List of Figures
List of Tables
Preface to the Third Edition
Acknowledgments to the Third Edition
Acknowledgments to the Second Edition
Acknowledgments to the First Edition
1 The Scientific Study of Politics
Overview
1.1 Political
Science?
1.2 Approaching Politics Scientifically: the Search for Causal Explanations
1.3 Thinking about the World in Terms of Variables and Causal Explanations
1.4 Models of Politics
1.5 Rules of the Road to Scientific Knowledge about Politics
1.5.1 Focus on Causality
1.5.2 Don't Let Data Alone Drive Your Theories
1.5.3 Consider Only Empirical Evidence
1.5.4 Check Your Ideology at the Door and Avoid Normative Statements
1.5.5 Pursue Both Generality and Parsimony
1.6 A Quick Look Ahead
Concepts Introduced in This Chapter
Exercises
2 The Art of Theory Building
Overview
2.1 Good Theories Come from Good Theory-Building Strategies
2.2 Promising Theories Offer Answers to Interesting Research Questions
2.3 Identifying Interesting Variation
2.3.1 Cross-Sectional Examples
2.3.2 Time-Series Example
2.4 Learning to Use Your Knowledge
2.4.1 Moving from a Specific Event to More General Theories
2.4.2 Know Local, Think Global: Can You Drop the Proper Nouns?
2.5 Three Strategies toward Developing an Original Theory
2.5.1 Theory Type 1: a New Y (and Some X)
2.5.2 Project Type 2: an Existing Y and a New X
2.5.3 A New Z which Modifies an Established X → Y?
2.6 Using the Literature without Getting Buried in It
2.6.1 Identifying the Important Work on a Subject — Using Citation Counts
2.6.2 Oh No! Someone Else Has Already Done What I Was Planning to Do. What Do I Do Now?
2.6.3 Critically Examining Previous Research to Develop an Original Theory
2.7 Think Formally about the Causes that Lead to Variation in Your Dependent Variable
2.7.1 Utility and Expected Utility
2.7.2 The Puzzle of Turnout
2.8 Think about the Institutions: the Rules Usually Matter
2.8.1 Legislative Rules
2.8.2 The Rules Matter!
2.8.3 Extensions
2.9 Conclusion
Concepts Introduced in This Chapter
Exercises
3 Evaluating Causal Relationships
Overview
3.1 Causality and Everyday Language
3.2 Four Hurdles along the Route to Establishing Causal Relationships
3.2.1 Putting It All Together — Adding Up the Answers to Our Four Questions
3.2.2 Identifying Causal Claims Is an Essential Thinking Skill
3.2.3 What Are the Consequences of Failing to Control for Other Possible Causes?
3.3 Why Is Studying Causality So Important? Three Examples from Political Science
3.3.1 Life Satisfaction and Democratic Stability
3.3.2 Race and Political Participation in the United States
3.3.3 Evaluating Whether "Head Start" Is Effective
3.4 Wrapping Up
Concepts Introduced in This Chapter
Exercises
4 Research Design
Overview
4.1 Comparison as the Key to Establishing Causal Relationships
4.2 Experimental Research Designs
4.2.1 Experimental Designs and the Four Causal Hurdles
4.2.2 "Random Assignment" versus "Random Sampling"
