Texts in Quantitative Political Analysis

This volume provides a methodological toolbox for conducting policy research. Recognizing that policy research spans various academic disciplines, each of which takes a different view on causality, the volume introduces a methodologically pluralistic approach to policy studies. Each chapter clarifies the research question that each technique can answer, the research design and data treatment that each technique requires for its results to be sound, the validity domain of its results, and the actual deployment of the technique through a replicable example. Techniques covered include quasi-experimental designs, approaches to account for selection bias and observed imbalances, directed acyclic graphs and structural equation models, Qualitative Comparative Analysis, Bayesian case study and process tracing, and Agent-Based Modelling. By working through the volume, readers will understand how to learn from different techniques, apply them consciously, and triangulate them to make better sense of findings. This volume is intended for advanced academic courses, as well as scholars and practitioners in policy-related fields, such as political science, economics, sociology, and public administration.

This is an open access book.

This text teaches basic R skills to political science students with no programming background. Intended specifically for the students who need to learn R for a class and who have no interest in R or may even be afraid of or hostile to it, this text builds an awareness of basics, confidence, and a skill set necessary to transition into more advanced texts.

To that end, in addition to standard topics, this book includes three chapters specific to the new or reluctant learner. The Introduction explicitly sets expectations for how to use the book and discusses fixed and growth mentalities, and why a growth mentality is crucial for learning R. Chapter 1 includes some basic information on programming, R, and their place in political science research. Chapter 2 explicitly discusses errors, warnings, and methods of debugging. Further chapters build on this by including new errors or warnings that students may encounter as they progress.

In service of the aim to give students a solid foundation in R and awareness of what it is and can do, this book teaches and uses both tidyverse and base R frameworks throughout. After completing the book, students should be prepared to learn more advanced materials.