Quantifying the Qualitative
Information Theory for Comparative Case Analysis
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Go to College Publishing WebsiteDescription
Quantifying the Qualitative 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.
Contents
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
References
Index
Index
Description
Quantifying the Qualitative 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.
Contents
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
References
Index
Index
Reviews
Quantifying the Qualitative
Information Theory for Comparative Case Analysis
January 2016 | 192 pages | Sage US
| Format | Published Date | ISBN | Price |
|---|
Quantifying the Qualitative 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.
Table Of Contents:
- 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
Recent Product Reviews:
“[Quantifying the Qualitative] gives students the tools they need to enhance systematic case-study analysis.”
Laura Roselle, Elon University and Duke University
“[This text] is a new and fresh approach to learning how to analyze case studies from a qualitative research paradigm that faculty can use and students can wrap their heads around.”
Shon D. Smith, University of Florida
“[This book] just oozes with policy recommendations and future research...a huge contribution.”
Mark Meo, University of Oklahoma
“[This text introduces] more contemporary tools to address questions that are important to the world now.”
Juanita A. Johnson, Union Institute & University