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• Tables, Figures, and Boxes
• Preface
• Acknowledgments
• About the Authors
• Introduction
• Facts and Values in Perspective
• The Scientific Approach
• What This Book Is About
• Conclusion
• Key Terms
• Notes
• Chapter 1 • The Definition and Measurement of Concepts
• Learning Objectives
• Conceptual Definitions
• Clarifying a Concept
• A Template for Writing a Conceptual Definition
• Why It’s Important to Identify the Unit of Analysis
• Operational Definitions
• Measurement Error
• Systematic Measurement Error
• Random Measurement Error
• Reliability and Validity
• Evaluating Reliability
• Evaluating Validity
• Working with Datasets, Codebooks, and Software
• Summary
• Key Terms
• Exercises
• Notes
• Chapter 2 • Measuring and Describing Variables
• Learning Objectives
• Essential Features
• Levels of Measurement
• Nominal-level Variables
• Ordinal-level Variables
• Interval-level Variables
• Which Level of Measurement Is Best?
• Describing Variables
• Nominal Variables
• Ordinal Variables
• Interval Variables
• Transforming Variables
• Additive Indexes
• Sometimes Less Is More: Simplifying Variables
• Centering and Standardizing Interval-level Variables
• Advanced Data Transformation Methods
• Summary
• Key Terms
• Exercises
• Notes
• Chapter 3 • Proposing Explanations, Framing Hypotheses, and Making Comparisons
• Learning Objectives
• “All Models Are Wrong, But Some Are Useful”
• Proposing Explanations
• Causal Mechanisms
• Framing Hypotheses
• Common Mistakes in Hypothesis Writing
• Making Comparisons
• Cross-tabulations
• Mean Comparisons
• Graphing Relationships and Describing Patterns
• Summary
• Key Terms
• Exercises
• Notes
• Chapter 4 • Research Design, Research Ethics, and Evidence of Causation
• Learning Objectives
• Establishing Causation
• Experimental Designs
• Random Assignment
• Pretreatment Measurements
• Laboratory Experiments
• Field Experiments
• Selecting Cases for Analysis
• Random Sampling
• Weighting Sample Observations
• Nonrandom Sampling and Qualitative Research Designs
• Conducting Research Ethically
• Experiments Involving Human Subjects
• Ethical Responsibilities to the Academic Community
• Summary
• Key Terms
• Exercises
• Notes
• Chapter 5 • Making Controlled Comparisons
• Learning Objectives
• The Logic of Controlled Comparisons
• Essential Terms and Concepts
• Effect of Partisanship on Gun Control Vote, Controlling for Gender: An Illustrative Example
• A Spurious Relationship
• Additive Relationships
• Interactive Relationships
• Many Faces of Interaction
• Controlled Mean Comparisons
• Example of an Additive Relationship
• Example of an Interactive Relationship
• Identifying Patterns
• Advanced Methods of Making Controlled Comparisons
• Summary
• Key Terms
• Exercises
• Notes
• Chapter 6 • Foundations of Statistical Inference
• Learning Objectives
• Population Parameters and Sample Statistics
• The Central Limit Theorem and the Normal Distribution
• Quantifying Standard Errors
• Standard Error of a Sample Mean
• Standard Error of a Sample Proportion
• Confidence Intervals
• Sample Size and the Margin of Error of a Poll
• Inferences with Small Batches: The Student’s t-Distribution
• Summary
• Key Terms
• Exercises
• Notes
• Chapter 7 • Tests of Significance and Measures of Association
• Learning Objectives
• Statistical Significance and Null Hypothesis Testing
• One-Sample Significance Tests
• Two-Sample Significance Tests
• Difference of Means Test
• Difference of Proportions Test
• The Chi-square Test
• Measures of Association
• Lambda
• Somers’ dyx
• Cramer’s V
• Criticisms of Null Hypothesis Testing
• Summary
• Key Terms
• Exercises
• Notes
• Chapter 8 • Correlation and Linear Regression
• Learning Objectives
• Correlation
• Bivariate Regression
• Educational Attainment and Voter Turnout in States Example
• R-square
• Adjusted R-square
• Multiple Regression
• Regression with Multiple Dummy Variables
• Interaction Effects in Multiple Regression
• Some Practical Issues: Multicollinearity, Parsimony, and Missing Data
• Analyzing Residuals to Evaluate Linear Regression Models
• Summary
• Key Terms
• Exercises
• Notes
• Chapter 9 • Logistic Regression
• Learning Objectives
• The Logistic Regression Approach
• Logistic Regression Approach to Vote Choice in the 2016 Presidential Election
• Finding the Best Fit: Maximum Likelihood Estimation
• Logistic Regression with Multiple Independent Variables
• Graphing Predicted Probabilities with Multiple Independent Variables
• Marginal Effects at the Means
• Marginal Effects at Representative Values
• Summary
• Key Terms
• Exercises
• Notes
• Chapter 10 • Conducting Your Own Political Analysis
• Picking a Good Topic
• Getting Focused and Staying Motivated
• Reviewing Prior Literature
• Collecting Data
• Political Science Data Archives
• Making Observations
• Automated Data Collection
• Survey Research
• Interviews
• Writing It Up
• General Suggestions about Tone and Citations
• Suggestions for Clear Organization
• Maintain a Scientific Mindset
• Key Terms
• Exercises
• Notes
• Index