I often describe myself as a reluctant quantitative researcher. Math was never my strong suit in high school nor undergrad. However, while getting my masters, I quickly realized that with my policy interests there was no way around quantitative methods--I had to go through them. Working with some great professors, I slow-fed myself the foundational levels of statistics and calculus until I mastered them. I started to make my weakness a strength. Then something miraculous happened: Reading Card and Kreuger's Nobel prize-winning 1994 minimum wage study, I fell in love with quantitative methods (specifically the econometric approach) and the stories (analyses) I could tell with them. Since then, I've broadened my tool kit to include techniques from biostatistics, machine learning and Bayesian statistics in my analyses of public policy. While mixed-methods research is often a buzz phrase, working with great partners when feasible, I combine my quantitative and GIS techniques with qualitative methodologies (e.g., case study analysis, structured interviewing). I find this produces a very holistic analysis that allows for much more effective story telling.
