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Using Propensity Score Subclassification for Multiple Treatment Doses to Evaluate a National Antidrug Media Campaign

Author

Listed:
  • Elaine Zanutto
  • Bo Lu
  • Robert Hornik

Abstract

In 1998, the U.S. Office of National Drug Control Policy launched a national media campaign in an effort to reduce and prevent drug use among young Americans. Because the campaign was implemented nationwide, there is no control group available for use in evaluating the effects of the campaign. Nevertheless, it is possible to use propensity score methods to evaluate the effects of the campaign. However, because teens receive varying degrees of exposure to the media campaign, it is necessary to apply propensity score methods that accommodate multiple treatment doses. This work extends that of previous authors to subclassification on the propensity score for observational studies with multiple treatment doses, rather than matching on the propensity score, and proposes modifications to accommodate complex survey data. This methodology is illustrated using data from a pilot study for the media campaign evaluation.

Suggested Citation

  • Elaine Zanutto & Bo Lu & Robert Hornik, 2005. "Using Propensity Score Subclassification for Multiple Treatment Doses to Evaluate a National Antidrug Media Campaign," Journal of Educational and Behavioral Statistics, , vol. 30(1), pages 59-73, March.
  • Handle: RePEc:sae:jedbes:v:30:y:2005:i:1:p:59-73
    DOI: 10.3102/10769986030001059
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    Cited by:

    1. Kuchibhotla, Murali, 2013. "Three essays in development economics," ISU General Staff Papers 201301010800004461, Iowa State University, Department of Economics.
    2. Lenis, David & Ackerman, Benjamin & Stuart, Elizabeth A., 2018. "Measuring model misspecification: Application to propensity score methods with complex survey data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 48-57.
    3. Anna Maria Koukal, 2017. "How Vatican II influenced female enfranchisement: A story of rapid cultural change," CREMA Working Paper Series 2017-07, Center for Research in Economics, Management and the Arts (CREMA).
    4. Shu Yang & Guido W. Imbens & Zhanglin Cui & Douglas E. Faries & Zbigniew Kadziola, 2016. "Propensity score matching and subclassification in observational studies with multi‐level treatments," Biometrics, The International Biometric Society, vol. 72(4), pages 1055-1065, December.
    5. Rüdiger Mutz & Hans-Dieter Daniel, 2012. "The generalized propensity score methodology for estimating unbiased journal impact factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(2), pages 377-390, August.

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