This book discusses and compares four different approaches towards analyzing panel data in QCA. It starts by introducing QCA as a research approach, then discusses the most important assumptions, and steps like set-calibration and theory-testing, and finally demonstrates each of the four panel data approaches, in separate chapters. Each chapter provides a step-by-step guide, including codes, sample data, and analysis results, that researchers can follow while building a panel data QCA model. Finally, it compares the strengths and weaknesses of each of these models and suggests scenarios where researchers can apply it. This book is supplemented by online RStudio materials, like datasets, codes, and Markdown documents for each chapter, and can be used as a textbook for introductory and advanced courses on panel-data QCA.
Key features:
Describes what panel data QCA is, and its main assumptions.
Demonstrates the three approaches and main steps of building a panel data QCA model.
Includes examples with data and R code to describe each of these steps.
