluating this law, and describe how we applied the principles of critical multiplism to address these issues. The evaluation uses data from three complementary studies, each with a unique set of strengths and weaknesses that can provide converging evidence for the effectiveness of the law. Results from a litter audit and an observational study suggest the ban reduced smoking in parks and beaches. The purpose, methodology and baseline results from an ongoing survey that measures how frequently adults in NYC and across New York State notice people smoking in parks and on beaches are presented and discussed. Limitations are considered and suggestions are offered for future evaluations of similar policies.


Environmentally-based interventions Critical multiplism Tobacco control Outdoor smoking bans 



We would like to thank Keith Kerman, Sherry Lee and Michelle Darbouze from the New York City Department of Parks and Recreation for their help designing the sample and collecting data for the smoking litter audit. We also thank Kari Auer, Ijeoma Mbamalu and the staff from the Bureau of Chronic Disease Prevention and Tobacco Control for their help collecting data for the litter audit study and the observational study of smoking in parks.


  1. Americans for Nonsmokers’ Rights. (2011). Outdoor area lists: as of July 1, 2011. Retrieved from
  2. Blankertz, L. (1998). The value and practicality of deliberate sampling for heterogeneity: A critical multiplist perspective. American Journal of Evaluation, 19, 307–324.Google Scholar
  3. Chang, C., Leighton, J., Mostashari, F., McCord, C., & Frieden, T. R. (2004). The New York City smoke-free air act: Second-hand smoke as a worker health and safety issue. American Journal of Industrial Medicine, 46, 188–195.PubMedCrossRefGoogle Scholar
  4. Chen, H., & Rossi, P. H. (1987). A theory-driven approach to validity. Evaluation and Program Planning, 10, 95–103.CrossRefGoogle Scholar
  5. Cohen, J. (1977). Statistical power analysis for the behavioral sciences. NY: Academic Press.Google Scholar
  6. Cook, T. D. (1985). Post-positivistic critical multiplism. In L. Shotland & M. M. Mark (Eds.), Social science and social policy. Newbury Park, CA: Sage.Google Scholar
  7. Cook, T. D. (1990). The generalization of causal connections: Multiple theories in search of clear practice. In L. Sechrest, J. Bunker, & E. Perrin (Eds.), Research methodology: Strengthening causal interpretation of non-experimental data. Rockville, MD: Agency for Health Care Policy & Research.Google Scholar
  8. Frieden, T. R., Mostashari, F., Kerker, B., Miller, N., Hajat, A., & Frankel, M. (2005). Adult tobacco use levels after intensive tobacco control measures: New York City, 2002–2003. American Journal of Public Health, 95, 1016–1023.PubMedCrossRefGoogle Scholar
  9. Gardner, W., Mulvey, E. P., & Shaw, E. C. (1995). Regression analysis of counts and rates: Poisson, overdispersed, and negative binomial models. Psychological Bulletin, 118, 392–404.PubMedCrossRefGoogle Scholar
  10. Hamilton, W. L., Biener, L., & Brennan, R. T. (2008). Do local tobacco regulations influence perceived smoking norms? Evidence from adult and youth surveys in Massachusetts. Health Education Research, 23, 709–722.PubMedCrossRefGoogle Scholar
  11. Hanley, J. A., Negassa, A., Edwardes, M. D., & Forrester, J. E. (2003). Statistical analysis of correlated data using generalized estimating equations: An orientation. American Journal of Epidemiology, 157, 364–375.PubMedCrossRefGoogle Scholar
  12. Harris, K. J., Stearns, J. N., Kovach, R. G., & Harrar, S. W. (2009). Enforcing an outdoor smoking ban on a college campus: Effects of a multicomponent approach. Journal of American College Health, 58, 121–126.PubMedCrossRefGoogle Scholar
  13. Houts, A. C., Cook, T. D., & Shadish, W. R. (1986). The Person-situation debate: A critical multiplist perspective. Journal of Personality, 54, 52–101.CrossRefGoogle Scholar
  14. Kaptchuk, T. J. (2001). The double-blind, randomized, placebo-controlled trial: Gold standard or golden calf? Journal of Clinical Epidemiology, 54, 541–549.PubMedCrossRefGoogle Scholar
  15. Kennedy, R. D., Fong, G. T., Thompson, M. E., Kaufman, P., Ferrence, R., & Schwartz, R. (2010). Evaluation of a comprehensive outdoor smoking by-law: A longitudinal study of smokers and non-smokers in the Canadian city of Woodstock. Poster presented at the annual meeting of the Society for Research on Nicotine and Tobacco, Baltimore, MD.Google Scholar
  16. Klepeis, N. E., Ott, W. R., & Switzer, P. (2007). Real-time measurement of outdoor tobacco smoke particles. Journal of Air and Waste Management Association, 57, 522–534.CrossRefGoogle Scholar
  17. Lewandowski, G. W., & Strohmetz, D. B. (2009). Actions can speak as loud as words: Measuring behavior in psychological science. Social and Personality Psychology Compass, 3, 992–1002.CrossRefGoogle Scholar
  18. Nagle, A. L., Schofield, M. J., & Redman, S. (1996). Smoking on hospital grounds and the impact of outdoor smoke-free zones. Tobacco Control, 5, 199–204.PubMedCrossRefGoogle Scholar
  19. Novotny, T. E., Lum, K., Smith, E., Wang, V., & Barnes, R. (2009). Cigarette butts and the case for an environmental policy on hazardous cigarette waste. International Journal of Research and Public Health, 6, 1–15.Google Scholar
  20. Reynolds, K. D., & West, S. G. (1988). A multiplist strategy for strengthening nonequivalent control group designs. Evaluation Review, 11, 691–714.CrossRefGoogle Scholar
  21. Shadish, W. R. (1993). Critical multiplism: A research strategy and its attendant tactics. New Directions for Evaluation, 60, 13–57.Google Scholar
  22. Shadish, W. R., & Cook, T. D. (2009). The renaissance of field experimentation in evaluating interventions. Annual Review of Psychology, 60, 607–629.PubMedCrossRefGoogle Scholar
  23. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.Google Scholar
  24. Shadish, W. R., Cook, T. D., & Houts, A. C. (1986). Quasi-experimentation in a critical multiplist mode. In W. M. K. Trochim (Ed.), Advances in quasi-experimental design and analysis (pp. 29–46). San Francisco, CA: Jossey-Bass.Google Scholar
  25. Shotland, R. L., & Mark, M. M. (1987). Improving inferences from multiple methods. New Directions for Program Evaluation, 35, 77–94.CrossRefGoogle Scholar
  26. Webb, E. J., Campbell, D. T., Schwarzt, R. D., & Sechrest, L. (1961). Unobtrusive measures. Thousand Oaks, CA: Sage.Google Scholar
  27. West, S. G. (2009). Alternatives to randomized experiments. Current Directions in Psychological Science, 18, 299–304.CrossRefGoogle Scholar

Copyright information

© Society for Community Research and Action 2012

Authors and Affiliations

  • Michael Johns
    • 1
    Email author
  • Micaela H. Coady
    • 1
  • Christina A. Chan
    • 1
  • Shannon M. Farley
    • 1
  • Susan M. Kansagra
    • 1
  1. 1.New York City Department of Health and Mental HygieneBureau of Chronic Disease Prevention and Tobacco ControlQueensUSA

Personalised recommendations