KEY FEATURES: Wide interdisciplinary applicability with step-by-step examples drawn from a number of sciences and professional fields, including defense, medicine, education, and ecology, demonstrate the powerful application of information metrics to comparative case studies. Presentation of techniques that can be used broadly allows readers to apply what they learn in settings including business, finance, health care, environmental policy, security, and other settings where consequential decisions are made under conditions of uncertainty and complexity. Clear and accessible prose illustrated by concrete and carefully explained examples makes the methods easy to understand and immediately applicable. A concise review of the exciting intellectual foundations of information theory motivates student interest by linking research with critical real-world problems, from World War II cryptography to Cold War nuclear deterrence to solving modern cyber-security and strategic challenges. Appendices available both in the book and online provide a walkthrough of Excel or Google sheets for automating simple calculations, along with sample Excel sheets (Appendix A) and an implementation of the methods in the open source language, R (Appendix B).

Chapter 7: The QCA Connection

The QCA Connection

The information methods we have demonstrated are very powerful on their own and have the important advantage of being simple and straightforward to conceptualize, to calculate, and to communicate. They can, however, also be integrated with other methods to gain further leverage. In particular, as discussed at the end of Chapter 6, it would be helpful to have an approach for understanding the structure of relationships among the independent variables, the causal factors, in addition to the enhanced measures of how informative each independent variable is about the dependent variable. In this chapter, we focus on qualitative case analysis (QCA), a popular and powerful approach to understanding and visualizing the patterns of co-occurrence among variables in a small-n study (Ragin, 1987).

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