PADM 504: Data Analysis for Policy and Administration

Course Description

This course is intended to prepare students to conduct independent research in policy  analysis, program evaluation, and public management. Students will learn not only learn statistical methods but also understand 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.

Course Schedule

Week 1: Introduction to the Course and Review

  • Syllabus Quiz Due
  • Levels of Measurement Quiz Due

Week 2: Secondary Data and Excel

  • Lesson 2 Practical Exercise

Week 3: Probability Theory and Null Hypothesis Testing

  • Lesson 3 Quiz

Week 4: Descriptive Statistics

  • Lesson 4 Practical Exercise

Week 5: Data Visualization

  • Lesson 5 Practical Exercise

Week 6: Contingency Tables

  • Readings:
    • Fay, K. & Boyd, M. J. (2010). Eta Squared. In N. J. Salkind, Encyclopedia of Research Design. Sage.
  • Lesson 6 Practical Exercise

Week 7: Comparing Means

  • Lesson 7 Practical Exercise

Week 8: Correlation

  • Lesson 8 Practical Exercise

Week 9: Bivariate and Multivariate Linear Regression

  • Lesson 9 Practical Exercise

Week 10: Project Week

  • Submit final project worksheet

Week 11: Regression Assumptions, Part 1

  • Lesson 10 Practical Exercise

Week 12: Regression Assumptions, Part 2

  • Lesson 11 Practical Exercise

Week 13: Categorical Outcomes

  • Lesson 12 Practical Exercise