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Selection Effects and Prevention Program Outcomes


A primary goal of the paper is to provide an example of an evaluation design and analytic method that can be used to strengthen causal inference in nonexperimental prevention research. We used this method in a nonexperimental multisite study to evaluate short-term outcomes of a preventive intervention, and we accounted for effects of two types of selection bias: self-selection into the program and differential dropout. To provide context for our analytic approach, we present an overview of the counterfactual model (also known as Rubin's causal model or the potential outcomes model) and several methods derived from that model, including propensity score matching, the Heckman two-step approach, and full information maximum likelihood based on a bivariate probit model and its trivariate generalization. We provide an example using evaluation data from a community-based family intervention and a nonexperimental control group constructed from the Washington State biennial Healthy Youth Survey (HYS) risk behavior data (HYS n = 68,846; intervention n = 1,502). We identified significant effects of participant, program, and community attributes in self-selection into the program and program completion. Identification of specific selection effects is useful for developing recruitment and retention strategies, and failure to identify selection may lead to inaccurate estimation of outcomes and their public health impact. Counterfactual models allow us to evaluate interventions in uncontrolled settings and still maintain some confidence in the internal validity of our inferences; their application holds great promise for the field of prevention science as we scale up to community dissemination of preventive interventions.

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  1. Note that this is a weaker assumption than the strong ignorability condition, which requires unconditional independence.

  2. Our number of program variable refers to frequency, not location within the county, and hence refers to opportunity for access, not ease of access.

  3. The county-level variables are used as instrumental variables (explanatory variables that are correlated with unobserved predictors but not with outcome) in our estimation. Instrumental variable methods are explained in Technical Appendix B.

  4. As indicated in footnote 2, this variable refers to opportunity for access not ease of access, hence is unlikely to be related to completion.

  5. One alternative would have been to include dummy variables for each program; however, the program-specific risk factor averages and standard deviations contain more information. (The county-level dummies provide an additional control for non-independence of observations.) Another approach would have been to treat program as a random effect, but we are more interested in the fixed effects of programs than in the programs as a random sample of programs from which we wish to generalize to the population of programs at large (cf. Serlin et al. 2003).


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This study was supported in part by the National Institute of Drug Abuse (grants R21 DA025139-01Al and R21 DA19758-01). We thank the Washington State Department of Health for providing the supplementary data sample, and we thank the program providers and families who participated in the program evaluation.

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Correspondence to Laura G. Hill.

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The first two authors are listed alphabetically and contributed equally to the conceptualization and writing of the manuscript.

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Hill, L.G., Rosenman, R., Tennekoon, V. et al. Selection Effects and Prevention Program Outcomes. Prev Sci 14, 557–569 (2013).

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  • Selection effects
  • Translational research
  • Universal prevention
  • Family-focused interventions
  • Causal inference
  • Observational research
  • Nonexperimental research