Katya Drozdova, Kurt Taylor Gaubatz

Quantifying the Qualitative

Information Theory for Comparative Case Analysis

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Sage Publications, Inc
• Seitenzahl: 192
• Erscheinungstermin: 12. Januar 2016
• Englisch
• Abmessung: 235mm x 191mm x 11mm
• Gewicht: 370g
• ISBN-13: 9781483392479
• ISBN-10: 1483392473
• Artikelnr.: 43115214

Autorenporträt

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