their clear scientific value, randomized experiments of prevention programs are often viewed warily by communities and institutions, and their place in community prevention studies is often questioned. Since trials can be conducted only under the aegis of communities and their organizations, this chapter presents information about these trials so community leaders and policy makers can make informed decisions about whether such trials match their own community values and meet their needs, or if alternative designs are needed.

The chapter also reviews the use of other designs, including natural experimental designs and nonexperimental designs to examine a program’s effects, whether a training model works, and whether a program can be implemented with sufficient strength or fidelity in different communities.

Next comes an overview of statistical analysis methods that incorporate longitudinal and multilevel data from prevention studies to model how interventions affect young people’s development in different contexts. We discuss the unique strengths of qualitative data in prevention research and ways that qualitative and quantitative data can be used alongside one another. Finally, the chapter identifies challenges that have not yet been met in addressing the fundamental research questions in the prevention field.


Randomized preventive trials are central in evaluating efficacy (impact under ideal conditions) or effectiveness (impact under conditions that are likely to occur in a real-world implementation) of specific intervention programs that are tested in particular contexts (Coie, Watt, et al., 1993; Kellam, Koretz, and Moscicki, 1999; Howe, Reiss, and Yuh, 2002; Kellam and Langevin, 2003). The design for a randomized trial divides participants into equivalent groups that are exposed to different interventions, and analysis that appropriately compares outcomes for those exposed to different interventions leads to inferential statements about each intervention’s effects. A well-conducted randomized trial is a high-precision instrument that leads to causal statements about a program’s effect so that one can be assured that any observed differences are due to the different interventions and not some other factor.

Randomization strengthens confidence in the conclusions about an intervention’s impact by ensuring the equivalence of the intervention and the control groups. Because of random assignment, participants in the two intervention conditions are nearly equivalent prior to the study, both on measured characteristics, such as age, gender, and baseline risk, and on relevant characteristics that may not be measured, such as community readiness. With randomized assignment to these groups, it is possible to

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