Quantifying the Qualitative
Information Theory for Comparative Case Analysis
Häftad, Engelska, 2016
1 489 kr
Produktinformation
- Utgivningsdatum2016-03-09
- Mått187 x 231 x 14 mm
- Vikt370 g
- FormatHäftad
- SpråkEngelska
- Antal sidor192
- Upplaga1
- FörlagSAGE Publications
- ISBN9781483392479
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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).Kurt Taylor Gaubatz, PhD, is an associate professor in the Graduate Program in International Studies at Old Dominion University. In addition to courses in international relations and international law, he regularly teaches research methods and advanced statistics. He has previously taught methodology and formal modeling as a faculty member at Stanford University and at Oxford University (Nuffield College), where he was the visiting John G. Winant Lecturer in American Foreign Policy. He has also served as the Susan Luise Dyer Peace Fellow at the Hoover Institution at Stanford University, and received a Pew Faculty Fellowship from the Kennedy School of Government at Harvard University. Professor Gaubatz’s most recent book is A Survivor’s Guide to R (SAGE 2015), which is a broad and cross-disciplinary introduction to the R language for statistical programming. He is also the author of Elections and War (Stanford University Press, 1999), which is a study of the electoral politics of military conflict. His work on international law and on the relationship between domestic politics and international relations has appeared in a number of leading journals. His work on political modeling has received funding from the US Department of Defense.Professor Gaubatz earned an AB in economics from U.C. Berkeley, an MALD in international law from the Fletcher School of Law and Diplomacy, an MDiv in theology from Princeton Theological Seminary, and a PhD in political science from Stanford University. More information can be found at kktg.net/kurt.
- CHAPTER 1: Enhancing Small-n Analysis: Information Theory and the Method of Structured-Focused ComparisonWhy Quantify the Qualitative? Enhancing Qualitative Analysis With Information TheoryWho Needs to Quantify the Qualitative?Information and Action Under UncertaintyOrigins and MotivationsFrom Cryptography and Communication to Comparative Case StudiesMaking Qualitative Analysis of Information Systematic: The Method of Structured-Focused ComparisonInformation Theory and Metrics for Qualitative LearningA Roadmap for Quantifying the QualitativeConclusionCHAPTER 2: The Information RevolutionInformation Theory for the Information AgeWhat’s Under the Hood: A Primer A Primer on Logarithms and Probability for Small-n AnalysisInformation Uncertainty MeasuresFundamental Contributions of Information TheoryThe Growing Use of Information MetricsA Note for Practitioners: From Analytics to ActionConclusionCHAPTER 3: Case SelectionResearch Design and Information TheoryCase Selection Strategies and ChallengesCoding CasesCase Selection and the Advantages of Information Theoretic AnalysisConclusionCHAPTER 4: The Information Method—If You Can Count, You Can Do ItQuantify: Setting up a Truth Table for Comparative Case AnalysisCount: Calculating the ProbabilitiesCompute: Computing the Uncertainty MeasuresCompare: Understanding the OutcomesConclusionCHAPTER 5: Information Metrics at Work—Three ExamplesExample 1—Ecology: Information Analysis for Tropical Forest LossExample 2—Education: Accounting for Teaching QualityExample 3— Medicine: Effective Nursing CareConclusionCHAPTER 6: Sensitivity Analysis—Entropy, Inference, and ErrorConfidence Intervals and the Information MetricAnalytic Leverage for a Study of Environmental IncentivesThe Information Metric and the Problem of InferenceSensitivity AnalysisDropped-Case AnalysisOutcome Coding SensitivityConclusionCHAPTER 7: The QCA ConnectionUnderstanding Qualitative Case Analysis (QCA)QCA and Causal ComplexityWhere QCA and Information Metrics DifferExamples of Enhancing QCA with Information MetricsConclusionSelected Introductory QCA ResourcesQCA Software and Web ResourcesCHAPTER 8: ConclusionInformation, Research, and the Digital EraReducing Uncertainty and Improving Judgment: Using Information Analysis in the Real WorldThe Limits and Further Possibilities for Information AnalysisExtensionsConclusionAPPENDIX A: Using Excel for Information MetricsStep One: Enter DataStep Two: Probability CalculationsStep Three: Entropy and Mutual Information MetricsAPPENDIX B: Using R for Information MetricsExample 1: Deriving Information Metrics from Conditional ProbabilitiesExample 2: Deriving Information Metrics with the abcd MethodReferencesIndex
"[Quantifying the Qualitative] gives students the tools they need to enhance systematic case-study analysis."