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In the last few years, Qualitative Comparative Analysis (QCA) has become one of the most important research approaches in social science. This has encouraged researchers to apply QCA, to analyze cross-sectional and panel data, leading to the development of a variety of cross-sectional and panel data QCA models.
This book compares four different panel data QCA models: Cluster QCA, Multiple Sub-QCA, Remote-Proximate Panel, and Relevant Variation Panel. It starts by introducing QCA as a research approach, then discusses the assumptions, and steps in a QCA research process. It then applies these assumptions and steps to demonstrate each of the 4 afore-mentioned panel data QCA models. Each chapter also provides a step-by-step guide, that researchers can follow while building any of these 4 panel data QCA models. Finally, it compares the strengths and weaknesses of each of these models and suggests scenarios where researchers can apply them. This book is supplemented by materials like datasets and codes, available at the end of each chapter, and online on Harvard Dataverse. This book can be used as a textbook for introductory and advanced courses on panel data QCA.
List of contents
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
About the author
Preya Bhattacharya is an assistant professor at the Department of Political Science, Xavier University of Louisiana. Before joining Xavier University, she was a postdoctoral fellow at the Sam Nunn School of International Affairs, Georgia Institute of Technology. Her research and teaching interests are international relations, international political economy, and research methods.
Summary
This book discusses and compares four different approaches towards analyzing panel data in QCA. It introduces QCA as a research approach, then discusses the most important assumptions, and steps like set-calibration and theory-testing, and demonstrates each of the four panel data approaches.
