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Title: Resolving the Replication Crisis Using Multilevel Modeling.

Abstract:聽Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. For more info, please visit: recent years we have come to learn that many prominent studies in social science and medicine, conducted at leading research institutions, published in top journals, and publicized in respected news outlets, do not and cannot be expected to replicate. Proposed solutions to the replication crisis in science fall into three categories: altering procedures and incentives, improving design and data collection, and improving statistical analysis. We argue that progress in all three dimensions is necessary: new procedures and incentives will offer little benefit without better data; more complex data structures require more elaborate analysis; and improved incentives are required for researchers to try new methods. We propose a way forward involving multilevel modeling, and we discuss in the context of applications in social research and public health.