This paper outlines a method for detecting and assessing the strength of social interactions through a changes-in-changes design. The proposed approach is based on a linear-in-means model and aims to resolve the "reflection problem", unobserved heterogeneities and endogenous group formation that plague identification of social interactions. Using longitudinal data from Add Health with rarely collected information on peer group's composition, we explore an exogenous variation in peer's drug use induced by a "mover friend" that occurs between Add Health's survey periods. This quasi-experiment shares a similar nature of a policy intervention of removing drug-user friends from a peer group. Such treatment-control group differences together with changes over time form the basis of our changes-in-changes design. Our study confirms a strong endogenous effect, which in turn motivates a "social multiplier", both of which are large enough to be relevant and are well worth attention to policy makers, researchers, health-care providers and educators for better understanding of how to protect young people and secure our future.