Randomization of treatment assignment has been one of the most powerful tools for the development of modern science ever since the 1920s, and randomized experiments have been widely used to test hypotheses in the natural sciences. The fields of political science and public policy were long dominated by observational studies, but they now show growing use of experimental studies.
The goal of the proposed research is to develop and evaluate new methods for the statistical analysis of randomized experiments. In the social sciences, experiments are often conducted outside of a laboratory to increase the generalizability of conclusions. However, this comes at the expense of having complete control over experimental participants' exposure to experimental treatments. Thus, statistical adjustments often must be made in order to ascertain valid causal effects. In the proposed research, new statistical methods will be developed and evaluated that address missing data problems in randomized experiments when the missing data mechanism depends on unobserved values of variables and implement efficient experimental designs when the unit of randomization is a cluster of individuals.
The intellectual merit of the proposal lies in the wide applicability of the proposed methods within and beyond political science and public policy. The PI's ongoing strategy is to develop statistical methods in the context of specific experiments. Use of these motivating examples allows the PI to discover areas in which statistical analysis must be improved and new methods need to be developed. Five experiments will be analyzed; voting (survey) experiments in Germany and Japan, randomized evaluation of the Mexican universal health insurance program, a field experiment about deliberative decision-making in Africa, and a survey experiment about effects of racial priming on political attitudes. The PI has worked extensively on methodological research for causal inference with a particular emphasis on applications in political science, and the proposed research builds on the earlier work. The proposed methods as well as their substantive applications make original contributions.
The broader impacts of this project are several. First, substantive progress will be made on the analyses of the aforementioned randomized experiments. New methods will be developed to better understand the effects of policy information and psychological manipulation on voting behavior; the health and financial effects of the Mexican universal health insurance program; the role of leaders in deliberative democracy; and the recent academic debate about racial priming. The proposed methods can also have applications beyond political science and public policy and into medical community recommendations for best practices in the conduct and analysis of cluster-randomized trials. The proposed research suggests the need to question the current standards for this research. Ultimately, improved methods will be made available to applied researchers with limited knowledge of statistical theory and computing.
