The course introduces the foundational concepts and skills needed to understand, organize, analyze, visualize, and communicate quantitative information in public administration and policy contexts. The course begins with the role of quantitative evidence in public administration, including basic research design, measurement, and principles of good data collection. Students will then develop the statistical foundations needed to describe and explore quantitative data, including measures of central tendency and dispersion, data visualization, sampling, probability, and the logic of statistical inference. Throughout the semester, students will develop practical data-analysis skills using R. Beginning with the R environment and reproducible analysis using R Markdown, students will learn to create and work with variables and vectors, organize data in data frames, import external datasets, calculate descriptive statistics, and create effective data visualizations. The emphasis is not simply on producing statistical output, but on understanding what quantitative information means and communicating it appropriately in public administration and policy settings. Students will primarily learn foundational R skills using base R during the first semester. Selected Tidyverse tools may be introduced later in the semester, with more systematic use of the Tidyverse occurring in Advanced Quantitative Methods. This course provides the conceptual and computational foundation for PSM B1620: Advanced Quantitative Methods, where students will extend these skills to statistical
inference, hypothesis testing, group comparisons, correlation, and regression-based methods.