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
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
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
CHAPTER 3: Case Selection
Research Design and Information Theory
Case Selection Strategies and Challenges
Case Selection and the Advantages of Information Theoretic Analysis
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
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
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
Outcome Coding Sensitivity
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
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
APPENDIX A: Using Excel for Information Metrics
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