Quantifying the Qualitative by Katya Drozdova and Kurt Taylor Gaubatz presents a systematic approach to comparative case analysis based on insights from information theory. This new method, which requires minimal quantitative skills, helps students, policymakers, professionals, and scholars learn more from comparative cases. The approach avoids the limitations of traditional statistics in the small-n context and allows analysts to systematically assess and compare the impact of a set of factors on case outcomes with easy-to-use analytics. Rigorous tools reduce bias, improve the knowledge gained from case studies, and provide straightforward metrics for effectively communicating results to a range of readers and leaders.

CHAPTER 1: Enhancing Small-n Analysis: Information Theory and the Method of Structured-Focused Comparison Why Quantify the Qualitative? Enhancing Qualitative Analysis With Information Theory Who Needs to Quantify the Qualitative? Information and Action Under Uncertainty Origins and Motivations From Cryptography and Communication to Comparative Case Studies Making Qualitative Analysis of Information Systematic: The Method of Structured-Focused Comparison Information Theory and Metrics for Qualitative Learning A Roadmap for Quantifying the Qualitative ConclusionCHAPTER 2: The Information Revolution Information Theory for the Information Age What's Under the Hood: A Primer A Primer on Logarithms and Probability for Small-n Analysis Information Uncertainty Measures Fundamental Contributions of Information Theory The Growing Use of Information Metrics A Note for Practitioners: From Analytics to Action ConclusionCHAPTER 3: Case Selection Research Design and Information Theory Case Selection Strategies and Challenges Coding Cases Case Selection and the Advantages of Information Theoretic Analysis ConclusionCHAPTER 4: The Information Method-If You Can Count, You Can Do It Quantify: Setting up a Truth Table for Comparative Case Analysis Count: Calculating the Probabilities Compute: Computing the Uncertainty Measures Compare: Understanding the Outcomes ConclusionCHAPTER 5: Information Metrics at Work-Three Examples Example 1-Ecology: Information Analysis for Tropical Forest Loss Example 2-Education: Accounting for Teaching Quality Example 3- Medicine: Effective Nursing Care ConclusionCHAPTER 6: Sensitivity Analysis-Entropy, Inference, and Error Confidence Intervals and the Information Metric Analytic Leverage for a Study of Environmental Incentives The Information Metric and the Problem of Inference Sensitivity Analysis Dropped-Case Analysis Outcome Coding Sensitivity ConclusionCHAPTER 7: The QCA Connection Understanding Qualitative Case Analysis (QCA) QCA and Causal Complexity Where QCA and Information Metrics Differ Examples of Enhancing QCA with Information Metrics Conclusion Selected Introductory QCA Resources QCA Software and Web ResourcesCHAPTER 8: Conclusion Information, Research, and the Digital Era Reducing Uncertainty and Improving Judgment: Using Information Analysis in the Real World The Limits and Further Possibilities for Information Analysis Extensions ConclusionAPPENDIX A: Using Excel for Information Metrics Step One: Enter Data Step Two: Probability Calculations Step Three: Entropy and Mutual Information MetricsAPPENDIX B: Using R for Information Metrics Example 1: Deriving Information Metrics from Conditional Probabilities Example 2: Deriving Information Metrics with the abcd MethodReferencesIndex