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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Ekaterina "Katya" Drozdova, PhD, is an associate professor of Political Science in the School of Business, Government, and Economics at Seattle Pacific University. She has lectured extensively and taught courses on subjects ranging from Research Methods in Social Science to Global Security, Strategy, History, Information, and Political Economy as well as carried out a number of research projects in these areas which successfully utilized both qualitative and quantitative techniques. Professor Drozdova has earned a PhD and MPhil in Information Systems from New York University's (NYU) Stern School of Business, Department of Information, Operations, and Management Sciences; as well as an MA in International Policy studies and BA in International Relations from Stanford University. Her research interests broadly focus on understanding how systemic risks and technology choices help shape operational strategies-with emphasis on organizational threat prevention and response applications in diverse contexts: from countering terrorist networks to securing energy, cyber, and other critical infrastructures. Katya has been actively involved with leading military, policy, law enforcement, and business professionals in identifying mission-critical challenges and formulating effective global responses across multiple organization risk areas. Her recent work and publications have dealt with issues of U.S. national and international security - specifically addressing the problems of hybrid and asymmetric low-tech threats in the high-tech age - as well as with optimization of organizations' human and technological networks for improved success rate in complex and hostile environments. Prof. Drozdova is an affiliate with the Empirical Studies of Conflict Project (ESOC) at Stanford and Princeton Universities as well as a principal investigator for "Mining Afghan Lessons from Soviet Era" (MALSE) research program, which has been funded by the U.S. Office of the Secretary of Defense's (OSD) Human Social Cultural and Behavioral (HSCB) Sciences program through the Office of Naval Research's (ONR) Expeditionary Maneuver Warfare and Combating Terrorism Department and the Naval Postgraduate School. She has been a fellow at NYU's Alexander Hamilton Center for Political Economy and Stanford University's Hoover Institution on War, Revolution, and Peace as well as Stanford's Center for International Security and Cooperation (CISAC). At CISAC, Katya has also been a member of the Consortium for Research on Information Security and Policy funded by the U.S. National Security Agency (NSA) and comprising leading scholars as well as industry and government practitioners, including former directors of Lawrence Livermore National Laboratory (LLNL) and Defense Advanced Research Projects Agency (DARPA).
Inhaltsangabe
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 Conclusion CHAPTER 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 Conclusion CHAPTER 3: Case Selection Research Design and Information Theory Case Selection Strategies and Challenges Coding Cases Case Selection and the Advantages of Information Theoretic Analysis Conclusion CHAPTER 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 Conclusion CHAPTER 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 Conclusion CHAPTER 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 Conclusion CHAPTER 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 Resources CHAPTER 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 Conclusion APPENDIX A: Using Excel for Information Metrics Step One: Enter Data Step Two: Probability Calculations Step Three: Entropy and Mutual Information Metrics APPENDIX B: Using R for Information Metrics Example 1: Deriving Information Metrics from Conditional Probabilities Example 2: Deriving Information Metrics with the abcd Method References Index
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 Conclusion CHAPTER 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 Conclusion CHAPTER 3: Case Selection Research Design and Information Theory Case Selection Strategies and Challenges Coding Cases Case Selection and the Advantages of Information Theoretic Analysis Conclusion CHAPTER 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 Conclusion CHAPTER 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 Conclusion CHAPTER 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 Conclusion CHAPTER 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 Resources CHAPTER 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 Conclusion APPENDIX A: Using Excel for Information Metrics Step One: Enter Data Step Two: Probability Calculations Step Three: Entropy and Mutual Information Metrics APPENDIX B: Using R for Information Metrics Example 1: Deriving Information Metrics from Conditional Probabilities Example 2: Deriving Information Metrics with the abcd Method References Index
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