In observational studies, it is desirable to reduce bias due to covariates by obtaining treated and control groups with similar distributions of the covariates. This is often done by choosing well matched samples of the original treated and control groups. However, sometimes the originally chosen control units cannot provide adequate matches for the treated units. In these cases, it may be desirable to obtain matched controls from two control groups. Multiple control groups have been used in the context of causal inference to test for hidden biases; however, little work has been done on their use in matching or adjustment for these biases. We provide a theoretical basis and practical guidelines for this new approach. The motivating example is a school-wide dropout prevention program where students in the original treated and control schools were significantly different from one another. We consider the use of a second source of control students to supplement the original group.