Data Analysis for Policy and Administration

Catalog description

This course is intended to prepare students to conduce independent research in policy analysis, program evaluation, and public management. Students will not only learn statistical methods, but how to use them to evaluate public problems and suggest solutions. The course begins with a review of basic research design practices and how statistical analysis fits into the research process. This will be followed with an overview of probability theory as a foundation for inferential statistics. We will then move through the core components of hypothesis testing - the normal distribution, confidence intervals, p-values, and the central limit theorem. All of this lays the foundation for addressing specific types of statistical techniques that administrators and analysts are likely to either conduct or interpret. These include basic descriptive statistics, contingency tables, comparing means, correlation, linear regression, and logistic regression. We will also cover how to effectively visualize data in tables, graphs, and basic spatial displays of data (i.e., mapping using online software). These skills are particularly important for writing professional and academic research reports in public policy and administration and presenting complex information in a straightforward way that the public can understand. The emphasis in the course is on how these tools can be used for public sector and academic research, as well as how to solve policy and management problems. It prepares students for more advanced courses in policy analysis and program evaluation.