Why is the regression point displacement design so useful in assessing the impact of community interventions?

What will be an ideal response?

The regression point displacement (RPD) design is a simple quasi-experimental strategy that has important implications, especially for community-based research. The problem with community-level interventions is that it is difficult to do causal assessment to determine whether your program (as opposed to other potential factors) made a difference. Typically, in community-level interventions, program costs limit implementation of the program in more than one community. You look at pre-post indicators for the program community and see whether there is a change. If you're relatively enlightened, you seek out another similar community and use it as a comparison. However, because the intervention is at the community level, you have only a single unit of measurement for your program and comparison groups.

The RPD design attempts to enhance the single-program-unit situation by comparing the performance on that single unit with the performance of a large set of comparison units. In community research, you would compare the pre-post results for the intervention community with a large set of other communities. The advantage of doing this is that you don't rely on a single nonequivalent community; you attempt to use results from a heterogeneous set of nonequivalent communities to model the comparison condition and then compare your single site to this model. For typical community-based research, such an approach may greatly enhance your ability to make causal inferences.