With the implementation of social interventions, program planners and funders intend to induce impacts on different levels and in different areas. In order to legitimate the resource input of interventions and to check whether implemented interventions achieve their intended goals, there is a need of impact evaluations that allow evaluators to make resilient conclusions about the effectiveness of interventions. Although this task is one of the most important in evaluation practice, it is also a very difficult one. This is because assessing the impacts of social interventions requires the attribution of observed changes in outcome variables of interest to an intervention under study (Stockmann 2008). Consequently, potential confounding factors that could also be responsible for observed effects have to be controlled or eliminated statistically or by design. In order to do this, evaluators frequently work with different kinds of comparative methods (Reichardt 2011) such as randomized controlled trials (RCTs), quasi-experimental designs (QEDs) or before-after measurements and longitudinal designs. Basically, all of these methods have in common that they help evaluators to estimate so-called treatment effects, which ideally represent precise and generalizable estimations of the impacts of interventions under study. Therefore, the use of such designs provides information about how to answer the question ‘Does an intervention have any effects?’ Since estimates of intervention effects provide valuable information about the ability of interventions to produce intended effects, they may serve as a basis for decision-making about the continuation or termination of interventions or to legitimate the resource input needed for their implementation.
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- The Future of Impact Evaluation Is Rigorous and Theory-Driven
Christoph E. Mueller
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