HOW CAN WE MAKE BETTER CAUSAL INFERENCES FROM SURVEY DATA? The main goal of my research is to improve statistical methods for making causal inferences using non-experimental data. Although randomized experiments are the best methods for demonstrating causal relationships, they are usually not ethical or practical for answering the kinds of questions that most social scientists ask. So we have to settle for “second best” methods that have lots of potential pitfalls. I have been studying a collection of methods that enable us to get much closer to an experimental design. Known as “fixed effects methods”, these statistical techniques enable one to control for all stable characteristics of persons, regardless of whether we can measure those characteristics. They accomplish this by using each person as his or her own control. In some versions, these methods can also answer questions about the direction of causality: does X cause Y or does Y cause X? My other major research interest is statistical methods for handling missing data. Two new methods, multiple imputation and maximum likelihood, have been shown to be far superior to more traditional missing-data methods. Nevertheless, they are still not widely used by social scientists. I hope to change that by making these methods easier to use and better understood.