Why is repeated measurement important in a withdrawal design?

Repeated measurement is crucial in a withdrawal design because it helps demonstrate the effects of an intervention by showing evidence of change in response to that intervention. Withdrawal designs typically involve applying a treatment, taking repeated measurements during that phase, then removing the treatment and observing changes again. This approach allows for the comparison of behavior before, during, and after the intervention, making it possible to directly link changes in behavior to the treatment administered. When a consistent pattern of change is observed through these measurements, it strengthens the case that the intervention itself is responsible for any observed differences, rather than other factors.

The other choices, while relevant to research in general, do not specifically capture the primary purpose of repeated measurement in withdrawal designs. Identifying environmental impacts or confirming a genetic basis for behavior does not focus on demonstrating the efficacy of an intervention, and establishing a baseline before treatment, although important, is more about understanding the starting point rather than the evaluation of the treatment’s effectiveness through observed changes over time. Therefore, the key objective of repeated measurement in this context is to provide robust evidence of change attributable to the intervention.