4.2.3 Varieties of Experiments and Near-Experiments
4.2.4 Are There Drawbacks to Experimental Research Designs?
4.3 Observational Studies (in Two Flavors)
4.3.1 Datum, Data, Data Set
4.3.2 Cross-Sectional Observational Studies
4.3.3 Time-Series Observational Studies
4.3.4 The Major Difficulty with Observational Studies
4.4 Dissecting the Research by Other Scholars
Concepts Introduced in This Chapter
Exercises
5 Measuring Concepts of Interest
Overview
5.1 Getting to Know Your Data
5.2 Social Science Measurement: the Varying Challenges of Quantifying Human Behavior
5.3 Problems in Measuring Concepts of Interest
5.3.1 Conceptual Clarity
5.3.2 Reliability
5.3.3 Measurement Bias and Reliability
5.3.4 Validity
5.3.5 The Relationship between Validity and Reliability
5.4 Controversy 1: Measuring Democracy
5.5 Controversy 2: Measuring Political Tolerance
5.6 Are There Consequences to Poor Measurement?
5.7 Conclusions
Concepts Introduced in This Chapter
Exercises
6 Getting to Know Your Data
Overview
6.1 Getting to Know Your Data Statistically
6.2 What Is the Variable's Measurement Metric?
6.2.1 Categorical Variables
6.2.2 Ordinal Variables
6.2.3 Continuous Variables
6.2.4 Variable Types and Statistical Analyses
6.3 Describing Categorical Variables
6.4 Describing Continuous Variables
6.4.1 Rank Statistics
6.4.2 Moments
6.5 Limitations of Descriptive Statistics and Graphs
6.6 Conclusions
Concepts Introduced in This Chapter
Exercises
7 Probability and Statistical Inference
Overview
7.1 Populations and Samples
7.2 Some Basics of Probability Theory
7.3 Learning about the Population from a Sample: the Central Limit Theorem
7.3.1 The Normal Distribution
7.4 Example: Presidential Approval Ratings
7.4.1 What Kind of Sample Was That?
7.4.2 Obtaining a Random Sample in the Cellphone Eras
7.4.3 A Note on the Effects of Sample Size
7.5 A Look Ahead: Examining Relationships between Variables
Concepts Introduced in This Chapter
Exercises
8 Bivariate Hypothesis Testing
Overview
8.1 Bivariate Hypothesis Tests and Establishing Causal Relationships
8.2 Choosing the Right Bivariate Hypothesis Test
8.3 All Roads Lead to
p
8.3.1 The Logic of p-Values
8.3.2 The Limitations of p-Values
8.3.3 From p-Values to Statistical Significance
8.3.4 The Null Hypothesis and p-Values
8.4 Three Bivariate Hypothesis Tests
8.4.1 Example 1: Tabular Analysis
8.4.2 Example 2: Difference of Means
8.4.3 Example 3: Correlation Coefficient
8.5 Wrapping Up
Concepts Introduced in This Chapter
Exercises
9 Two-Variable Regression Models
Overview
9.1 Two–Variable Regression
9.2 Fitting a Line: Population ⇔ Sample
9.3 Which Line Fits Best? Estimating the Regression Line
9.4 Measuring Our Uncertainty about the OLS Regression Line
9.4.1 Goodness-of-Fit: Root Mean-Squared Error
9.4.2 Goodness-of-Fit: R-Squared Statistic
9.4.3 Is That a "Good" Goodness-of-Fit?
9.4.4 Uncertainty about Individual Components of the Sample Regression Model
9.4.5 Confidence Intervals about Parameter Estimates
9.4.6 Two-Tailed Hypothesis Tests
9.4.7 The Relationship between Confidence Intervals and Two-Tailed Hypothesis Tests
9.4.8 One-Tailed Hypothesis Tests
9.5 Assumptions, More Assumptions, and Minimal Mathematical Requirements
9.5.1 Assumptions about the Population Stochastic Component
9.5.2 Assumptions about Our Model Specification
9.5.3 Minimal Mathematical Requirements
9.5.4 How Can We Make All of These Assumptions?
Concepts Introduced in This Chapter
Exercises
10 Multiple Regression: the Basics
Overview
10.1 Modeling Multivariate Reality
10.2 The Population Regression Function
10.3 From Two-Variable to Multiple Regression
10.4 Interpreting Multiple Regression
10.5 Which Effect Is "Biggest"?
10.6 Statistical and Substantive Significance
10.7 What Happens when We Fail to Control for
Z?
10.7.1 An Additional Minimal Mathematical Requirement in Multiple Regression
10.8 An Example from the Literature: Competing Theories of How Politics Affects International Trade
10.9 Making Effective Use of Tables and Figures
10.9.1 Constructing Regression Tables
10.9.2 Writing about Regression Tables
10.10 Implications and Conclusions
Concepts Introduced in This Chapter
Exercises
11 Multiple Regression Model Specification
Overview
11.1 Extensions of Ordinary Least-Squares
11.2 Being Smart with Dummy Independent Variables in OLS
11.2.1 Using Dummy Variables to Test Hypotheses about a Categorical Independent Variable with Only Two Values
11.2.2 Using Dummy Variables to Test Hypotheses about a Categorical Independent Variable with More Than Two Values
11.2.3 Using Dummy Variables to Test Hypotheses about Multiple Independent Variables
11.3 Testing Interactive Hypotheses with Dummy Variables
11.4 Outliers and Influential Cases in OLS
11.4.1 Identifying Influential Cases
11.4.2 Dealing with Influential Cases
11.5 Multicollinearity
11.5.1 How Does Multicollinearity Happen?
11.5.2 Detecting Multicollinearity
11.5.3 Multicollinearity: a Simulated Example
11.5.4 Multicollinearity: a Real-World Example
11.5.5 Multicollinearity: What Should I Do?
11.6 Wrapping Up
Concepts Introduced in This Chapter
Exercises
12 Limited Dependent Variables and Time-Series Data
Overview
12.1 Extensions of Ordinary Least Squares
12.2 Dummy Dependent Variables
12.2.1 The Linear Probability Model
12.2.2 Binomial Logit and Binomial Probit
12.2.3 Goodness-of-Fit with Dummy Dependent Variables
12.3 Being Careful with Time Series
12.3.1 Time-Series Notation
12.3.2 Memory and Lags in Time-Series Analysis
12.3.3 Trends and the Spurious Regression Problem
12.3.4 The Differenced Dependent Variable
12.3.5 The Lagged Dependent Variable
12.4 Example: the Economy and Presidential Popularity
12.5 Wrapping Up
Concepts Introduced in This Chapter
Exercises
Appendix A. Critical Values of Chi-Squared
Appendix B. Critical Values of t
Appendix C. The Λ Link Function for Binomial Logit Models
Appendix D. The Φ Link Function for Binomial Probit Models
Bibliography
Index
