In the Spring of 2015, I committed to carrying out my general exams in a way that made a contribution to public knowledge. I'm summarizing every book and article I read on AcaWiki (the full list is here).
This repository on Github includes all the data analyses I carry out in the quantitative section on "Statistical Methods for Computational Social Science."
--J. Nathan Matias, PhD Student, MIT Media Lab & Center for Civic Media
To execute these code examples, you will need to have jupyter notebook 3.0, along with standard Python data/stats libraries pandas, statsmodels, numpy, seaborn, and scipy.
Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. 1 edition. Thousand Oaks: SAGE Publications, Inc.
Murnane, Richard J., and John B. Willett. 2010. Methods Matter: Improving causal Inference in Educational and Social Science Research, Oxford University Press.
- Regression Discontinuity Analysis
- Instrumental Variables Estimation in Python
- Using Instrumental-Variables Estimation to Recover the Treatment Effect in Quasi-Experiments
Singer, Judith D., and John B. Willett. 2003. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford university press.