Statistics Seminar: Mike Baiocchi, Stanford
We need better tools to solve these problems – several statistical wins in the fight against sexual assault
Our team’s research on preventing sexual assault in Kenya has led to the development of several new statistical techniques to overcome challenges inherent in contexts where behaviors and specifically behavior-change are of great importance. The techniques include: (i) a method for designing randomized trials when you anticipate “spillover” or “contamination” between the intervention groups, (ii) a method for using open-response/free-text and getting “confidence sets” and “p-values” that rigorously assess the causal differences between the two groups, and (iii) a geo-spatial, mixed methods approach to understanding and communicating the burden of gender based violence. The goal of this talk is to present cool new methods that are useful for you and your research. Lots of intuition and pictures will be used.
Biosketch: Michael Baiocchi, PhD, is an Assistant Professor in the Stanford Prevention Research Center. He is an interventional-statistician, creating interventions and the means for analyzing them. He specializes in creating simple, easy to understand statistical methodologies for getting reliable results out of messy data and messy situations. His research is in nonparametric estimation and design-based inference. He was the inaugural Stein Fellow in the department of Statistics at Stanford University. He is the principal investigator on a large (enrollment: 5,000+ students, 100+ schools) randomized study of a sexual assault prevention intervention in the informal settlements around Nairobi, Kenya. He is currently launching a five-university study of a sexual assault resistance program in the United States.