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.
Prevention Science – Springer Journals
Published: Feb 17, 2013
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