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.
1. Introduction
2. Assumptions and Steps of a QCA Research Process
3. Panel data Qualitative Comparative Analysis
4. Data Calibration
5. Applying the cluster() function to analyze panel data
6. Establish separate QCA models for different time points
7. Two-Step Panel Data QCA
8. Set-Theoretic Approach Towards Studying Change
9. Conclusion
Biography
Preya Bhattacharya is an Assistant Professor, at the Department of Political Science, Xavier University of Louisiana. Her articles have been published in the International Journal of Social Research Methods and Methodological Innovations. Along with panel data QCA, her research interests include microfinance, economic participation of women, and post-conflict economic development. She has taught courses on International Political Economy and Quantitative Methods, amongst others.
