Using R for Policy Data Analysis
Data analysis is an essential part of quantitative policy analysis; however, focused application of statistical methods is outside of the scope of what can be taught in classes such as Cost –Benefit Analysis (CBA) and Program Evaluation. In this course, students who will apply a variety of data analysis techniques using R, a free open source statistics and graphical analysis environment that is increasingly used by data miners and analysts. Class sessions will include a combination of instruction on data analysis techniques, in-class application using R, and presentations by practicing policy data analysts. Applications will focus on analysis that is relevant to the social safety net, including cases that focus on consumer protection, affordable housing and homelessness.
Students will gain experience with data analysis that is critical to the successful execution of CBA and Program Evaluation studies. By the end of the course, students will be able to use R to:
- Estimate the size of a population impacted by a policy or program (e.g. number of people experiencing homelessness at a point in time)
- Estimate the incidence of a relevant condition (e.g. being housing cost burdened) or central tendency of relevant variables (e.g. average monthly rent).
- Illustrate differences in incidence measures across demographic groups. Calculate disproportionality and disparity indices.
- Test for associations between two variables using correlation analysis, analysis of variance (ANOVA), and Chi-Square analysis.